From 849311290578d92816333f7e987e03d009093711 Mon Sep 17 00:00:00 2001
From: PrimozGodec <p.godec9@gmail.com>
Date: Wed, 10 Jan 2024 16:10:32 +0000
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>documentation, education, features, interface, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">10 Tips and Tricks for Using Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 17, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>TIP #1: <strong>Follow tutorials and example workflows to get started.</strong></p>
 <p>It&#x27;s difficult to start using new software. Where does one start, especially a total novice in data mining? For this exact reason we&#x27;ve prepared <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">Getting Started With Orange - YouTube tutorials</a> for complete beginners. Example workflows on the other hand can be accessed via Help - Examples.</p>
 <p>TIP #2: <strong>Make use of Orange documentation.</strong></p>
@@ -179,4 +179,4 @@
 <p>Orange is geared to remember your last settings, thus assisting you in a rapid analysis. However, sometimes you need to start anew. Go to Options - Reset widget settings... and restart Orange. This will return Orange to its original state.</p>
 <p>TIP #10: <strong>Use Educational add-on.</strong></p>
 <p>To learn about how some algorithms work, use <a href="https://github.com/biolab/orange3-educational">Orange3-Educational</a> add-on. It contains 4 widgets that will help you get behind the scenes of some famous algorithms. And since they&#x27;re interactive, they&#x27;re also a lot of fun!</p>
-<a href="/blog/2016-10-tips-for-using-orange/__optimized-images__/educational.png" data-gallery="true"><img alt="" loading="lazy" width="1238" height="686" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-10-tips-for-using-orange/__optimized-images__/educational.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-10-17 12:18:23+00:00","draft":false,"title":"10 Tips and Tricks for Using Orange","blog":["documentation","education","features","interface","orange3"],"oldUrl":"/blog/2016/10/17/tips-for-using-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    ol: \"ol\",\n    li: \"li\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"TIP #1: \", _jsx(_components.strong, {\n        children: \"Follow tutorials and example workflows to get started.\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It's difficult to start using new software. Where does one start, especially a total novice in data mining? For this exact reason we've prepared \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"Getting Started With Orange - YouTube tutorials\"\n      }), \" for complete beginners. Example workflows on the other hand can be accessed via Help - Examples.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #2: \", _jsx(_components.strong, {\n        children: \"Make use of Orange documentation.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can access it in three ways:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Press F1 when the widget is selected. This will open help screen.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select Widget - Help when the widget is selected. It works the same as above.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Visit online \", _jsx(_components.a, {\n          href: \"http://orange.biolab.si/toolbox/\",\n          children: \"documentation\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"TIP #3:** Embed your help screen.**\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Drag and drop help screen to the side of your Orange canvas. It will become embedded in the canvas. You can also make it narrower, allowing for a full-size analysis while exploring the docs.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-10-tips-for-using-orange/__optimized-images__/Embed-Help.png\",\n      alt: \"\",\n      width: \"1033\",\n      height: \"727\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #4: \", _jsx(_components.strong, {\n        children: \"Use right-click.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Right-click on the canvas and a widget menu will appear. Start typing the widget you're looking for and press Enter when the widget becomes the top widget. This will place the widget onto the canvas immediately. You can also navigate the menu with up and down.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #5: \", _jsx(_components.strong, {\n        children: \"Turn off channel names.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sometimes it is annoying to see channel names above widget links. If you're already comfortable using Orange, you can turn them off in Options - Settings. Turn off 'Show channel names between widgets'.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #6: \", _jsx(_components.strong, {\n        children: \"Hide control pane.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Once you've set the parameters, you'd probably want to focus just on visualizations. There's a simple way to do this in Orange. Click on the split between the control pane and visualization pane - you should see a hand appearing instead of a cursor. Click and observe how the control pane gets hidden away neatly. To make it reappear, click the split again.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-10-tips-for-using-orange/__optimized-images__/Panel1.jpg\",\n      alt: \"\",\n      width: \"757\",\n      height: \"496\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-10-tips-for-using-orange/__optimized-images__/Panel2.jpg\",\n      alt: \"\",\n      width: \"618\",\n      height: \"496\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #7: \", _jsx(_components.strong, {\n        children: \"Label your data.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So you've plotted your data, but have no idea what you're seeing. Use annotation! In some widgets you will see a drop-down menu called Annotation, while in others it will be called a Label. This will mark your data points with suitable labels, making your MDS plots and Scatter Plots much more informative. Scatter Plot also enables you to label only selected points for better clarity.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #8: \", _jsx(_components.strong, {\n        children: \"Find your plot.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scrolled around and lost the plot? Zoomed in too much? To re-position the plot click 'Reset zoom' and the visualization will jump snugly into the visualization pane. Comes in handy when browsing the subsets and trying to see the bigger picture every now and then.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-10-tips-for-using-orange/__optimized-images__/zoom-pan-1.png\",\n      alt: \"\",\n      width: \"719\",\n      height: \"676\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #9: \", _jsx(_components.strong, {\n        children: \"Reset widget settings.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is geared to remember your last settings, thus assisting you in a rapid analysis. However, sometimes you need to start anew. Go to Options - Reset widget settings... and restart Orange. This will return Orange to its original state.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #10: \", _jsx(_components.strong, {\n        children: \"Use Educational add-on.\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To learn about how some algorithms work, use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-educational\",\n        children: \"Orange3-Educational\"\n      }), \" add-on. It contains 4 widgets that will help you get behind the scenes of some famous algorithms. 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\ No newline at end of file
+<a href="/blog/2016-10-tips-for-using-orange/__optimized-images__/educational.png" data-gallery="true"><img alt="" loading="lazy" width="1238" height="686" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-10-tips-for-using-orange/__optimized-images__/educational.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-10-17 12:18:23+00:00","draft":false,"title":"10 Tips and Tricks for Using Orange","blog":["documentation","education","features","interface","orange3"],"oldUrl":"/blog/2016/10/17/tips-for-using-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    ol: \"ol\",\n    li: \"li\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"TIP #1: \", _jsx(_components.strong, {\n        children: \"Follow tutorials and example workflows to get started.\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It's difficult to start using new software. Where does one start, especially a total novice in data mining? For this exact reason we've prepared \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"Getting Started With Orange - YouTube tutorials\"\n      }), \" for complete beginners. Example workflows on the other hand can be accessed via Help - Examples.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #2: \", _jsx(_components.strong, {\n        children: \"Make use of Orange documentation.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can access it in three ways:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Press F1 when the widget is selected. This will open help screen.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select Widget - Help when the widget is selected. It works the same as above.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Visit online \", _jsx(_components.a, {\n          href: \"http://orange.biolab.si/toolbox/\",\n          children: \"documentation\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"TIP #3:** Embed your help screen.**\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Drag and drop help screen to the side of your Orange canvas. It will become embedded in the canvas. You can also make it narrower, allowing for a full-size analysis while exploring the docs.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-10-tips-for-using-orange/__optimized-images__/Embed-Help.png\",\n      alt: \"\",\n      width: \"1033\",\n      height: \"727\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #4: \", _jsx(_components.strong, {\n        children: \"Use right-click.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Right-click on the canvas and a widget menu will appear. Start typing the widget you're looking for and press Enter when the widget becomes the top widget. This will place the widget onto the canvas immediately. You can also navigate the menu with up and down.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #5: \", _jsx(_components.strong, {\n        children: \"Turn off channel names.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sometimes it is annoying to see channel names above widget links. If you're already comfortable using Orange, you can turn them off in Options - Settings. Turn off 'Show channel names between widgets'.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #6: \", _jsx(_components.strong, {\n        children: \"Hide control pane.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Once you've set the parameters, you'd probably want to focus just on visualizations. There's a simple way to do this in Orange. Click on the split between the control pane and visualization pane - you should see a hand appearing instead of a cursor. Click and observe how the control pane gets hidden away neatly. 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Scatter Plot also enables you to label only selected points for better clarity.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"TIP #8: \", _jsx(_components.strong, {\n        children: \"Find your plot.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scrolled around and lost the plot? Zoomed in too much? To re-position the plot click 'Reset zoom' and the visualization will jump snugly into the visualization pane. 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diff --git a/blog/2020-year-in-code/index.html b/blog/2020-year-in-code/index.html
index f1149e240..cd0737de7 100644
--- a/blog/2020-year-in-code/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - 2020 - Year in Code</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - 2020 - Year in Code"/><meta property="og:description" content="Statistics of Orange development in 2020."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2020-12-21"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="2020"/><meta property="article:tag" content="code"/><meta property="article:tag" content="overview"/><meta property="article:tag" content="Github"/><meta property="og:image" content="/blog/2020-12-year-in-code/__optimized-images__/2020-12-21-github-stats.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>2020, code, overview, Github</strong></p><h1 class="sc-c67a4900-0 loxVcf">2020 - Year in Code</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 21, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>2020 - Year in Code</h1>
 <p>2020 is coming a to close. This year had its share of challenges, but we are among the lucky ones being able to work from home. Of course, some of us had to manage being a parent and a developer at the same time, but for the most part, we were successful. We&#x27;ve managed to write a couple of new widgets, solve issues, implement enhancement, wrote documentation, and tried to keep the Orange community alive and kicking.</p>
 <p>Here&#x27;s Orange&#x27;s year in code (and other stats).</p>
@@ -161,4 +161,4 @@
 <p>We&#x27;ve had 651,479 views of <a href="https://www.youtube.com/c/OrangeDataMining">YouTube videos</a> and 6,700 new subscribers, which made us really happy! We produced 7 new videos, 3 about analyzing COVID-19 data and 4 about text mining. We&#x27;ve not been so good in terms of <a href="https://orange.biolab.si/blog/">blog</a> writing - we&#x27;ve only published 12 blogs. New Year&#x27;s resolution - write more blogs.</p>
 <a href="/blog/2020-12-year-in-code/__optimized-images__/2020-12-21-contributors.png" data-gallery="true"><img alt="" loading="lazy" width="407" height="406" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-12-year-in-code/__optimized-images__/2020-12-21-contributors.png"/></a>
 <p>Despite the pandemic, we&#x27;ve had three workshops, one summer school, and one online course. We absolutely hate holding lectures online, because we love to interact with our students and see in person how they use Orange. On the other hand, we&#x27;ve joined <a href="https://discord.gg/FWrfeXV">Discord</a>, where users can ask and answer questions and we&#x27;ve trying <a href="https://github.com/biolab/orange3/discussions">Github Discussions</a> as well. Our main goal for 2021 is to create an online community of Orange novices and experts, where the users will be able to exchange information, ideas, projects, data, code, and experiences (or just chat).</p>
-<p>We wish you all a lovely holiday season and may 2021 treat us better. To a fruitful and fun new year!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-12-21","draft":false,"title":"2020 - Year in Code","thumbImage":"2020-12-21-github-stats.png","frontPageImage":"2020-12-21-github-stats.png","blog":["2020","code","overview","Github"],"shortExcerpt":"Statistics of Orange development in 2020.","longExcerpt":"Statistical report on Orange software development and educational content for 2020.","x2images":true,"oldUrl":"/blog/2020/2020-12-21-year-in-code/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"2020 - Year in Code\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"2020 is coming a to close. 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+<p>We wish you all a lovely holiday season and may 2021 treat us better. To a fruitful and fun new year!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-12-21","draft":false,"title":"2020 - Year in Code","thumbImage":"2020-12-21-github-stats.png","frontPageImage":"2020-12-21-github-stats.png","blog":["2020","code","overview","Github"],"shortExcerpt":"Statistics of Orange development in 2020.","longExcerpt":"Statistical report on Orange software development and educational content for 2020.","x2images":true,"oldUrl":"/blog/2020/2020-12-21-year-in-code/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"2020 - Year in Code\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"2020 is coming a to close. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>sql</strong></p><h1 class="sc-c67a4900-0 loxVcf">2UDA</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>LAN</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 04, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In one of the <a href="/blog/2015/10/19/sql-for-orange/">previous blog posts</a> we mentioned that installing the optional dependency <em>psycopg2</em> allows Orange to connect to <a href="http://www.postgresql.org/">PostgreSQL</a> databases and work directly on the data stored there.
 It is also possible to transfer a whole table to the client machine, keep it in the local memory, and continue working with it as with any other Orange data set loaded from a file. But the true power of this feature lies in the ability of Orange to leave the bulk of the data on the server, delegate some of the computations to the database, and transfer only the needed results. This helps especially when the connection is too slow to transfer all the data and when the data is too big to fit in the memory of the local machine, since SQL databases are much better equipped to work with large quantities of data residing on the disk.</p>
 <p>If you want to test this feature it is now even easier to do so! A third party distribution called <a href="http://2ndquadrant.com/2uda"><strong>2UDA</strong></a> provides a single installer for all major OS platforms that combines Orange and a PostgreSQL 9.5 server along with LibreOffice (optional) and installs all the needed dependencies. The database even comes with some sample data sets that can be used to start testing and using Orange out of the box. 2UDA is also a great way to get the very latest version of PostgreSQL, which is important for Orange as it relies heavily on its new <a href="http://blog.2ndquadrant.com/tablesample-in-postgresql-9-5-2/">TABLESAMPLE</a> clause. It enables time-based sampling of tables, which is used in Orange to get approximate results quickly and allow responsive and interactive work with big data.</p>
 <p>We hope this will help us reach an even wider audience and introduce Orange to a whole new group of people managing and storing their data in SQL databases. We believe that having lots of data is a great starting point, but the benefits truly kick in with the ability to easily extract useful information from it.</p>
-<a href="/blog/2015-12-2uda/__optimized-images__/2uda-final-logo_thumbnail.png" data-gallery="true"><img alt="" loading="lazy" width="150" height="102" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-12-2uda/__optimized-images__/2uda-final-logo_thumbnail.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"LAN","date":"2015-12-04 07:20:12+00:00","draft":false,"title":"2UDA","blog":["sql"],"oldUrl":"/blog/2015/12/04/2uda/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In one of the \", _jsx(_components.a, {\n        href: \"/blog/2015/10/19/sql-for-orange/\",\n        children: \"previous blog posts\"\n      }), \" we mentioned that installing the optional dependency \", _jsx(_components.em, {\n        children: \"psycopg2\"\n      }), \" allows Orange to connect to \", _jsx(_components.a, {\n        href: \"http://www.postgresql.org/\",\n        children: \"PostgreSQL\"\n      }), \" databases and work directly on the data stored there.\\nIt is also possible to transfer a whole table to the client machine, keep it in the local memory, and continue working with it as with any other Orange data set loaded from a file. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>opengl, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">3D Visualizations in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 07, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Over the summer I worked (and still do) on several new 3D visualization widgets as well as a 3D plotting library they use, which will hopefully simplify making more widgets. The library is designed to be similar in terms of API to the new <a href="/blog/2011/09/03/gsoc-review-visualizations-with-qt/">Qt plotting library</a> Noughmad is working on.</p>
 <p>The library uses OpenGL 2/3: since Khronos deprecated parts of the old OpenGL API (particularly immediate mode and fixed-function functionality) care has been taken to use only capabilities less likely to go away in the years to come. All the drawing is done using shaders; geometry data is fed to the graphics hardware using Vertex Buffers. The library is fully functional under OpenGL 2.0; when hardware supports newer versions (3+), several optimizations are possible (e.g. geometry processing is done on the GPU rather than on CPU), possibly resulting in improved user experience.</p>
 <p>Widgets I worked on and are reasonably usable:</p>
@@ -164,4 +164,4 @@ <h4>Sphereviz3D</h4>
 <p>Sphereviz3D has 2D symbols option enabled (also available in all 3D widgets). VizRank has been modified to work with three dimensions; PCA and SPCA options under FreeViz return first three most important components when used in these widgets.</p>
 <h3>Future</h3>
 <p>Documentation for widgets and the library is still missing. Some additional widgets are being considered, such as NetExplorer3D.</p>
-<p>I wrote few technical details <a href="http://matejd.github.com/">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-07 10:30:00+00:00","draft":false,"title":"3D Visualizations in Orange","blog":["opengl","visualization"],"oldUrl":"/blog/2011/09/07/3d-visualizations-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h4: \"h4\",\n    img: \"img\",\n    h3: \"h3\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Over the summer I worked (and still do) on several new 3D visualization widgets as well as a 3D plotting library they use, which will hopefully simplify making more widgets. 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+<p>I wrote few technical details <a href="http://matejd.github.com/">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-07 10:30:00+00:00","draft":false,"title":"3D Visualizations in Orange","blog":["opengl","visualization"],"oldUrl":"/blog/2011/09/07/3d-visualizations-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h4: \"h4\",\n    img: \"img\",\n    h3: \"h3\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Over the summer I worked (and still do) on several new 3D visualization widgets as well as a 3D plotting library they use, which will hopefully simplify making more widgets. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dask, development</strong></p><h1 class="sc-c67a4900-0 loxVcf">A dash of Dask</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Noah Novšak</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 31, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It&#x27;s been a while since our last progress update on <a href="/blog/2022-12-13-dask-table">Dask</a>, and quite a bit has happened since. Most noticeably, we&#x27;ve got some more visualization widgets working, but I leave that for another blog post. Today, I want to focus on our updated machine-learning methods. K-Means, PCA, Naive Bayes, Linear, and Logistic Regression are now running. Well, maybe not flawlessly, but hey, they work.</p>
 <p>Currently, most of Orange&#x27;s learners wrap existing scikit-learn methods. Extending them to support Dask was as simple as making a helper function that switches between the <code>sklearn</code> and <code>dask_ml</code> implementations depending on the data received (with a couple of caveats).</p>
 <p>Ideally, this kind of data handling should be separate from the canvas and widgets. Both to facilitate development and to not obtrude the end user. Also, we need the learners to maintain backward compatibility because Orange is quite a large project. These requirements mean some <em>magic</em> has to be done behind the scenes to account for the subtle differences between scikit-learn and dask-ml. So, to anyone trying to use one of the learners mentioned above in unpredicted ways, you have been warned.</p>
@@ -169,4 +169,4 @@
 att = [a <span class="hljs-keyword">for</span> a, n <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(data.domain.attributes, nans) <span class="hljs-keyword">if</span> n &lt; threshold]
 </code></pre>
 <p>In theory, option one should have a more minor memory impact, and you may prefer a version of it under certain circumstances. However, accessing data from the hard drive is slow, even more so when only reading small pieces at a time. Option two has an advantage because it can access all the data it requires more smartly while using vectorization. The only downside is that we must store a temporary array in memory. Regardless, this is the approach we&#x27;ve opted to use going forward, prioritizing computation speed under the assumption that these temporary arrays are manageable. To illustrate the performance difference I ran this code on a 1000x1000 table. The calculation went from taking three seconds to four milliseconds. That&#x27;s almost a thousand times faster.</p>
-<p>This decision is a good illustration of what we aim to achieve with Dask and how. While you may consider committing an intermediate array to memory unappealing and contradictory to using dask to help orange scale, you should also look at the bigger picture. The main goal of this project is not to handle <em>big data</em> but <em>bigger</em> data. Essentially, we would like to transition from assuming all the data fits comfortably into memory to storing just a single column or row at a time. Going beyond that would require fundamental architectural changes and significant hardware upgrades to your laptop.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2023-08-31","draft":false,"title":"A dash of Dask","thumbImage":"2023-08-31-dask-logistic-regression.png","frontPageImage":"2023-08-31-dask-logistic-regression.png","blog":["dask","development"],"shortExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.","longExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.","oldUrl":"/blog/2023/2023-08-31-a-dash-of-dask/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    em: \"em\",\n    pre: \"pre\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"It's been a while since our last progress update on \", _jsx(_components.a, {\n        href: \"/blog/2022-12-13-dask-table\",\n        children: \"Dask\"\n      }), \", and quite a bit has happened since. 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+<p>This decision is a good illustration of what we aim to achieve with Dask and how. While you may consider committing an intermediate array to memory unappealing and contradictory to using dask to help orange scale, you should also look at the bigger picture. The main goal of this project is not to handle <em>big data</em> but <em>bigger</em> data. Essentially, we would like to transition from assuming all the data fits comfortably into memory to storing just a single column or row at a time. Going beyond that would require fundamental architectural changes and significant hardware upgrades to your laptop.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2023-08-31","draft":false,"title":"A dash of Dask","thumbImage":"2023-08-31-dask-logistic-regression.png","frontPageImage":"2023-08-31-dask-logistic-regression.png","blog":["dask","development"],"shortExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.","longExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.","oldUrl":"/blog/2023/2023-08-31-a-dash-of-dask/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    em: \"em\",\n    pre: \"pre\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"It's been a while since our last progress update on \", _jsx(_components.a, {\n        href: \"/blog/2022-12-13-dask-table\",\n        children: \"Dask\"\n      }), \", and quite a bit has happened since. 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The preprocessor in question is called \", _jsx(_components.code, {\n        children: \"RemoveNaNColumns\"\n      }), \", and its job is to eliminate columns with too many missing values like so:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"nans = np.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"sum\"\n        }), \"(np.isnan(data.X), axis=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \")\\natt = [a \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" a, n \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"zip\"\n        }), \"(data.domain.attributes, nans) \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" n \u003c threshold]\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This specific bit of code has yet to be touched up to work with Dask, but interestingly, it still works. 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Here's an example of how each of these approaches might look.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# option 1\"\n        }), \"\\natt = [a \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i, a \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"enumerate\"\n        }), \"(data.domain.attributes)\\n       \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" np.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"sum\"\n        }), \"(np.isnan(data.X[:, i])) \u003c threshold]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# option 2\"\n        }), \"\\nnans = np.asarray(np.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"sum\"\n        }), \"(np.isnan(data.X), axis=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"))\\natt = [a \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" a, n \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"zip\"\n        }), \"(data.domain.attributes, nans) \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" n \u003c threshold]\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In theory, option one should have a more minor memory impact, and you may prefer a version of it under certain circumstances. However, accessing data from the hard drive is slow, even more so when only reading small pieces at a time. Option two has an advantage because it can access all the data it requires more smartly while using vectorization. The only downside is that we must store a temporary array in memory. Regardless, this is the approach we've opted to use going forward, prioritizing computation speed under the assumption that these temporary arrays are manageable. To illustrate the performance difference I ran this code on a 1000x1000 table. The calculation went from taking three seconds to four milliseconds. That's almost a thousand times faster.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This decision is a good illustration of what we aim to achieve with Dask and how. While you may consider committing an intermediate array to memory unappealing and contradictory to using dask to help orange scale, you should also look at the bigger picture. The main goal of this project is not to handle \", _jsx(_components.em, {\n        children: \"big data\"\n      }), \" but \", _jsx(_components.em, {\n        children: \"bigger\"\n      }), \" data. Essentially, we would like to transition from assuming all the data fits comfortably into memory to storing just a single column or row at a time. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, examples, overfitting, regression, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">A visit from the Tilburg University</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 02, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Biolab is currently hosting two amazing data scientists from the Tilburg University - dr. Marie Nilsen and dr. Eric Postma, who are preparing a 20-lecture MOOC on data science for non-technical audience. A part of the course will use Orange. The majority of their students is coming from humanities, law, economy and behavioral studies, thus we are discussing options and opportunities for adapting Orange for social scientists. Another great thing is that the course is designed for beginner level data miners, showcasing that anybody can mine the data and learn from it. And then consult with statisticians and data mining expert (of course!).</p>
 <a href="/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/IMG_26951.jpg" data-gallery="true"><img alt="" loading="lazy" width="1208" height="906" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/IMG_26951.jpg"/></a>
 <p>Biolab team with Marie and Eric, who is standing next to Ivan Cankar - the very serious guy in the middle.</p>
@@ -165,4 +165,4 @@
 <p>But hold on! The curve now becomes very steep. Would the lower end of the curve at about (0.9, -2.2) still be a realistic estimate of our data set? Probably not. Even when we look at the Data Table with coefficient values, they seem to skyrocket.</p>
 <a href="/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/blog6.png" data-gallery="true"><img alt="" loading="lazy" width="481" height="423" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/blog6.png"/></a>
 <p>This is a typical danger of overfitting, which is often hard to explain, but with the help of these three widgets becomes as clear as day!
-Now go out and share the knowledge!!!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-10-02 12:52:27+00:00","draft":false,"title":"A visit from the Tilburg University","blog":["education","examples","overfitting","regression","visualization"],"oldUrl":"/blog/2015/10/02/a-visit-from-the-tilburg-university/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Biolab is currently hosting two amazing data scientists from the Tilburg University - dr. Marie Nilsen and dr. Eric Postma, who are preparing a 20-lecture MOOC on data science for non-technical audience. A part of the course will use Orange. The majority of their students is coming from humanities, law, economy and behavioral studies, thus we are discussing options and opportunities for adapting Orange for social scientists. Another great thing is that the course is designed for beginner level data miners, showcasing that anybody can mine the data and learn from it. And then consult with statisticians and data mining expert (of course!).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/IMG_26951.jpg\",\n      alt: \"\",\n      width: \"1208\",\n      height: \"906\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Biolab team with Marie and Eric, who is standing next to Ivan Cankar - the very serious guy in the middle.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To honor this occasion we invite you to check out the Polynomial regression widget, which is specially intended for educational use. There, you can showcase the problem of overfitting through visualization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, we set up a workflow.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/blog7.jpg\",\n      alt: \"\",\n      width: \"310\",\n      height: \"119\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then we paint, say, at most 10 points into the Paint Data widget. (Why at most ten? You’ll see later.)\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-a-visit-from-the-tilburg-university/__optimized-images__/blog1.png\",\n      alt: \"\",\n      width: \"837\",\n      height: \"661\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we open our Polynomial Regression widget, where we play with polynomial degree. Polynomial Degree 1 gives us a line. With coefficient 2 we get a curve that fits only one point. 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+Now go out and share the knowledge!!!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-10-02 12:52:27+00:00","draft":false,"title":"A visit from the Tilburg University","blog":["education","examples","overfitting","regression","visualization"],"oldUrl":"/blog/2015/10/02/a-visit-from-the-tilburg-university/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Biolab is currently hosting two amazing data scientists from the Tilburg University - dr. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">Accepted GSoC 2011 students announced</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 25, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Accepted proposals/projects for Google Summer of Code 2011 have been <a href="http://socghop.appspot.com/gsoc/projects/list/google/gsoc2011">announced</a>. We got 3 students which will this year work on Orange:</p>
 <ul>
 <li>
@@ -162,4 +162,4 @@
 <p>Wencan Luo: <a href="http://socghop.appspot.com/gsoc/project/google/gsoc2011/wcluo/14001">Multi-label classification</a></p>
 </li>
 </ul>
-<p>Congrats to all accepted students. We are looking forward working with you. And for all other students: please apply again next year. Your proposals were good, but we just could not accept everybody.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-04-25 08:22:00+00:00","draft":false,"title":"Accepted GSoC 2011 students announced","blog":["gsoc"],"oldUrl":"/blog/2011/04/25/accepted-gsoc-2011-students-announced/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Accepted proposals/projects for Google Summer of Code 2011 have been \", _jsx(_components.a, {\n        href: \"http://socghop.appspot.com/gsoc/projects/list/google/gsoc2011\",\n        children: \"announced\"\n      }), \". 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+<p>Congrats to all accepted students. We are looking forward working with you. And for all other students: please apply again next year. Your proposals were good, but we just could not accept everybody.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-04-25 08:22:00+00:00","draft":false,"title":"Accepted GSoC 2011 students announced","blog":["gsoc"],"oldUrl":"/blog/2011/04/25/accepted-gsoc-2011-students-announced/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Accepted proposals/projects for Google Summer of Code 2011 have been \", _jsx(_components.a, {\n        href: \"http://socghop.appspot.com/gsoc/projects/list/google/gsoc2011\",\n        children: \"announced\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>pivot table, aggregate, data, groupby</strong></p><h1 class="sc-c67a4900-0 loxVcf">Aggregate, Group By and Pivot with... Pivot Table!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 27, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange recently welcomed its new Pivot Table widget, which offers functionalities for data aggregation, grouping and, well, pivot tables. The widget is a one-stop-shop for pandas&#x27; aggregate, groupby and pivot_table functions.</p>
 <a href="/blog/2019-08-pivot-table/__optimized-images__/2019-08-27_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="538" height="356" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-08-pivot-table/__optimized-images__/2019-08-27_workflow.png"/></a>
 <p>Let us see how to achieve these tasks in Orange. For all of the below examples we will be using the <em>heart_disease.tab</em> data.</p>
@@ -195,4 +195,4 @@ <h2>pandas.DataFrame.pivot_table</h2>
 <p>In Orange:</p>
 <p>In <strong>Pivot Table</strong> set <em>Rows</em> to <em>diameter narrowing</em>, <em>Columns</em> to <em>gender</em>, <em>Values</em> to <em>age</em> and aggregation method to <em>mean</em>. The widget already offers a view of the final data table, but we can also output it and use it in other Orange widgets.</p>
 <a href="/blog/2019-08-pivot-table/__optimized-images__/2019-08-27_pivot-table.png" data-gallery="true"><img alt="" loading="lazy" width="569" height="525" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-08-pivot-table/__optimized-images__/2019-08-27_pivot-table.png"/></a>
-<p><strong>Pivot Table</strong> widget really versatile - like a Swiss knife for data transformation.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-08-27","draft":false,"title":"Aggregate, Group By and Pivot with... 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+<p><strong>Pivot Table</strong> widget really versatile - like a Swiss knife for data transformation.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-08-27","draft":false,"title":"Aggregate, Group By and Pivot with... 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In other words, we want to see the average age of females with diameter narrowing, males with diameter narrowing, females without diameter narrowing and males without diameter narrowing.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html#pandas.DataFrame.pivot_table\",\n        children: \"In pandas\"\n      }), \":\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"\u003e\u003e\u003e pd.pivot_table(df, values='age', index=['diameter narrowing'], columns=['gender'], aggfunc=np.mean)\\ngender                 female       male\\ndiameter narrowing\\n0                   54.555556  51.043478\\n1                   59.080000  56.087719\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Orange:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.strong, {\n        children: \"Pivot Table\"\n      }), \" set \", _jsx(_components.em, {\n        children: \"Rows\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \", \", _jsx(_components.em, {\n        children: \"Columns\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"gender\"\n      }), \", \", _jsx(_components.em, {\n        children: \"Values\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" and aggregation method to \", _jsx(_components.em, {\n        children: \"mean\"\n      }), \". 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - All I See is Silhouette</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - All I See is Silhouette"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-03-23 09:09:26+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="forestlearner"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="plot"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="24"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, classification, clustering, examples, forestlearner, orange3, plot, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">All I See is Silhouette</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 23, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Silhouette plot is such a nice method for visually assessing cluster quality and the degree of cluster membership that we simply couldn&#x27;t wait to get it into Orange3. And now we did.</p>
 <p>What this visualization displays is the average distance between instances within the cluster and instances in the nearest cluster. For a given data instance, the silhouette close to 1 indicates that the data instance is close to the center of the cluster. Instances with silhouette scores close to 0 are on the border between two clusters. Overall, the quality of the clustering could be assessed by the average silhouette scores of the data instances. But here, we are more interested in the individual silhouettes and their visualization in the silhouette plot.</p>
 <p>Using the good old iris data set, we are going to assess the silhouettes for each of the data instances. In k-means we set the number of clusters to 3 and send the data to Silhouette plot. Good clusters should include instances with higher silhouette scores. But we&#x27;re doing the opposite. In Orange, we are selecting instances with scores close to 0 from the silhouette plot and pass them to other widgets for exploration. No surprise, they are at the periphery of two clusters. This is so perfectly demonstrated in the scatter plot.</p>
@@ -160,4 +160,4 @@
 <a href="/blog/2016-03-all-i-see-is-silhouette/__optimized-images__/silhouette3-1.png" data-gallery="true"><img alt="" loading="lazy" width="1562" height="993" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-03-all-i-see-is-silhouette/__optimized-images__/silhouette3-1.png"/></a>
 <p>Finally, we can observe these instances in the Scatter Plot. Classifiers indeed have problems with borderline data instances. Our hypothesis was correct.</p>
 <a href="/blog/2016-03-all-i-see-is-silhouette/__optimized-images__/silhouette4-1.png" data-gallery="true"><img alt="" loading="lazy" width="1520" height="1052" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-03-all-i-see-is-silhouette/__optimized-images__/silhouette4-1.png"/></a>
-<p>Silhouette plot is yet another one of the great visualizations that can help you with data analysis or with understanding certain machine learning concepts. What did we say? Fruitful and fun!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-03-23 09:09:26+00:00","draft":false,"title":"All I See is Silhouette","blog":["analysis","classification","clustering","examples","forestlearner","orange3","plot","visualization"],"oldUrl":"/blog/2016/03/23/all-i-see-is-silhouette/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Silhouette plot is such a nice method for visually assessing cluster quality and the degree of cluster membership that we simply couldn't wait to get it into Orange3. And now we did.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What this visualization displays is the average distance between instances within the cluster and instances in the nearest cluster. For a given data instance, the silhouette close to 1 indicates that the data instance is close to the center of the cluster. Instances with silhouette scores close to 0 are on the border between two clusters. Overall, the quality of the clustering could be assessed by the average silhouette scores of the data instances. But here, we are more interested in the individual silhouettes and their visualization in the silhouette plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using the good old iris data set, we are going to assess the silhouettes for each of the data instances. In k-means we set the number of clusters to 3 and send the data to Silhouette plot. Good clusters should include instances with higher silhouette scores. But we're doing the opposite. 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+<p>Silhouette plot is yet another one of the great visualizations that can help you with data analysis or with understanding certain machine learning concepts. What did we say? Fruitful and fun!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-03-23 09:09:26+00:00","draft":false,"title":"All I See is Silhouette","blog":["analysis","classification","clustering","examples","forestlearner","orange3","plot","visualization"],"oldUrl":"/blog/2016/03/23/all-i-see-is-silhouette/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Silhouette plot is such a nice method for visually assessing cluster quality and the degree of cluster membership that we simply couldn't wait to get it into Orange3. And now we did.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What this visualization displays is the average distance between instances within the cluster and instances in the nearest cluster. For a given data instance, the silhouette close to 1 indicates that the data instance is close to the center of the cluster. Instances with silhouette scores close to 0 are on the border between two clusters. Overall, the quality of the clustering could be assessed by the average silhouette scores of the data instances. But here, we are more interested in the individual silhouettes and their visualization in the silhouette plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using the good old iris data set, we are going to assess the silhouettes for each of the data instances. In k-means we set the number of clusters to 3 and send the data to Silhouette plot. Good clusters should include instances with higher silhouette scores. But we're doing the opposite. 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Say, by a random forest.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-03-all-i-see-is-silhouette/__optimized-images__/silhouette1-1.png\",\n      alt: \"\",\n      width: \"662\",\n      height: \"339\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will use ten-fold cross validation in Test\u0026Score, send the evaluation results to confusion matrix and select misclassified instances in the widget. Then we will explore the inclusion of these misclassifications in the set of low-silhouette instances in the Venn diagram. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>survival analysis, kaplan-meier</strong></p><h1 class="sc-c67a4900-0 loxVcf">An introduction to the Kaplan-Meier Estimator</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ela Praznik</strong></p><p class="sc-b9a2839b-4 ehbERY">May 25, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The Kaplan-Meier Estimator is a function that relates time to the probability of an event of interest. It is widely used in survival analysis and with the addition of the Survival Analysis package to Orange it is now readily available for accessible visual analytics. However, any kind of efficient data analysis requires a basic understanding of what the implemented model does. This blog post thus aims to:</p>
 <ul>
 <li>explain the most important concepts needed for understanding the Kaplan-Meier Estimator,</li>
@@ -187,4 +187,4 @@
 <p>We can see this reflected in the survival curves of the three grades: patients with grade 1 tumors have the highest survival probability throughout the study and patients with grade 3 have the lowest. We can also plot the confidence intervals and check if they overlap. The confidence intervals of all three survival curves are separated for the first two years (700 days), meaning that there is probably a significant difference in recurrence of breast cancer between different tumor grades in the first two years.</p>
 <p>Finally, we can compare the median survival times of the groups using the Log-Rank test. The null hypothesis is that there is no difference in survival between the two groups and a p-value below 0.05 allows us to reject the null hypothesis and indicates a significant difference. We can see that there indeed is a significant difference in recurrence-free survival time between the three groups. However, the Log Rank test does not tell us among which groups. This too can be done with additional statistics tests but for this blog post, we&#x27;ve covered more than enough.</p>
 <a href="/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmgroups.png" data-gallery="true"><img alt="" loading="lazy" width="1998" height="1620" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmgroups.png"/></a>
-<p>You now hopefully have a basic understanding of the working of the Kaplan-Meier Estimator and know how to implement it in Orange using just a few clicks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ela Praznik","date":"2022-05-25","draft":false,"title":"An introduction to the Kaplan-Meier Estimator","thumbImage":"2022-05-26-kmgroups-thumbImage.png","frontPageImage":"2022-05-26-kmgroups-thumbImage.png","blog":["survival analysis","kaplan-meier"],"shortExcerpt":"Understanding the Kaplan-Meier widget in the Survival Analysis add-on.","longExcerpt":"The Survival Analysis add-on offers accesible tools for survival analysis, the Kaplan-Meier widget being a prime example. In this blogpost we provide an introduction to the Kaplan-Meier Estimator using a simple example and show how the tool can be used in Orange on larger datasets.","x2images":true,"oldUrl":"/blog/2022/2022-05-25-KaplanMeier/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The Kaplan-Meier Estimator is a function that relates time to the probability of an event of interest. It is widely used in survival analysis and with the addition of the Survival Analysis package to Orange it is now readily available for accessible visual analytics. However, any kind of efficient data analysis requires a basic understanding of what the implemented model does. This blog post thus aims to:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"explain the most important concepts needed for understanding the Kaplan-Meier Estimator,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"show how to manually calculate and interpret a survival curve to build an intuitive understanding and\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"show how this can be implemented in Orange with a few clicks.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Why the need for a special method?\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The \\\"survival\\\" in survival analysis gets its name from its use in the analysis of the survival of patients. However, the event of interest can be many things: a disease relapse, the first technical failure of a car, or a user leaving a webpage. There are many real-world problems where we would like to estimate the probability of an event happening in relation to the passing of time. For such estimations, we need a survival dataset, which necessarily includes data instances characterized by the occurrence of an event and the time at which it did.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Perhaps the easiest way of getting an understanding of the concepts involved in the estimation of a survival function is with a simple example. Say we get our hands on a tiny dataset from a not-so-grim survival scenario: 10 people received 10 flowers for their birthday and tracked when the flowers wither. The tracking lasted for 10 days. Some flowers wither quicker than others and we are interested in the probability of a flower withering over time. The two main parameters of interest are thus time (in days) and flower withering-status, where \\\"status = 1\\\" means withered and \\\"status = 0\\\" means not-withered. How are we to go about determining the probability of withering over time?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-flowerdata.png\",\n      width: \"443\",\n      height: \"438\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-flowerdata.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There are two distinct problems when it comes to the analysis of survival:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"The first one has to do with the fact that \\\"status = 0\\\" does not necessarily mean that the flower has survived. What it does mean is that from that moment on the flower is no longer part of the ongoing study. For instance, a cat might have knocked over the vase and the flower was then discarded along with the broken vase. This does not mean the flower has withered nor that it has survived. We do not want to discard these kinds of data instances, so instead, we label them as \", _jsx(_components.strong, {\n          children: \"censored\"\n        }), \". This is what \\\"status = 0\\\" really stands for.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"The second problem has to do with time. First, we do not necessarily have status data available for each day and indeed in our flower dataset we do not - we only have status data for 7 out of 10 days. Furthermore, the effects (for instance, of being without a root) accumulate over time. Even though a flower might have survived for 5 days, its survival probability on day 5 is not 100 %. A decrease in the survival probability also accumulates over time, so it cannot be determined by simply counting the survivors and non-survivors on a particular day.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So which statistical tool should we use?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Although the fact that we have a numeric variable, time, might tempt us to use linear regression this will not be very useful since we just mentioned that the survival probability is non-linear. We might try logistic regression since the probability curve produced by it is not linear and might catch the trend of survival over time better. But logistic regression won't do either. The fifth flower has a status 0 on day 3. As we said before, this does not mean the flower has withered - it only means something happened to it that prevented it from continuing to be part of the study. The probabilities in logistic regression would count this flower as if it survived after the third day, simply because its status is not 1. Thus, logistic regression overestimates survival probability and is therefore also an inappropriate tool to analyze survival data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is where the Kaplan-Meier Estimator comes in. It solves both issues, since it models non-linear survival probabilities over time while accounting for subjects of the study who were unable to continue to be part of the study, but did not experience the event of interest. It does so by marking these observances as censored since it is inappropriate to say that the subject (flower) either withered or survived.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Calculating the survival curve manually\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Back to our example. Let's imagine 10 people that all have their birthday on the same day. Their mothers, friends, or spouses go and buy them a flower on that day. The probability of a flower withering the day before is probably zero since flower shops don't sell withered flowers. On the day when these freshly cut flowers are sold and given as a gift none of them wither, but the next day three do. From this, we can calculate the probability of withering and the probability of survival for that day. The probability of withering is the number of flowers withered divided by all the flowers that are at risk on that particular day:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Probability of withering: 3/10 = 0.3 = 30%\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Probability of surviving: 7/10 = 0.7 = 70%\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The probability of surviving is cumulative and so we have to multiply the probability of surviving that day with the previous probability of surviving. Since the probability of withering on the first day was 0 this means the survival probability was 1 and the multiplication does not change anything for the second day.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"On the third day, another flower withered and one stopped being part of the experiment for an unknown reason (probably that pesky cat). We have to remove it from the number of flowers at risk of withering for the next day. So while on the third day there are 7 flowers at risk, since 3 withered away the day before, on the fourth day there are only 5 at risk, since the day before one withered away and one got censored for an unknown reason. So the probability of withering on the fourth day is 1/5 and the probability of surviving is 4/5 multiplied by the probability of surviving the previous day, which was 0.6. Let's display all our calculations in a table:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-kmmanually.png\",\n      width: \"1037\",\n      height: \"371\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmmanually.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"On the 5th and 6th days, none of the remaining flowers withered but two got censored. There is no need to calculate the probability of survival on days where there are only censored data points, but for the sake of clarity, we included the calculations in the table. We can see that the censored data affects the number of flowers at risk but does not change the survival probability. On the eighth day, there are thus only 2 flowers at risk, and one of them withers. Taking into account the survival probability of the previous day this gives us the probability of survival of 0.24.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What would happen if we ignored censored data? We could just exclude them from the analysis but we don't want to do that, because these data points are still informative when it comes to survival. And if we would treat them as surviving we would overestimate the survival probability.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we plot the survival probability for each day we get the Kaplan-Meier Plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Orange use case\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now that we calculated the Kaplan-Meier estimator on a small dataset manually and got an understanding of what's going on, let's perform this on a larger dataset in Orange. We will use the data from the German Breast Cancer Study Group 2, a survival dataset that is made readily available in Orange. First, we load the data using the \", _jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget and inspect the data in tabular format with the use of \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \". We see that our data consists of 686 data instances that stand for patients. Each patient is characterized by 8 clinical features and the time at which recurrence of the disease happened or when the patient stopped being part of the experiment due to unknown reasons (censored). We then connect our data with the \", _jsx(_components.strong, {\n        children: \"Kaplan-Meier widget\"\n      }), \" that calculates the survival probability at different time points and plots the survival curve. The widget also allows us to choose one of the three categorical features and compare the survival curves of the different categories.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-kmworkflow.png\",\n      width: \"1185\",\n      height: \"977\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmworkflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Comparing two or more survival curves can be done by visual inspection or by comparing different statistics. For instance, we can compare the survival curves of patients according to their tumor grade which is a quantitative measure of how abnormal the tumor cells and tissue look under a microscope. For characterizing breast cancer grades doctors most often use the Nottingham grading system which divides the tumor into three grades, where 1 corresponds to low grade, 2 to intermediate grade, and 3 to high grade. The high grade means that the cells and tissue look most abnormal compared to healthy tissue.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see this reflected in the survival curves of the three grades: patients with grade 1 tumors have the highest survival probability throughout the study and patients with grade 3 have the lowest. We can also plot the confidence intervals and check if they overlap. The confidence intervals of all three survival curves are separated for the first two years (700 days), meaning that there is probably a significant difference in recurrence of breast cancer between different tumor grades in the first two years.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, we can compare the median survival times of the groups using the Log-Rank test. The null hypothesis is that there is no difference in survival between the two groups and a p-value below 0.05 allows us to reject the null hypothesis and indicates a significant difference. We can see that there indeed is a significant difference in recurrence-free survival time between the three groups. However, the Log Rank test does not tell us among which groups. This too can be done with additional statistics tests but for this blog post, we've covered more than enough.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-kmgroups.png\",\n      width: \"1998\",\n      height: \"1620\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmgroups.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You now hopefully have a basic understanding of the working of the Kaplan-Meier Estimator and know how to implement it in Orange using just a few clicks.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-05-KaplanMeier/__optimized-images__/2022-05-26-kmgroups-thumbImage.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"an-introduction-to-the-kaplan-meier-estimator"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>You now hopefully have a basic understanding of the working of the Kaplan-Meier Estimator and know how to implement it in Orange using just a few clicks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ela Praznik","date":"2022-05-25","draft":false,"title":"An introduction to the Kaplan-Meier Estimator","thumbImage":"2022-05-26-kmgroups-thumbImage.png","frontPageImage":"2022-05-26-kmgroups-thumbImage.png","blog":["survival analysis","kaplan-meier"],"shortExcerpt":"Understanding the Kaplan-Meier widget in the Survival Analysis add-on.","longExcerpt":"The Survival Analysis add-on offers accesible tools for survival analysis, the Kaplan-Meier widget being a prime example. In this blogpost we provide an introduction to the Kaplan-Meier Estimator using a simple example and show how the tool can be used in Orange on larger datasets.","x2images":true,"oldUrl":"/blog/2022/2022-05-25-KaplanMeier/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The Kaplan-Meier Estimator is a function that relates time to the probability of an event of interest. It is widely used in survival analysis and with the addition of the Survival Analysis package to Orange it is now readily available for accessible visual analytics. However, any kind of efficient data analysis requires a basic understanding of what the implemented model does. This blog post thus aims to:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"explain the most important concepts needed for understanding the Kaplan-Meier Estimator,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"show how to manually calculate and interpret a survival curve to build an intuitive understanding and\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"show how this can be implemented in Orange with a few clicks.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Why the need for a special method?\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The \\\"survival\\\" in survival analysis gets its name from its use in the analysis of the survival of patients. However, the event of interest can be many things: a disease relapse, the first technical failure of a car, or a user leaving a webpage. There are many real-world problems where we would like to estimate the probability of an event happening in relation to the passing of time. For such estimations, we need a survival dataset, which necessarily includes data instances characterized by the occurrence of an event and the time at which it did.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Perhaps the easiest way of getting an understanding of the concepts involved in the estimation of a survival function is with a simple example. Say we get our hands on a tiny dataset from a not-so-grim survival scenario: 10 people received 10 flowers for their birthday and tracked when the flowers wither. The tracking lasted for 10 days. Some flowers wither quicker than others and we are interested in the probability of a flower withering over time. The two main parameters of interest are thus time (in days) and flower withering-status, where \\\"status = 1\\\" means withered and \\\"status = 0\\\" means not-withered. How are we to go about determining the probability of withering over time?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-flowerdata.png\",\n      width: \"443\",\n      height: \"438\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-flowerdata.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There are two distinct problems when it comes to the analysis of survival:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"The first one has to do with the fact that \\\"status = 0\\\" does not necessarily mean that the flower has survived. What it does mean is that from that moment on the flower is no longer part of the ongoing study. For instance, a cat might have knocked over the vase and the flower was then discarded along with the broken vase. This does not mean the flower has withered nor that it has survived. We do not want to discard these kinds of data instances, so instead, we label them as \", _jsx(_components.strong, {\n          children: \"censored\"\n        }), \". This is what \\\"status = 0\\\" really stands for.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"The second problem has to do with time. First, we do not necessarily have status data available for each day and indeed in our flower dataset we do not - we only have status data for 7 out of 10 days. Furthermore, the effects (for instance, of being without a root) accumulate over time. Even though a flower might have survived for 5 days, its survival probability on day 5 is not 100 %. A decrease in the survival probability also accumulates over time, so it cannot be determined by simply counting the survivors and non-survivors on a particular day.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So which statistical tool should we use?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Although the fact that we have a numeric variable, time, might tempt us to use linear regression this will not be very useful since we just mentioned that the survival probability is non-linear. We might try logistic regression since the probability curve produced by it is not linear and might catch the trend of survival over time better. But logistic regression won't do either. The fifth flower has a status 0 on day 3. As we said before, this does not mean the flower has withered - it only means something happened to it that prevented it from continuing to be part of the study. The probabilities in logistic regression would count this flower as if it survived after the third day, simply because its status is not 1. Thus, logistic regression overestimates survival probability and is therefore also an inappropriate tool to analyze survival data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is where the Kaplan-Meier Estimator comes in. It solves both issues, since it models non-linear survival probabilities over time while accounting for subjects of the study who were unable to continue to be part of the study, but did not experience the event of interest. It does so by marking these observances as censored since it is inappropriate to say that the subject (flower) either withered or survived.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Calculating the survival curve manually\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Back to our example. Let's imagine 10 people that all have their birthday on the same day. Their mothers, friends, or spouses go and buy them a flower on that day. The probability of a flower withering the day before is probably zero since flower shops don't sell withered flowers. On the day when these freshly cut flowers are sold and given as a gift none of them wither, but the next day three do. From this, we can calculate the probability of withering and the probability of survival for that day. The probability of withering is the number of flowers withered divided by all the flowers that are at risk on that particular day:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Probability of withering: 3/10 = 0.3 = 30%\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Probability of surviving: 7/10 = 0.7 = 70%\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The probability of surviving is cumulative and so we have to multiply the probability of surviving that day with the previous probability of surviving. Since the probability of withering on the first day was 0 this means the survival probability was 1 and the multiplication does not change anything for the second day.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"On the third day, another flower withered and one stopped being part of the experiment for an unknown reason (probably that pesky cat). We have to remove it from the number of flowers at risk of withering for the next day. So while on the third day there are 7 flowers at risk, since 3 withered away the day before, on the fourth day there are only 5 at risk, since the day before one withered away and one got censored for an unknown reason. So the probability of withering on the fourth day is 1/5 and the probability of surviving is 4/5 multiplied by the probability of surviving the previous day, which was 0.6. Let's display all our calculations in a table:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-kmmanually.png\",\n      width: \"1037\",\n      height: \"371\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmmanually.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"On the 5th and 6th days, none of the remaining flowers withered but two got censored. There is no need to calculate the probability of survival on days where there are only censored data points, but for the sake of clarity, we included the calculations in the table. We can see that the censored data affects the number of flowers at risk but does not change the survival probability. On the eighth day, there are thus only 2 flowers at risk, and one of them withers. Taking into account the survival probability of the previous day this gives us the probability of survival of 0.24.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What would happen if we ignored censored data? We could just exclude them from the analysis but we don't want to do that, because these data points are still informative when it comes to survival. And if we would treat them as surviving we would overestimate the survival probability.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we plot the survival probability for each day we get the Kaplan-Meier Plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Orange use case\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now that we calculated the Kaplan-Meier estimator on a small dataset manually and got an understanding of what's going on, let's perform this on a larger dataset in Orange. We will use the data from the German Breast Cancer Study Group 2, a survival dataset that is made readily available in Orange. First, we load the data using the \", _jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget and inspect the data in tabular format with the use of \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \". We see that our data consists of 686 data instances that stand for patients. Each patient is characterized by 8 clinical features and the time at which recurrence of the disease happened or when the patient stopped being part of the experiment due to unknown reasons (censored). We then connect our data with the \", _jsx(_components.strong, {\n        children: \"Kaplan-Meier widget\"\n      }), \" that calculates the survival probability at different time points and plots the survival curve. The widget also allows us to choose one of the three categorical features and compare the survival curves of the different categories.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-KaplanMeier/2022-04-20-kmworkflow.png\",\n      width: \"1185\",\n      height: \"977\",\n      src: \"/blog/2022-05-KaplanMeier/__optimized-images__/2022-04-20-kmworkflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Comparing two or more survival curves can be done by visual inspection or by comparing different statistics. For instance, we can compare the survival curves of patients according to their tumor grade which is a quantitative measure of how abnormal the tumor cells and tissue look under a microscope. For characterizing breast cancer grades doctors most often use the Nottingham grading system which divides the tumor into three grades, where 1 corresponds to low grade, 2 to intermediate grade, and 3 to high grade. The high grade means that the cells and tissue look most abnormal compared to healthy tissue.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see this reflected in the survival curves of the three grades: patients with grade 1 tumors have the highest survival probability throughout the study and patients with grade 3 have the lowest. We can also plot the confidence intervals and check if they overlap. The confidence intervals of all three survival curves are separated for the first two years (700 days), meaning that there is probably a significant difference in recurrence of breast cancer between different tumor grades in the first two years.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, we can compare the median survival times of the groups using the Log-Rank test. The null hypothesis is that there is no difference in survival between the two groups and a p-value below 0.05 allows us to reject the null hypothesis and indicates a significant difference. We can see that there indeed is a significant difference in recurrence-free survival time between the three groups. However, the Log Rank test does not tell us among which groups. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, clustering, data, dataloading, orange3, visualization, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Analyzing Surveys</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 26, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Our streak of workshops continues. This time we taught professionals from public administration how they can leverage data analytics and machine learning to retrieve interesting information from surveys. Thanks to the <a href="http://www.mju.gov.si/en/">Ministry of Public Administration</a>, this is only the first in a line of workshops on data science we are preparing for public sector employees.</p>
 <a href="/blog/2017-10-analyzing-surveys/__optimized-images__/FRI_2422.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="467" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-10-analyzing-surveys/__optimized-images__/FRI_2422.jpg"/></a>
 <p>For this purpose, we have designed EnKlik Anketa widget, which you can find in Prototypes add-on. The widget reads data from a Slovenian online survey service <a href="http://english.1ka.si/">OneClick Survey</a> and imports the results directly into Orange.</p>
@@ -172,4 +172,4 @@
 Box Plot separates distributions by Cluster and orders attributes by how well they split selected subgroups.</p>
 <a href="/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.45.32.png" data-gallery="true"><img alt="" loading="lazy" width="992" height="192" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.45.32.png"/></a>
 <p>The final workflow.</p>
-<p>Seems like our second cluster (C2) is the sporty one. If we are serving in the public administration, perhaps we can design initiatives targeting cluster C1 to do more sports. It is so easy to analyze the data in Orange!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-10-26 11:03:10+00:00","draft":false,"title":"Analyzing Surveys","blog":["analysis","clustering","data","dataloading","orange3","visualization","workshop"],"oldUrl":"/blog/2017/10/26/analyzing-surveys/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Our streak of workshops continues. This time we taught professionals from public administration how they can leverage data analytics and machine learning to retrieve interesting information from surveys. Thanks to the \", _jsx(_components.a, {\n        href: \"http://www.mju.gov.si/en/\",\n        children: \"Ministry of Public Administration\"\n      }), \", this is only the first in a line of workshops on data science we are preparing for public sector employees.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/FRI_2422.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"467\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this purpose, we have designed EnKlik Anketa widget, which you can find in Prototypes add-on. The widget reads data from a Slovenian online survey service \", _jsx(_components.a, {\n        href: \"http://english.1ka.si/\",\n        children: \"OneClick Survey\"\n      }), \" and imports the results directly into Orange.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We have prepared a test survey, which you can import by entering a public link to data into the widget. Here's the link: \", _jsx(_components.a, {\n        href: \"https://www.1ka.si/podatki/141025/72F5B3CC/\",\n        children: \"https://www.1ka.si/podatki/141025/72F5B3CC/\"\n      }), \" . Copy it into the Public link URL line in the widget. Once you press Enter, the widget loads the data and displays retrieved features, just like the File widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.29.10.png\",\n        alt: \"\",\n        width: \"1064\",\n        height: \"1104\"\n      }), \"\\nEnKlik Anketa widget is similar to the File widget. It also enables changing the attribute type and role.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The survey is in Slovenian, but we can use Edit Domain to turn feature names into English equivalent.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.30.53.png\",\n      alt: \"\",\n      width: \"1194\",\n      height: \"742\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We renamed attributes in order as they appear in the survey. If you load the survey yourself, you can rename them just like you see here.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As always, we can check the data in a Data Table. We have 41 respondents and 7 questions. Each respondent chose a nickname, which makes it easier to browse the data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.33.25.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"769\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we can perform familiar clustering to uncover interesting groups in our data. Connect Distances to Edit Domain and Hierarchical Clustering to Distances.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.36.26.png\",\n      alt: \"\",\n      width: \"1372\",\n      height: \"1582\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Distance from Pipi and Chad to other respondents is very high, which makes them complete outliers.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.36.46.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"626\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have two outliers, Pipi and Chad. One is an excessive sportsman (100 h of sport per week) and the other terminally ill (general health -1). Or perhaps they both simply didn't fill out the survey correctly. If we use the Data Table to filter out Pipi and Chad, we get a fairly good clustering.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.41.01.png\",\n      alt: \"\",\n      width: \"1344\",\n      height: \"1522\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can use Box Plot, to observe what makes each cluster special. Connect Box Plot to Hierarchical Clustering (with the two groups selected), select grouping by_ Cluster_ and tick \", _jsx(_components.em, {\n        children: \"Order by relevance\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.41.50.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"937\"\n      }), \"\\nBox Plot separates distributions by Cluster and orders attributes by how well they split selected subgroups.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.45.32.png\",\n      alt: \"\",\n      width: \"992\",\n      height: \"192\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The final workflow.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Seems like our second cluster (C2) is the sporty one. 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+<p>Seems like our second cluster (C2) is the sporty one. If we are serving in the public administration, perhaps we can design initiatives targeting cluster C1 to do more sports. It is so easy to analyze the data in Orange!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-10-26 11:03:10+00:00","draft":false,"title":"Analyzing Surveys","blog":["analysis","clustering","data","dataloading","orange3","visualization","workshop"],"oldUrl":"/blog/2017/10/26/analyzing-surveys/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Our streak of workshops continues. This time we taught professionals from public administration how they can leverage data analytics and machine learning to retrieve interesting information from surveys. Thanks to the \", _jsx(_components.a, {\n        href: \"http://www.mju.gov.si/en/\",\n        children: \"Ministry of Public Administration\"\n      }), \", this is only the first in a line of workshops on data science we are preparing for public sector employees.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/FRI_2422.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"467\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this purpose, we have designed EnKlik Anketa widget, which you can find in Prototypes add-on. The widget reads data from a Slovenian online survey service \", _jsx(_components.a, {\n        href: \"http://english.1ka.si/\",\n        children: \"OneClick Survey\"\n      }), \" and imports the results directly into Orange.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We have prepared a test survey, which you can import by entering a public link to data into the widget. Here's the link: \", _jsx(_components.a, {\n        href: \"https://www.1ka.si/podatki/141025/72F5B3CC/\",\n        children: \"https://www.1ka.si/podatki/141025/72F5B3CC/\"\n      }), \" . Copy it into the Public link URL line in the widget. Once you press Enter, the widget loads the data and displays retrieved features, just like the File widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.29.10.png\",\n        alt: \"\",\n        width: \"1064\",\n        height: \"1104\"\n      }), \"\\nEnKlik Anketa widget is similar to the File widget. It also enables changing the attribute type and role.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The survey is in Slovenian, but we can use Edit Domain to turn feature names into English equivalent.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.30.53.png\",\n      alt: \"\",\n      width: \"1194\",\n      height: \"742\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We renamed attributes in order as they appear in the survey. If you load the survey yourself, you can rename them just like you see here.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As always, we can check the data in a Data Table. We have 41 respondents and 7 questions. Each respondent chose a nickname, which makes it easier to browse the data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.33.25.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"769\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we can perform familiar clustering to uncover interesting groups in our data. Connect Distances to Edit Domain and Hierarchical Clustering to Distances.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.36.26.png\",\n      alt: \"\",\n      width: \"1372\",\n      height: \"1582\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Distance from Pipi and Chad to other respondents is very high, which makes them complete outliers.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.36.46.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"626\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have two outliers, Pipi and Chad. One is an excessive sportsman (100 h of sport per week) and the other terminally ill (general health -1). Or perhaps they both simply didn't fill out the survey correctly. If we use the Data Table to filter out Pipi and Chad, we get a fairly good clustering.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.41.01.png\",\n      alt: \"\",\n      width: \"1344\",\n      height: \"1522\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can use Box Plot, to observe what makes each cluster special. Connect Box Plot to Hierarchical Clustering (with the two groups selected), select grouping by_ Cluster_ and tick \", _jsx(_components.em, {\n        children: \"Order by relevance\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.41.50.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"937\"\n      }), \"\\nBox Plot separates distributions by Cluster and orders attributes by how well they split selected subgroups.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-analyzing-surveys/__optimized-images__/Screen-Shot-2017-10-26-at-12.45.32.png\",\n      alt: \"\",\n      width: \"992\",\n      height: \"192\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The final workflow.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Seems like our second cluster (C2) is the sporty one. If we are serving in the public administration, perhaps we can design initiatives targeting cluster C1 to do more sports. It is so easy to analyze the data in Orange!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"analyzing-surveys"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Association Rules in Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Association Rules in Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-04-25 07:46:33+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="association rules"/><meta property="article:tag" content="business intelligence"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="toolbox"/><meta name="next-head-count" content="23"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, association rules, business intelligence, examples, orange3, toolbox</strong></p><h1 class="sc-c67a4900-0 loxVcf">Association Rules in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 25, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange is welcoming back one of its more exciting add-ons: <a href="https://pypi.python.org/pypi/Orange3-Associate">Associate</a>! <a href="https://en.wikipedia.org/wiki/Association_rule_learning">Association rules</a> can help the user quickly and simply discover the underlying relationships and connections between data instances. Yeah!</p>
 <p>The add-on currently has two widgets: one for Association Rules and the other for Frequent Itemsets. With Frequent Itemsets we first check frequency of items and itemsets in our transaction matrix. This tell us which items (products) and itemsets are the most frequent in our data, so it would make a lot of sense focusing on these products. Let&#x27;s use this widget on real Foodmart 2000 data set.</p>
 <a href="/blog/2016-04-association-rules-in-orange/__optimized-images__/blog5-1.png" data-gallery="true"><img alt="" loading="lazy" width="860" height="541" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-04-association-rules-in-orange/__optimized-images__/blog5-1.png"/></a>
@@ -175,4 +175,4 @@
 <p>fresh vegetables, plastic utensils, bologna, soda --&gt; chocolate candy</p>
 <p>These seem to picnickers, clients who don&#x27;t want to spend a whole lot of time preparing their food. The first group is probably more gourmet, while the second seems to enjoy sweets. A logical step would be to place dried fruit closer to the wine section and the candy bars closer to sodas. What do you say? This already happened in your local supermarket? Coincidence? I don&#x27;t think so. :)</p>
 <a href="/blog/2016-04-association-rules-in-orange/__optimized-images__/blog6.png" data-gallery="true"><img alt="" loading="lazy" width="467" height="279" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-04-association-rules-in-orange/__optimized-images__/blog6.png"/></a>
-<p>Association rules are a powerful way to improve your business by organizing your actual or online store, adjusting marketing strategies to target suitable groups, providing product recommendations and generally understanding your client base better. Just another way Orange can be used as a business intelligence tool!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-04-25 07:46:33+00:00","draft":false,"title":"Association Rules in Orange","blog":["addons","analysis","association rules","business intelligence","examples","orange3","toolbox"],"oldUrl":"/blog/2016/04/25/association-rules-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange is welcoming back one of its more exciting add-ons: \", _jsx(_components.a, {\n        href: \"https://pypi.python.org/pypi/Orange3-Associate\",\n        children: \"Associate\"\n      }), \"! \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Association_rule_learning\",\n        children: \"Association rules\"\n      }), \" can help the user quickly and simply discover the underlying relationships and connections between data instances. Yeah!\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The add-on currently has two widgets: one for Association Rules and the other for Frequent Itemsets. With Frequent Itemsets we first check frequency of items and itemsets in our transaction matrix. This tell us which items (products) and itemsets are the most frequent in our data, so it would make a lot of sense focusing on these products. Let's use this widget on real Foodmart 2000 data set.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-04-association-rules-in-orange/__optimized-images__/blog5-1.png\",\n      alt: \"\",\n      width: \"860\",\n      height: \"541\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First let's check our data set. We have 62560 instances with 102 features. That's a whole lot of transactions. Now we connect Frequent Itemsets to our File widget and observe the results. We went with a quite low minimal support due to the large number of transactions.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Collapse All will display the most frequent items, so these will be our most important products ('bestsellers'). Our clients seem to be buying a whole lot of fresh vegetables and fresh fruit. Call your marketing department - you could become the ultimate place to buy fruits and veggies from.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-04-association-rules-in-orange/__optimized-images__/blog2-1.png\",\n      alt: \"\",\n      width: \"635\",\n      height: \"435\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If there's a little arrow on the left side of the item, you can expand it to see all the other items connected to the selected attribute. So if a person buy fresh vegetables, it is most likely to buy fresh fruits as an accompanying product group. 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We probably want low support, since it will be hard to find a few prevailing rules for 62,000+ transactions. However, you want the discovered rules to be true most of the time, so increase the confidence.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-04-association-rules-in-orange/__optimized-images__/blog1.png\",\n      alt: \"\",\n      width: \"1062\",\n      height: \"595\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The table on the right displays a list of rules with 6 different measures of association rule quality:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"support: how often a rule is applicable to a given data set (rule/data)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"confidence: how frequently items in Y appear in transactions with X or in other words how frequently the rule is true (support for a rule/support of antecedent)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"coverage: how often antecedent item is found in the data set (support of antecedent/data)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"strength:  (support of consequent/support of antecedent)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"lift: how frequently a rule is true per consequent item (data * confidence/support of consequent)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"leverage: the difference between two item appearing in a transaction and the two items appearing independently (support*data - antecedent support * consequent support/data2)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange will rank the rules automatically. 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+<p>Association rules are a powerful way to improve your business by organizing your actual or online store, adjusting marketing strategies to target suitable groups, providing product recommendations and generally understanding your client base better. Just another way Orange can be used as a business intelligence tool!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-04-25 07:46:33+00:00","draft":false,"title":"Association Rules in Orange","blog":["addons","analysis","association rules","business intelligence","examples","orange3","toolbox"],"oldUrl":"/blog/2016/04/25/association-rules-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange is welcoming back one of its more exciting add-ons: \", _jsx(_components.a, {\n        href: \"https://pypi.python.org/pypi/Orange3-Associate\",\n        children: \"Associate\"\n      }), \"! \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Association_rule_learning\",\n        children: \"Association rules\"\n      }), \" can help the user quickly and simply discover the underlying relationships and connections between data instances. Yeah!\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The add-on currently has two widgets: one for Association Rules and the other for Frequent Itemsets. With Frequent Itemsets we first check frequency of items and itemsets in our transaction matrix. This tell us which items (products) and itemsets are the most frequent in our data, so it would make a lot of sense focusing on these products. Let's use this widget on real Foodmart 2000 data set.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-04-association-rules-in-orange/__optimized-images__/blog5-1.png\",\n      alt: \"\",\n      width: \"860\",\n      height: \"541\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First let's check our data set. We have 62560 instances with 102 features. That's a whole lot of transactions. Now we connect Frequent Itemsets to our File widget and observe the results. We went with a quite low minimal support due to the large number of transactions.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Collapse All will display the most frequent items, so these will be our most important products ('bestsellers'). Our clients seem to be buying a whole lot of fresh vegetables and fresh fruit. Call your marketing department - you could become the ultimate place to buy fruits and veggies from.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-04-association-rules-in-orange/__optimized-images__/blog2-1.png\",\n      alt: \"\",\n      width: \"635\",\n      height: \"435\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If there's a little arrow on the left side of the item, you can expand it to see all the other items connected to the selected attribute. So if a person buy fresh vegetables, it is most likely to buy fresh fruits as an accompanying product group. 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We probably want low support, since it will be hard to find a few prevailing rules for 62,000+ transactions. However, you want the discovered rules to be true most of the time, so increase the confidence.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-04-association-rules-in-orange/__optimized-images__/blog1.png\",\n      alt: \"\",\n      width: \"1062\",\n      height: \"595\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The table on the right displays a list of rules with 6 different measures of association rule quality:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"support: how often a rule is applicable to a given data set (rule/data)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"confidence: how frequently items in Y appear in transactions with X or in other words how frequently the rule is true (support for a rule/support of antecedent)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"coverage: how often antecedent item is found in the data set (support of antecedent/data)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"strength:  (support of consequent/support of antecedent)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"lift: how frequently a rule is true per consequent item (data * confidence/support of consequent)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"leverage: the difference between two item appearing in a transaction and the two items appearing independently (support*data - antecedent support * consequent support/data2)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange will rank the rules automatically. Now give a quick look at the rules. How about these two rules?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"fresh vegetables, plastic utensils, deli meats, wine --\u003e dried fruit\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"fresh vegetables, plastic utensils, bologna, soda --\u003e chocolate candy\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"These seem to picnickers, clients who don't want to spend a whole lot of time preparing their food. The first group is probably more gourmet, while the second seems to enjoy sweets. A logical step would be to place dried fruit closer to the wine section and the candy bars closer to sodas. What do you say? This already happened in your local supermarket? Coincidence? 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - BDTN 2016 Workshop: Introduction to Data Science</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - BDTN 2016 Workshop: Introduction to Data Science"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-12-16 15:57:11+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="education"/><meta property="article:tag" content="interactive data visualization"/><meta property="article:tag" content="tutorial"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, interactive data visualization, tutorial, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">BDTN 2016 Workshop: Introduction to Data Science</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 16, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Every year <a href="http://www.bestljubljana.si/">BEST Ljubljana</a> organizes BEST Days of Technology and Sciences, an event hosting a broad variety of workshops, hackathons and lectures for the students of natural sciences and technology. <strong>Introduction to Data Science</strong>, organized by our own Laboratory for Bioinformatics, was this year one of them.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/10/02/intro-to-data-mining-for-life-scientists/">Intro to Data Mining for Life Scientists</a></p>
 <p>The task was to teach and explain basic data mining concepts and techniques in four hours. To complete beginners. Not daunting at all...</p>
@@ -160,4 +160,4 @@
 <p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2/">Data Mining Course in Houston #2</a></p>
 <p>These workshops are not only fun, but an amazing learning opportunity for us as well, as they show how our users think and how to even further improve Orange.</p>
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+<a href="/blog/2016-12-bdtn-2016-workshop-introduction-to-data-science/__optimized-images__/IMG_9224.jpg" data-gallery="true"><img alt="" loading="lazy" width="3473" height="2315" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-12-bdtn-2016-workshop-introduction-to-data-science/__optimized-images__/IMG_9224.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-12-16 15:57:11+00:00","draft":false,"title":"BDTN 2016 Workshop: Introduction to Data Science","blog":["education","interactive data visualization","tutorial","workshop"],"oldUrl":"/blog/2016/12/16/bdtn-2016-workshop-introduction-to-data-science/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Every year \", _jsx(_components.a, {\n        href: \"http://www.bestljubljana.si/\",\n        children: \"BEST Ljubljana\"\n      }), \" organizes BEST Days of Technology and Sciences, an event hosting a broad variety of workshops, hackathons and lectures for the students of natural sciences and technology. \", _jsx(_components.strong, {\n        children: \"Introduction to Data Science\"\n      }), \", organized by our own Laboratory for Bioinformatics, was this year one of them.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/10/02/intro-to-data-mining-for-life-scientists/\",\n        children: \"Intro to Data Mining for Life Scientists\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The task was to teach and explain basic data mining concepts and techniques in four hours. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bohinj, orange25, retreat</strong></p><h1 class="sc-c67a4900-0 loxVcf">Biolab retreat Februar 2011</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 11, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>From Wednesday, 2nd February 2011, to Saturday, 5th February 2011, we have been on working retreat at <a href="http://en.wikipedia.org/wiki/Lake_Bohinj">Lake Bohinj</a>. The whole <a href="http://www.biolab.si/">Bioinformatics Laboratory</a> of the <a href="http://www.fri.uni-lj.si/">Faculty of Computer and Information technology</a> has temporary moved to a nice house just few meters from the lake, enjoing the nature and without any distractions. Plan: working on the next version of Orange, <a href="http://orange.biolab.si/trac/intertrac/wiki%3AOrange25">Orange 2.5</a> and documentation rewrite. Orange 2.5 will have a better and restructured Python scripting interface along with great and shinny documentation.</p>
 <p>Overall summary of the retreat: first commit (<a href="http://orange.biolab.si/trac/intertrac/changeset%3A9743">revision 9743</a>) by Marko on Wednesday, last commit (<a href="http://orange.biolab.si/trac/intertrac/changeset%3A10181">revision 10181</a>) by Matija at 1:26:49 on Saturday. This gives 439 revisions made during the retreat.</p>
 <p>Some photos to give you a taste how it was.</p>
-<table><thead><tr><th><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0292_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0292_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th><th><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0293_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0293_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th><th><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0360_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0360_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th></tr></thead><tbody><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0364_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0364_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0373_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0373_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0325_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0325_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0327_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0327_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0303_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0303_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0304_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0304_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0306_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0306_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0322_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0322_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0324_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0324_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0338_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0338_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0346_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0346_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a 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src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0379_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0381_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0381_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0382_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0382_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a 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decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0294_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0295_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0295_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0297_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0297_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0388_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0388_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0392_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0392_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0394_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0394_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0396_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0396_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0400_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0400_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0273_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0273_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0285_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0285_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0277_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0277_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0281_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0281_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a 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fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-02-11 13:41:00+00:00","draft":false,"title":"Biolab retreat Februar 2011","blog":["bohinj","orange25","retreat"],"oldUrl":"/blog/2011/02/11/biolab-retreat-februar-2011/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    table: \"table\",\n    thead: \"thead\",\n    tr: \"tr\",\n    th: \"th\",\n    img: \"img\",\n    tbody: \"tbody\",\n    td: \"td\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: 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The whole \", _jsx(_components.a, {\n        href: \"http://www.biolab.si/\",\n        children: \"Bioinformatics Laboratory\"\n      }), \" of the \", _jsx(_components.a, {\n        href: \"http://www.fri.uni-lj.si/\",\n        children: \"Faculty of Computer and Information technology\"\n      }), \" has temporary moved to a nice house just few meters from the lake, enjoing the nature and without any distractions. Plan: working on the next version of Orange, \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/trac/intertrac/wiki%3AOrange25\",\n        children: \"Orange 2.5\"\n      }), \" and documentation rewrite. 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width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0360_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th></tr></thead><tbody><tr><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0364_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0364_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0373_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" 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decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-02-biolab-retreat-februar-2011/__optimized-images__/img_0291_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td></td></tr></tbody></table></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a 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fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-02-11 13:41:00+00:00","draft":false,"title":"Biolab retreat Februar 2011","blog":["bohinj","orange25","retreat"],"oldUrl":"/blog/2011/02/11/biolab-retreat-februar-2011/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    table: \"table\",\n    thead: \"thead\",\n    tr: \"tr\",\n    th: \"th\",\n    img: \"img\",\n    tbody: \"tbody\",\n    td: \"td\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"From Wednesday, 2nd February 2011, to Saturday, 5th February 2011, we have been on working retreat at \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Lake_Bohinj\",\n        children: \"Lake Bohinj\"\n      }), \". The whole \", _jsx(_components.a, {\n        href: \"http://www.biolab.si/\",\n        children: \"Bioinformatics Laboratory\"\n      }), \" of the \", _jsx(_components.a, {\n        href: \"http://www.fri.uni-lj.si/\",\n        children: \"Faculty of Computer and Information technology\"\n      }), \" has temporary moved to a nice house just few meters from the lake, enjoing the nature and without any distractions. Plan: working on the next version of Orange, \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/trac/intertrac/wiki%3AOrange25\",\n        children: \"Orange 2.5\"\n      }), \" and documentation rewrite. 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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Box Plot Alternative: Violin Plot</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Box Plot Alternative: Violin Plot"/><meta property="og:description" content="Violin plot can tell you more than a box plot."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2021-08-05"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="visualization"/><meta property="article:tag" content="violin plot"/><meta property="article:tag" content="box plot"/><meta property="og:image" content="/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Box Plot Alternative: Violin Plot</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Box Plot Alternative: Violin Plot"/><meta property="og:description" content="Violin plot can tell you more than a box plot."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2021-08-05"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="visualization"/><meta property="article:tag" content="violin plot"/><meta property="article:tag" content="box plot"/><meta property="og:image" content="/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -172,4 +172,4 @@
 <a href="/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-outliers.png" data-gallery="true"><img alt="" loading="lazy" width="1466" height="986" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-outliers.png"/></a>
 <a href="/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="323" height="259" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-workflow.png"/></a>
 <p>Happy plotting!</p>
-<p>* Tukey&#x27;s fences represent the first and the third quartile with added 1.5 x interquartile range, which defines the outliers, that is the data instances lying outside the defined range.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-08-05","draft":false,"title":"Box Plot Alternative: Violin Plot","thumbImage":"2021-08-05-violin.png","frontPageImage":"2021-08-05-violin.png","blog":["visualization","violin plot","box plot"],"shortExcerpt":"Violin plot can tell you more than a box plot.","longExcerpt":"Box plots with an upgrade - violin plots are your new favorite visualization!","x2images":true,"oldUrl":"/blog/2021/2021-08-05-violin-plot/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"There's nothing more beautiful than seeing your data in plot. Preferably one that exposes interesting properties of the data or relationships between data instances. We recently wrote about embeddings and dimensionality reduction techniques, but now we are back to basics - whisker plots, means, medians, and distributions.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"blog/2021/2021-06-17-pca-mds-tsne/\",\n        name: \"PCA vs. MDS vs. t-SNE\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Most of you will be familiar with a good ol' box plot, also called whisker plot. The visualization shows essential data properties - mean, median, interquartile range, and variance. We can expose outliers and data irregularities with a quick glance at the box plot. A great thing about box plot is that is also allows us to split the data by a categorical variable, adding another layer of interpretation.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-box-plot-hd.png\",\n      width: \"1844\",\n      height: \"964\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-box-plot-hd.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"But if box plot is so great, why do we need another visualization? Well, there are some properties of the data that box plot cannot reveal. Enter violin plot, a box plot lookalike with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Kernel_density_estimation\",\n        children: \"kernel density estimation\"\n      }), \" (KDE).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Violin plot has most of the properties of a box plot. The white dot in the center of the plot represents the median, the thick black stripe the interquartile range, and the thin black line the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Outlier#Tukey's_fences\",\n        children: \"Tukey's fence\"\n      }), \"*.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-violin-plot.png\",\n      width: \"1954\",\n      height: \"1310\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While violin plot doesn't reveal certain aspects of the data, such as the mean and the exact values statistics, it does reveal something very interesting. Violin plot applies a kernel density estimation over data points, which show the likelihood of data points given a normal distribution.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In other words, where the violin is \\\"fatter\\\", there are more data points in the neighborhood. And where it is \\\"thinner\\\", there are less. Violin plots are thus great for exposing underlying distributions, especially if they are multimodal, which cannot be determined from the box plot.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-violin-plot-points.png\",\n      width: \"1954\",\n      height: \"1310\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin-plot-points.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Consider a simple data set we have painted with Paint Data. It clearly has a bimodal distribution, with the first mean around 0.25 and the second one around 0.7.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-paint-data.png\",\n      width: \"1150\",\n      height: \"200\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-paint-data.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Box plot, however, only shows a single mean, which is around 0.47. It fails to expose the bimodal nature of the data, something that could be very relevant downstream.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-box-plot-pd.png\",\n      width: \"1026\",\n      height: \"306\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-box-plot-pd.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Violin plot to the rescue! 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Keep in mind, though, that these are the outliers only for the specified attribute.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-outliers.png\",\n      width: \"1466\",\n      height: \"986\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-outliers.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-workflow.png\",\n      width: \"323\",\n      height: \"259\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy plotting!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"* Tukey's fences represent the first and the third quartile with added 1.5 x interquartile range, which defines the outliers, that is the data instances lying outside the defined range.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"box-plot-alternative-violin-plot"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>* Tukey&#x27;s fences represent the first and the third quartile with added 1.5 x interquartile range, which defines the outliers, that is the data instances lying outside the defined range.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-08-05","draft":false,"title":"Box Plot Alternative: Violin Plot","thumbImage":"2021-08-05-violin.png","frontPageImage":"2021-08-05-violin.png","blog":["visualization","violin plot","box plot"],"shortExcerpt":"Violin plot can tell you more than a box plot.","longExcerpt":"Box plots with an upgrade - violin plots are your new favorite visualization!","x2images":true,"oldUrl":"/blog/2021/2021-08-05-violin-plot/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"There's nothing more beautiful than seeing your data in plot. 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A great thing about box plot is that is also allows us to split the data by a categorical variable, adding another layer of interpretation.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-box-plot-hd.png\",\n      width: \"1844\",\n      height: \"964\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-box-plot-hd.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"But if box plot is so great, why do we need another visualization? Well, there are some properties of the data that box plot cannot reveal. Enter violin plot, a box plot lookalike with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Kernel_density_estimation\",\n        children: \"kernel density estimation\"\n      }), \" (KDE).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Violin plot has most of the properties of a box plot. The white dot in the center of the plot represents the median, the thick black stripe the interquartile range, and the thin black line the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Outlier#Tukey's_fences\",\n        children: \"Tukey's fence\"\n      }), \"*.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-violin-plot.png\",\n      width: \"1954\",\n      height: \"1310\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While violin plot doesn't reveal certain aspects of the data, such as the mean and the exact values statistics, it does reveal something very interesting. Violin plot applies a kernel density estimation over data points, which show the likelihood of data points given a normal distribution.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In other words, where the violin is \\\"fatter\\\", there are more data points in the neighborhood. And where it is \\\"thinner\\\", there are less. Violin plots are thus great for exposing underlying distributions, especially if they are multimodal, which cannot be determined from the box plot.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-violin-plot-points.png\",\n      width: \"1954\",\n      height: \"1310\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin-plot-points.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Consider a simple data set we have painted with Paint Data. It clearly has a bimodal distribution, with the first mean around 0.25 and the second one around 0.7.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-paint-data.png\",\n      width: \"1150\",\n      height: \"200\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-paint-data.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Box plot, however, only shows a single mean, which is around 0.47. It fails to expose the bimodal nature of the data, something that could be very relevant downstream.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-box-plot-pd.png\",\n      width: \"1026\",\n      height: \"306\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-box-plot-pd.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Violin plot to the rescue! The plot nicely shows two \\\"bellies\\\", exposing a bimodal distribution of the data.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-violin-plot-bimodal.png\",\n      width: \"766\",\n      height: \"850\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin-plot-bimodal.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One final thing. We have mentioned Tukey's fences, which a simple approach for finding the outliers. Data points that lie outside of the thin black line are considered outliers. We can simply select all the instances that fall inside the line and output them. Thus we have removed the outliers. Keep in mind, though, that these are the outliers only for the specified attribute.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-outliers.png\",\n      width: \"1466\",\n      height: \"986\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-outliers.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-08-violin-plot/2021-08-05-workflow.png\",\n      width: \"323\",\n      height: \"259\",\n      src: \"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy plotting!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"* Tukey's fences represent the first and the third quartile with added 1.5 x interquartile range, which defines the outliers, that is the data instances lying outside the defined range.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-08-violin-plot/__optimized-images__/2021-08-05-violin.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"box-plot-alternative-violin-plot"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>history</strong></p><h1 class="sc-c67a4900-0 loxVcf">Brief History of Orange, Praise to Donald Michie</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 09, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://www.informatica.si/">Informatica</a> has recently published <a href="http://www.informatica.si/PDF/37-1/09_Demsar%20-%20Orange%20-%20Data%20Mining%20Fruitful%20and%20Fun%20-%20A%20Historical%20Perspective.pdf">our paper on the history of Orange</a>. The paper is a post-publication from a <a href="http://videolectures.net/is2012_turingslais2012/">Conference on 100 Years of Alan Turing and 20 Years of Slovene AI Society</a>, where Janez Demšar gave a <a href="http://videolectures.net/is2012_demsar_orange/">talk</a> on the topics.</p>
 <p>History of Orange goes all the way back to 1997, when late <a href="http://en.wikipedia.org/wiki/Donald_Michie">Donald Michie</a> had an idea that machine learning needs an open toolbox for machine learning. To spark the development, we co-organized WebLab97 at beautiful <a href="http://en.wikipedia.org/wiki/Bled">Bled, Slovenia</a>. Workshop&#x27;s name reflected Michie&#x27;s idea that tool should be a web application where people can submit data mining code, procedures, testing scripts, and data and share them in the joint web workspace.</p>
 <p>Donald Michie, a pioneer of Artificial Intelligence, was always ahead of time. (Check out <a href="http://videolectures.net/is2012_bratko_ai_slovenia/">a great talk by Ivan Bratko</a> on their friendship and adventures in chess and machine learning). At WebLab97, Michie was actually very, very ahead of time. But despite the presence of IBM&#x27;s Java team that could guide us in developments of the toolbox, the technology was not ripe and initiative of WebLab was gone as the conference ended. But, at least for us, the idea sparked interest of Janez and myself, and development of what is now Orange begun shortly after.</p>
 <p>Our paper gives brief account of Orange&#x27;s history and its developments since WebLab97. For reasons of brevity it does not mention that prior to Qt we have experimented with other GUI platforms. Prior to Qt, we laid our hopes to <a href="http://pmw.sourceforge.net/">Pwm Python megawidgets</a>, a library that helped us to construct the first Orange graphical user interface. The GUI part of Orange was called Orange*First. Its screenshot shows a tab for interactive discretisation, thanks to <a href="http://www.uthouston.edu/sbmi/faculty-and-staff/noriaki-aoki.htm">Noriaki Aoki</a> who then proposed that this kind of visualisation should be useful in medical data analysis:</p>
 <a href="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/orange-first_1.png__600x422_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="600" height="422" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/orange-first_1.png__600x422_q95_crop_upscale.png"/></a>
 <p>PS Somehow, I have lost a latex file with a WebLab97 program. It should be on some backup tape, somewhere. The following scan of the first page (and a <a href="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/weblab97.pdf">weblab97.pdf</a>), left in some PPT presentation, is all that I can retrieve. The program of the second day is missing, with keynotes from Tom Mitchell, and much talk about then already a success story of R.</p>
-<a href="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/weblab97_1.jpg__600x794_q95_crop_upscale.jpg" data-gallery="true"><img alt="" loading="lazy" width="600" height="794" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/weblab97_1.jpg__600x794_q95_crop_upscale.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2013-10-09 06:33:00+00:00","draft":false,"title":"Brief History of Orange, Praise to Donald Michie","blog":["history"],"oldUrl":"/blog/2013/10/09/brief-history-of-orange-praise-to-donald-michie/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.informatica.si/\",\n        children: \"Informatica\"\n      }), \" has recently published \", _jsx(_components.a, {\n        href: \"http://www.informatica.si/PDF/37-1/09_Demsar%20-%20Orange%20-%20Data%20Mining%20Fruitful%20and%20Fun%20-%20A%20Historical%20Perspective.pdf\",\n        children: \"our paper on the history of Orange\"\n      }), \". The paper is a post-publication from a \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_turingslais2012/\",\n        children: \"Conference on 100 Years of Alan Turing and 20 Years of Slovene AI Society\"\n      }), \", where Janez Demšar gave a \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_demsar_orange/\",\n        children: \"talk\"\n      }), \" on the topics.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"History of Orange goes all the way back to 1997, when late \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Donald_Michie\",\n        children: \"Donald Michie\"\n      }), \" had an idea that machine learning needs an open toolbox for machine learning. To spark the development, we co-organized WebLab97 at beautiful \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Bled\",\n        children: \"Bled, Slovenia\"\n      }), \". Workshop's name reflected Michie's idea that tool should be a web application where people can submit data mining code, procedures, testing scripts, and data and share them in the joint web workspace.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Donald Michie, a pioneer of Artificial Intelligence, was always ahead of time. (Check out \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_bratko_ai_slovenia/\",\n        children: \"a great talk by Ivan Bratko\"\n      }), \" on their friendship and adventures in chess and machine learning). At WebLab97, Michie was actually very, very ahead of time. But despite the presence of IBM's Java team that could guide us in developments of the toolbox, the technology was not ripe and initiative of WebLab was gone as the conference ended. But, at least for us, the idea sparked interest of Janez and myself, and development of what is now Orange begun shortly after.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Our paper gives brief account of Orange's history and its developments since WebLab97. For reasons of brevity it does not mention that prior to Qt we have experimented with other GUI platforms. Prior to Qt, we laid our hopes to \", _jsx(_components.a, {\n        href: \"http://pmw.sourceforge.net/\",\n        children: \"Pwm Python megawidgets\"\n      }), \", a library that helped us to construct the first Orange graphical user interface. The GUI part of Orange was called Orange*First. Its screenshot shows a tab for interactive discretisation, thanks to \", _jsx(_components.a, {\n        href: \"http://www.uthouston.edu/sbmi/faculty-and-staff/noriaki-aoki.htm\",\n        children: \"Noriaki Aoki\"\n      }), \" who then proposed that this kind of visualisation should be useful in medical data analysis:\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/orange-first_1.png__600x422_q95_crop_upscale.png\",\n      alt: \"\",\n      width: \"600\",\n      height: \"422\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"PS Somehow, I have lost a latex file with a WebLab97 program. It should be on some backup tape, somewhere. The following scan of the first page (and a \", _jsx(_components.a, {\n        href: \"/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/weblab97.pdf\",\n        children: \"weblab97.pdf\"\n      }), \"), left in some PPT presentation, is all that I can retrieve. The program of the second day is missing, with keynotes from Tom Mitchell, and much talk about then already a success story of R.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/weblab97_1.jpg__600x794_q95_crop_upscale.jpg\",\n      alt: \"\",\n      width: \"600\",\n      height: \"794\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"brief-history-of-orange-praise-to-donald-michie"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<a href="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/weblab97_1.jpg__600x794_q95_crop_upscale.jpg" data-gallery="true"><img alt="" loading="lazy" width="600" height="794" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/weblab97_1.jpg__600x794_q95_crop_upscale.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2013-10-09 06:33:00+00:00","draft":false,"title":"Brief History of Orange, Praise to Donald Michie","blog":["history"],"oldUrl":"/blog/2013/10/09/brief-history-of-orange-praise-to-donald-michie/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.informatica.si/\",\n        children: \"Informatica\"\n      }), \" has recently published \", _jsx(_components.a, {\n        href: \"http://www.informatica.si/PDF/37-1/09_Demsar%20-%20Orange%20-%20Data%20Mining%20Fruitful%20and%20Fun%20-%20A%20Historical%20Perspective.pdf\",\n        children: \"our paper on the history of Orange\"\n      }), \". The paper is a post-publication from a \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_turingslais2012/\",\n        children: \"Conference on 100 Years of Alan Turing and 20 Years of Slovene AI Society\"\n      }), \", where Janez Demšar gave a \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_demsar_orange/\",\n        children: \"talk\"\n      }), \" on the topics.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"History of Orange goes all the way back to 1997, when late \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Donald_Michie\",\n        children: \"Donald Michie\"\n      }), \" had an idea that machine learning needs an open toolbox for machine learning. To spark the development, we co-organized WebLab97 at beautiful \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Bled\",\n        children: \"Bled, Slovenia\"\n      }), \". Workshop's name reflected Michie's idea that tool should be a web application where people can submit data mining code, procedures, testing scripts, and data and share them in the joint web workspace.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Donald Michie, a pioneer of Artificial Intelligence, was always ahead of time. (Check out \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_bratko_ai_slovenia/\",\n        children: \"a great talk by Ivan Bratko\"\n      }), \" on their friendship and adventures in chess and machine learning). At WebLab97, Michie was actually very, very ahead of time. But despite the presence of IBM's Java team that could guide us in developments of the toolbox, the technology was not ripe and initiative of WebLab was gone as the conference ended. But, at least for us, the idea sparked interest of Janez and myself, and development of what is now Orange begun shortly after.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Our paper gives brief account of Orange's history and its developments since WebLab97. For reasons of brevity it does not mention that prior to Qt we have experimented with other GUI platforms. Prior to Qt, we laid our hopes to \", _jsx(_components.a, {\n        href: \"http://pmw.sourceforge.net/\",\n        children: \"Pwm Python megawidgets\"\n      }), \", a library that helped us to construct the first Orange graphical user interface. The GUI part of Orange was called Orange*First. Its screenshot shows a tab for interactive discretisation, thanks to \", _jsx(_components.a, {\n        href: \"http://www.uthouston.edu/sbmi/faculty-and-staff/noriaki-aoki.htm\",\n        children: \"Noriaki Aoki\"\n      }), \" who then proposed that this kind of visualisation should be useful in medical data analysis:\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2013-10-brief-history-of-orange-praise-to-donald-michie/__optimized-images__/orange-first_1.png__600x422_q95_crop_upscale.png\",\n      alt: \"\",\n      width: \"600\",\n      height: \"422\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"PS Somehow, I have lost a latex file with a WebLab97 program. It should be on some backup tape, somewhere. 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diff --git a/blog/business-case-studies-with-orange/index.html b/blog/business-case-studies-with-orange/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>business intelligence, HR, logistic regression, nomogram, predictive models</strong></p><h1 class="sc-c67a4900-0 loxVcf">Business Case Studies with Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">May 18, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Previous week Blaž, Robert and I visited Wärtsilä in the lovely Dolina near Trieste, Italy. <a href="https://www.wartsila.com/" target="_blank" rel="noreferrer">Wärtsilä</a> is one of the leading designers of lifecycle power solutions for the global marine and energy markets and its <a href="https://www.wartsila.com/ita/en/about" target="_blank" rel="noreferrer">subsidiary in Trieste</a> is one of the largest Wärtsilä Group engine production plants. We were there to hold a one-day workshop on data mining and machine learning with the aim to identify relevant use cases in business and show how to address them.</p>
 <p>\</p>
 <a href="/blog/2019-5-business-case-studies-with-orange/__optimized-images__/business-case-1.png" data-gallery="true"><img alt="" loading="lazy" width="640" height="303" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-5-business-case-studies-with-orange/__optimized-images__/business-case-1.png"/></a>
@@ -197,4 +197,4 @@
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 <p><br/>
 <!-- -->\</p>
-<p>This is something HR department can work with to design proper policies and keep best talent. The same workflow can be used for churn prediction, process optimization and predicting success of a new product.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-05-18","draft":false,"title":"Business Case Studies with Orange","thumbImage":"wartsila-blog.jpg","blog":["business intelligence","HR","logistic regression","nomogram","predictive models"],"shortExcerpt":"At the latest workshop we demonstrated how to predict, which employees are most likely to resign in the future.","longExcerpt":"At the latest workshop in Italy we taught the participants how to identify relevant business use cases and how to predict, which employees are most likely to resign in the future.","oldUrl":"/blog/2019/5/18/business-case-studies-with-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    br: \"br\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Previous week Blaž, Robert and I visited Wärtsilä in the lovely Dolina near Trieste, Italy. \", _jsx(LinkNew, {\n        url: \"https://www.wartsila.com/\",\n        name: \"Wärtsilä\"\n      }), \" is one of the leading designers of lifecycle power solutions for the global marine and energy markets and its \", _jsx(LinkNew, {\n        url: \"https://www.wartsila.com/ita/en/about\",\n        name: \"subsidiary in Trieste\"\n      }), \" is one of the largest Wärtsilä Group engine production plants. 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\ No newline at end of file
+<p>This is something HR department can work with to design proper policies and keep best talent. The same workflow can be used for churn prediction, process optimization and predicting success of a new product.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-05-18","draft":false,"title":"Business Case Studies with Orange","thumbImage":"wartsila-blog.jpg","blog":["business intelligence","HR","logistic regression","nomogram","predictive models"],"shortExcerpt":"At the latest workshop we demonstrated how to predict, which employees are most likely to resign in the future.","longExcerpt":"At the latest workshop in Italy we taught the participants how to identify relevant business use cases and how to predict, which employees are most likely to resign in the future.","oldUrl":"/blog/2019/5/18/business-case-studies-with-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    br: \"br\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Previous week Blaž, Robert and I visited Wärtsilä in the lovely Dolina near Trieste, Italy. \", _jsx(LinkNew, {\n        url: \"https://www.wartsila.com/\",\n        name: \"Wärtsilä\"\n      }), \" is one of the leading designers of lifecycle power solutions for the global marine and energy markets and its \", _jsx(LinkNew, {\n        url: \"https://www.wartsila.com/ita/en/about\",\n        name: \"subsidiary in Trieste\"\n      }), \" is one of the largest Wärtsilä Group engine production plants. We were there to hold a one-day workshop on data mining and machine learning with the aim to identify relevant use cases in business and show how to address them.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-5-business-case-studies-with-orange/business-case-1.png\",\n      width: \"640\",\n      height: \"303\",\n      src: \"/blog/2019-5-business-case-studies-with-orange/__optimized-images__/business-case-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2017/11/17/data-mining-business-public-administration/\",\n        name: \"Data Mining for Business and Public Administration\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One such important use case is employee attrition. It is vital for any company to retain its most valuable workers, so they must learn how to identify dissatisfied employees and provide incentive from the to stay. It is easy to construct a workflow in Orange that helps us with this.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, let us load Attrition – Train data set from the Datasets widget. This is a synthetic data set from IBM Watson that has 1470 instances (employees) and 18 features describing them. Our target variable is Attrition, where Yes means the person left the company and No means it stayed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-5-business-case-studies-with-orange/business-case-2.png\",\n      src: \"70%\",\n      width: \"1580\",\n      height: \"876\",\n      src: \"/blog/2019-5-business-case-studies-with-orange/__optimized-images__/business-case-2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now our goal is to construct a predictive model that will successfully predict the likelihood of a person leaving. Let us connect a couple of classifiers and the data set to Test and Score and see which model performs best.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-5-business-case-studies-with-orange/business-case-3.png\",\n      src: \"60%\",\n      width: \"762\",\n      height: \"584\",\n      src: \"/blog/2019-5-business-case-studies-with-orange/__optimized-images__/business-case-3.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", _jsx(_components.br, {}), \"\\n\", \"Seems like Logistic Regression is the winner here, since its AUC score it the highest of the three.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-5-business-case-studies-with-orange/business-case-4.png\",\n      src: \"80%\",\n      width: \"1398\",\n      height: \"866\",\n      src: \"/blog/2019-5-business-case-studies-with-orange/__optimized-images__/business-case-4.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", _jsx(_components.br, {}), \"\\n\", \"A great thing about Logistic Regression is that it is interpretable. 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index d5e4b1264..feb5c44c4 100644
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Can We Download Orange Faster?</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Can We Download Orange Faster?"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-08-28 09:16:48+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="download"/><meta property="article:tag" content="orange3"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, download, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Can We Download Orange Faster?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 28, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>One day Blaž and Janez came to us and started complaining how slow Orange download is in the US. Since they hold a large course at Baylor College of Medicine every year, this causes some frustration.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2/">Introduction to Data Mining Course in Houston</a></p>
 <p>But we have the data and we&#x27;ve promptly tried to confirm their complaints by analyzing them... well, in Orange!</p>
@@ -175,4 +175,4 @@
 <p>And the longest time someone was prepared to wait for the download? Over 3 hours. Kudos, mate! We appreciate it! 🙌</p>
 <a href="/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-28-at-10.26.01-1.png" data-gallery="true"><img alt="" loading="lazy" width="708" height="386" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-28-at-10.26.01-1.png"/></a>
 <p>This simple workflow is all it took to do our analysis.</p>
-<p>So how is your download speed for Orange compared to other things you are downloading? Better, worse? We&#x27;re keen to hear it! 👂</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-08-28 09:16:48+00:00","draft":false,"title":"Can We Download Orange Faster?","blog":["analysis","download","orange3"],"oldUrl":"/blog/2017/08/28/can-we-download-orange-faster/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One day Blaž and Janez came to us and started complaining how slow Orange download is in the US. Since they hold a large course at Baylor College of Medicine every year, this causes some frustration.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/09/15/data-mining-in-houston-2/\",\n        children: \"Introduction to Data Mining Course in Houston\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But we have the data and we've promptly tried to confirm their complaints by analyzing them... well, in Orange!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, let us observe the data. We have 4887 recorded download sessions with one meta feature reporting on the country of the download and four features with time, size, speed in bytes and speed in gigabytes of the download.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-13.47.51.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"898\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data of Orange download statistics. We get reports on the country of download, the size and the time of the download. We have constructed speed and size in gigabytes ourselves with simple formulae.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now let us check the validity of Blaž's and Janez's complaint. We will use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-geo\",\n        children: \"orange3-geo add-on\"\n      }), \" for plotting geolocated data. For any geoplotting, we need coordinates - latitude and longitude. To retrieve them automatically, we will use Geocoding widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-13.57.15.png\",\n      alt: \"\",\n      width: \"820\",\n      height: \"614\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We instruct Geocoding to retrieve coordinates from our Country feature. Identifier type tells the widget in what format the region name appears.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We told the widget to use the ISO-compliant country code from Country attribute and encode it into coordinates. If we check the new data in a Data Table, we see our data is enhanced with new features.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.03.08.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"645\"\n      }), \"\\nEnhanced data table. Besides latitude and longitude, Geocoding can also append country-level data (economy, continent, region...).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we have coordinates, we can plot these data regionally - in Choropleth widget! This widget plots data on three levels - country, state/region and county/municipality. Levels correspond to the administrative division of each country.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.10.53.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"882\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Choropleth widget offers 3 aggregation levels. We chose country (e.g. administrative level 0), but with a more detailed data one could also plot by state/county/municipality. Administrative levels are different for each country (e.g. Bundesländer for Germany, states for the US, provinces for Canada...).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the plot above, we have simply displayed the amount of people (Count) that downloaded Orange in the past couple of months. Seems like we indeed have most users in the US, so it might make sense to solve installation issues for this region first.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now let us check the speed of the download - it is really so slow in the US? If we take the mean, we can see that Slovenia is far ahead of the rest as far as download speed is concerned. No wonder - we are downloading via the local network. Scandinavia, Central Europe and a part of the Balkans seem to do quite ok as well.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.21.50.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"987\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Aggregation by mean.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But mean sometimes doesn't show the right picture - it is sensitive to outliers, which would be the case of Slovenia here. Let us try median instead. Looks like 50% of American download at speed lower than 1.5MB/s. Quite average, but it could be better.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.36.54.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"987\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Aggregation by median.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And the longest time someone was prepared to wait for the download? Over 3 hours. Kudos, mate! 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+<p>So how is your download speed for Orange compared to other things you are downloading? Better, worse? We&#x27;re keen to hear it! 👂</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-08-28 09:16:48+00:00","draft":false,"title":"Can We Download Orange Faster?","blog":["analysis","download","orange3"],"oldUrl":"/blog/2017/08/28/can-we-download-orange-faster/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One day Blaž and Janez came to us and started complaining how slow Orange download is in the US. Since they hold a large course at Baylor College of Medicine every year, this causes some frustration.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/09/15/data-mining-in-houston-2/\",\n        children: \"Introduction to Data Mining Course in Houston\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But we have the data and we've promptly tried to confirm their complaints by analyzing them... well, in Orange!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, let us observe the data. We have 4887 recorded download sessions with one meta feature reporting on the country of the download and four features with time, size, speed in bytes and speed in gigabytes of the download.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-13.47.51.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"898\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data of Orange download statistics. We get reports on the country of download, the size and the time of the download. We have constructed speed and size in gigabytes ourselves with simple formulae.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now let us check the validity of Blaž's and Janez's complaint. We will use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-geo\",\n        children: \"orange3-geo add-on\"\n      }), \" for plotting geolocated data. For any geoplotting, we need coordinates - latitude and longitude. To retrieve them automatically, we will use Geocoding widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-13.57.15.png\",\n      alt: \"\",\n      width: \"820\",\n      height: \"614\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We instruct Geocoding to retrieve coordinates from our Country feature. Identifier type tells the widget in what format the region name appears.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We told the widget to use the ISO-compliant country code from Country attribute and encode it into coordinates. If we check the new data in a Data Table, we see our data is enhanced with new features.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.03.08.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"645\"\n      }), \"\\nEnhanced data table. Besides latitude and longitude, Geocoding can also append country-level data (economy, continent, region...).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we have coordinates, we can plot these data regionally - in Choropleth widget! This widget plots data on three levels - country, state/region and county/municipality. Levels correspond to the administrative division of each country.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.10.53.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"882\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Choropleth widget offers 3 aggregation levels. We chose country (e.g. administrative level 0), but with a more detailed data one could also plot by state/county/municipality. Administrative levels are different for each country (e.g. Bundesländer for Germany, states for the US, provinces for Canada...).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the plot above, we have simply displayed the amount of people (Count) that downloaded Orange in the past couple of months. Seems like we indeed have most users in the US, so it might make sense to solve installation issues for this region first.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now let us check the speed of the download - it is really so slow in the US? If we take the mean, we can see that Slovenia is far ahead of the rest as far as download speed is concerned. No wonder - we are downloading via the local network. Scandinavia, Central Europe and a part of the Balkans seem to do quite ok as well.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.21.50.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"987\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Aggregation by mean.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But mean sometimes doesn't show the right picture - it is sensitive to outliers, which would be the case of Slovenia here. Let us try median instead. Looks like 50% of American download at speed lower than 1.5MB/s. Quite average, but it could be better.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-25-at-14.36.54.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"987\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Aggregation by median.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And the longest time someone was prepared to wait for the download? Over 3 hours. Kudos, mate! We appreciate it! 🙌\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-can-we-download-orange-faster/__optimized-images__/Screen-Shot-2017-08-28-at-10.26.01-1.png\",\n      alt: \"\",\n      width: \"708\",\n      height: \"386\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This simple workflow is all it took to do our analysis.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So how is your download speed for Orange compared to other things you are downloading? Better, worse? We're keen to hear it! 👂\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"can-we-download-orange-faster"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Celebrity Lookalike or How to Make Students Love Machine Learning</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Celebrity Lookalike or How to Make Students Love Machine Learning"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-11-25 14:55:39+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="education"/><meta property="article:tag" content="images"/><meta property="article:tag" content="interactive data visualization"/><meta property="article:tag" content="orange3"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, images, interactive data visualization, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Celebrity Lookalike or How to Make Students Love Machine Learning</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 25, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Recently we&#x27;ve been participating at <a href="http://www.tms.si/index.php?e_id=484&amp;lang=2">Days of Computer Science</a>, organized by the Museum of Post and Telecommunications and the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. The project brought together pupils and students from around the country and hopefully showed them what computer science is mostly about. Most children would think programming is just typing lines of code. But it&#x27;s more than that. It&#x27;s a way of thinking, a way to solve problems creatively and efficiently. And even better, computer science can be used for solving a great variety of problems.</p>
 <p><strong>Related:</strong> <a href="/features/education-in-data-science/">On teaching data science with Orange</a></p>
 <p>Orange team has prepared a small demo project called <em>Celebrity Lookalike</em>. We found 65 celebrity photos online and loaded them in <a href="http://orange.biolab.si/">Orange</a>. Next we cropped photos to faces and turned them black and white, to avoid bias in background and color. Next we inferred embeddings with <a href="http://image-net.org/">ImageNet</a> widget and got 2048 features, which are the penultimate result of the ImageNet neural network.</p>
@@ -162,4 +162,4 @@
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 <p>Hopefully this inspires a new generation of students to become scientists, researchers and to actively find solutions to their problems. Coding or not. :)</p>
 <a href="/blog/2016-11-celebrity-lookalike-or-how-to-make-students-love-machine-learning/__optimized-images__/DSC_4982.jpg" data-gallery="true"><img alt="" loading="lazy" width="931" height="607" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-11-celebrity-lookalike-or-how-to-make-students-love-machine-learning/__optimized-images__/DSC_4982.jpg"/></a>
-<p>_Note: _<em>Most widgets we have designed for this projects (like Face Detector, Webcam Capture, and Lookalike) are available in <a href="https://github.com/biolab/orange3-prototypes">Orange3-Prototypes</a> and are not actively maintained. They can, however, be used for personal projects and sheer fun. Orange does not own the copyright of the images.</em></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-11-25 14:55:39+00:00","draft":false,"title":"Celebrity Lookalike or How to Make Students Love Machine Learning","blog":["education","images","interactive data visualization","orange3"],"oldUrl":"/blog/2016/11/25/celebrity-lookalike-or-how-to-make-students-love-machine-learning/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Recently we've been participating at \", _jsx(_components.a, {\n        href: \"http://www.tms.si/index.php?e_id=484\u0026lang=2\",\n        children: \"Days of Computer Science\"\n      }), \", organized by the Museum of Post and Telecommunications and the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. 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+<p>_Note: _<em>Most widgets we have designed for this projects (like Face Detector, Webcam Capture, and Lookalike) are available in <a href="https://github.com/biolab/orange3-prototypes">Orange3-Prototypes</a> and are not actively maintained. They can, however, be used for personal projects and sheer fun. Orange does not own the copyright of the images.</em></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-11-25 14:55:39+00:00","draft":false,"title":"Celebrity Lookalike or How to Make Students Love Machine Learning","blog":["education","images","interactive data visualization","orange3"],"oldUrl":"/blog/2016/11/25/celebrity-lookalike-or-how-to-make-students-love-machine-learning/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Recently we've been participating at \", _jsx(_components.a, {\n        href: \"http://www.tms.si/index.php?e_id=484\u0026lang=2\",\n        children: \"Days of Computer Science\"\n      }), \", organized by the Museum of Post and Telecommunications and the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, explanation, box plot</strong></p><h1 class="sc-c67a4900-0 loxVcf">Characterizing Clusters with a Box Plot</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 21, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>There are many ways to cluster the data in Orange. <a href="https://orangedatamining.com/widget-catalog/unsupervised/hierarchicalclustering/">Hiearchical clustering</a>, <a href="https://orangedatamining.com/widget-catalog/unsupervised/kmeans/">k-means</a>, and <a href="https://orangedatamining.com/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a> are just few of the widgets we can use to find groups of data instances with similar values of attributes. Once we infer the clusters, we need to analyze them to determine their characterizing features. It is there that actually the fun begins.</p>
 <a href="/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="1348" height="1328" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-workflow.png"/></a>
 <p>Out of many ways for cluster analysis, perhaps the simplest one is by using the Box Plot. Consider the following example on the employee attrition data set. I have used <a href="https://orangedatamining.com/widget-catalog/unsupervised/tsne/">t-SNE</a> to observe that that this data perhaps contains about five clusters. I have selected the data instances from the rightmost cluster, for which I would like to know which features are those that  separate this cluster from everything else. To do so, I use the <a href="https://orangedatamining.com/widget-catalog/visualize/boxplot/">Box Plot</a>. But note: by default, Orange will wire the connections from t-SNE to Box Plot so as to communicate only the selected data instances. Instead, we would like to send all the data to the Box Plot, but include the column called Selected as an selection indicator. Selected data instances will have this feature set to Yes, and all other to No. To rewire the connections between t-SNE to Box Plot appropriately, we need to double click the link and between these two widgets and set it so that entire data is send to the Box Plot.</p>
@@ -160,4 +160,4 @@
 <a href="/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-boxplot.png" data-gallery="true"><img alt="" loading="lazy" width="1016" height="530" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-boxplot.png"/></a>
 <p>Placing the t-SNE and the Box Plot widget side-by-side, we can now characterize other clusters. The small one on the top contains only managers. And there is a cluster of employees in human resource department, and researcher, and some other managers.</p>
 <a href="/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-side-by-side.png" data-gallery="true"><img alt="" loading="lazy" width="1274" height="712" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-side-by-side.png"/></a>
-<p>We could also use the Box Plot to report on the differences between two selected clusters. In t-SNE, as in any point-based visualization in Orange, we mark different clusters by selecting points with a shift-modifier. We also need to rewire our workflow to, this time, send only the selected data instances to the box plot. And the subgrouping feature would now need to be the cluster indicator, that is, a feature called Group. But let us leave these details and examples to some other blog.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2021-10-21","draft":false,"title":"Characterizing Clusters with a Box Plot","thumbImage":"2021-10-21-cluster-explain.png","frontPageImage":"2021-10-21-cluster-explain.png","blog":["clustering","explanation","box plot"],"shortExcerpt":"Box Plot widget offers a simple means for explaining clusters.","longExcerpt":"Box Plot widget offers a simple means for explaining clusters.","x2images":true,"oldUrl":"/blog/2021/2021-10-21-cluster-explanation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"There are many ways to cluster the data in Orange. \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/hierarchicalclustering/\",\n        children: \"Hiearchical clustering\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/kmeans/\",\n        children: \"k-means\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/DBSCAN/\",\n        children: \"DBSCAN\"\n      }), \" are just few of the widgets we can use to find groups of data instances with similar values of attributes. 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+<p>We could also use the Box Plot to report on the differences between two selected clusters. In t-SNE, as in any point-based visualization in Orange, we mark different clusters by selecting points with a shift-modifier. We also need to rewire our workflow to, this time, send only the selected data instances to the box plot. And the subgrouping feature would now need to be the cluster indicator, that is, a feature called Group. But let us leave these details and examples to some other blog.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2021-10-21","draft":false,"title":"Characterizing Clusters with a Box Plot","thumbImage":"2021-10-21-cluster-explain.png","frontPageImage":"2021-10-21-cluster-explain.png","blog":["clustering","explanation","box plot"],"shortExcerpt":"Box Plot widget offers a simple means for explaining clusters.","longExcerpt":"Box Plot widget offers a simple means for explaining clusters.","x2images":true,"oldUrl":"/blog/2021/2021-10-21-cluster-explanation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"There are many ways to cluster the data in Orange. \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/hierarchicalclustering/\",\n        children: \"Hiearchical clustering\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/kmeans/\",\n        children: \"k-means\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/DBSCAN/\",\n        children: \"DBSCAN\"\n      }), \" are just few of the widgets we can use to find groups of data instances with similar values of attributes. 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Very interesting, the cluster we selected contains people that work in sales! That's a great explanation. Also, the Department, the next-ranked feature, tells us that the cluster almost exclusively includes the employees from the Sales Department. These employees were primarily educated marketing, as reported by next-ranked feature EducationField.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-10-cluster-explanation/2021-10-21-cluster-explain-boxplot.png\",\n      width: \"1016\",\n      height: \"530\",\n      src: \"/blog/2021-10-cluster-explanation/__optimized-images__/2021-10-21-cluster-explain-boxplot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Placing the t-SNE and the Box Plot widget side-by-side, we can now characterize other clusters. The small one on the top contains only managers. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Classifying instances with Orange in Python</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Classifying instances with Orange in Python"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-08-14 12:31:57+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="data"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="python"/><meta property="article:tag" content="tree"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, data, examples, orange3, python, tree</strong></p><h1 class="sc-c67a4900-0 loxVcf">Classifying instances with Orange in Python</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 14, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Last week we showed you how to create your own data table in Python shell. Now we’re going to take you a step further and show you how to easily <strong>classify data</strong> with Orange.</p>
 <p>First we’re going to create a <a href="/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/">new data table</a> with 10 fruits as our instances.</p>
 <pre><code>    import Orange
@@ -192,4 +192,4 @@
 <p>Final result:</p>
 <pre><code>    &quot;apple&quot;
 </code></pre>
-<p>You can use your own data set to see how this model works for different data types. Let us know how it goes! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-08-14 12:31:57+00:00","draft":false,"title":"Classifying instances with Orange in Python","blog":["classification","data","examples","orange3","python","tree"],"oldUrl":"/blog/2015/08/14/classifying-instances-with-orange-in-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week we showed you how to create your own data table in Python shell. Now we’re going to take you a step further and show you how to easily \", _jsx(_components.strong, {\n        children: \"classify data\"\n      }), \" with Orange.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First we’re going to create a \", _jsx(_components.a, {\n        href: \"/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/\",\n        children: \"new data table\"\n      }), \" with 10 fruits as our instances.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    from Orange.data import *\\n    \\n    color = DiscreteVariable(\\\"color\\\", values=[\\\"orange\\\", \\\"green\\\", \\\"yellow\\\"])calories = ContinuousVariable(\\\"calories\\\")\\n    fiber = ContinuousVariable(\\\"fiber\\\")\\n    fruit = DiscreteVariable(\\\"fruit\\\", values=[\\\"orange\\\", \\\"apple\\\", \\\"peach\\\"])\\n    \\n    domain = Domain([color, calories, fiber], class_vars=fruit)\\n    \\n    data=Table(domain, [\u003c/span\u003e\\n    [\\\"green\\\", 4, 1.2, \\\"apple\\\"], \\n    [\\\"orange\\\", 5, 1.1, \\\"orange\\\"],\\n    [\\\"yellow\\\", 4, 1.0, \\\"peach\\\"],\\n    [\\\"orange\\\", 4, 1.1, \\\"orange\\\"],\\n    [\\\"yellow\\\", 4, 1.1,\\\"peach\\\"],\\n    [\\\"green\\\", 5, 1.3, \\\"apple\\\"],\\n    [\\\"green\\\", 4, 1.3, \\\"apple\\\"],\\n    [\\\"orange\\\", 5, 1.0, \\\"orange\\\"],\\n    [\\\"yellow\\\", 4.5, 1.3, \\\"peach\\\"],\\n    [\\\"green\\\", 5, 1.0, \\\"orange\\\"]])\\n    \\n    print(data)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we have to select a model for classification. Among the many learners in Orange library, we decided to use the \", _jsx(_components.strong, {\n        children: \"Tree Learner\"\n      }), \" for this example. Since we’re dealing with fruits, we thought it’s only appropriate. :)\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let’s create a learning algorithm and use it to induce the classifier from the data.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    tree_learner = Orange.classification.TreeLearner()\\n    tree = tree_learner(data)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we can predict what variety \", _jsx(_components.strong, {\n        children: \"a green fruit\"\n      }), \" with \", _jsx(_components.strong, {\n        children: \"3.5 calories\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"2g of fiber\"\n      }), \" is with the help of our model. To do this, simply call the model and use a list of new data as argument.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print(tree([\\\"green\\\", 3.5, 2]))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Python returns index as a result:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    1\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To check the index, we can call \", _jsx(_components.strong, {\n        children: \"class variable\"\n      }), \" values with the corresponding index:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    domain.class_var.values[1]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Final result:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \\\"apple\\\"\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can use your own data set to see how this model works for different data types. 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+<p>You can use your own data set to see how this model works for different data types. Let us know how it goes! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-08-14 12:31:57+00:00","draft":false,"title":"Classifying instances with Orange in Python","blog":["classification","data","examples","orange3","python","tree"],"oldUrl":"/blog/2015/08/14/classifying-instances-with-orange-in-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week we showed you how to create your own data table in Python shell. Now we’re going to take you a step further and show you how to easily \", _jsx(_components.strong, {\n        children: \"classify data\"\n      }), \" with Orange.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First we’re going to create a \", _jsx(_components.a, {\n        href: \"/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/\",\n        children: \"new data table\"\n      }), \" with 10 fruits as our instances.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    from Orange.data import *\\n    \\n    color = DiscreteVariable(\\\"color\\\", values=[\\\"orange\\\", \\\"green\\\", \\\"yellow\\\"])calories = ContinuousVariable(\\\"calories\\\")\\n    fiber = ContinuousVariable(\\\"fiber\\\")\\n    fruit = DiscreteVariable(\\\"fruit\\\", values=[\\\"orange\\\", \\\"apple\\\", \\\"peach\\\"])\\n    \\n    domain = Domain([color, calories, fiber], class_vars=fruit)\\n    \\n    data=Table(domain, [\u003c/span\u003e\\n    [\\\"green\\\", 4, 1.2, \\\"apple\\\"], \\n    [\\\"orange\\\", 5, 1.1, \\\"orange\\\"],\\n    [\\\"yellow\\\", 4, 1.0, \\\"peach\\\"],\\n    [\\\"orange\\\", 4, 1.1, \\\"orange\\\"],\\n    [\\\"yellow\\\", 4, 1.1,\\\"peach\\\"],\\n    [\\\"green\\\", 5, 1.3, \\\"apple\\\"],\\n    [\\\"green\\\", 4, 1.3, \\\"apple\\\"],\\n    [\\\"orange\\\", 5, 1.0, \\\"orange\\\"],\\n    [\\\"yellow\\\", 4.5, 1.3, \\\"peach\\\"],\\n    [\\\"green\\\", 5, 1.0, \\\"orange\\\"]])\\n    \\n    print(data)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we have to select a model for classification. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, image analytics, orange3, tutorial, youtube</strong></p><h1 class="sc-c67a4900-0 loxVcf">Clustering of Monet and Manet</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">May 09, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Ever had a hard time telling the difference between Claude Monet and Édouard Manet? Orange can help you cluster these two authors and even more, discover which of Monet&#x27;s masterpiece is indeed very similar to Manet&#x27;s! Use Image Analytics add-on and play with it. Here&#x27;s how:</p>
-<div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/R5uchDa_ba4" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-09 08:19:34+00:00","draft":false,"title":"Clustering of Monet and Manet","blog":["addons","image analytics","orange3","tutorial","youtube"],"oldUrl":"/blog/2018/05/09/clustering-of-monet-and-manet/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Ever had a hard time telling the difference between Claude Monet and Édouard Manet? Orange can help you cluster these two authors and even more, discover which of Monet's masterpiece is indeed very similar to Manet's! Use Image Analytics add-on and play with it. Here's how:\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"R5uchDa_ba4\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"clustering-of-monet-and-manet"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/R5uchDa_ba4" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-09 08:19:34+00:00","draft":false,"title":"Clustering of Monet and Manet","blog":["addons","image analytics","orange3","tutorial","youtube"],"oldUrl":"/blog/2018/05/09/clustering-of-monet-and-manet/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Ever had a hard time telling the difference between Claude Monet and Édouard Manet? Orange can help you cluster these two authors and even more, discover which of Monet's masterpiece is indeed very similar to Manet's! Use Image Analytics add-on and play with it. Here's how:\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"R5uchDa_ba4\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"clustering-of-monet-and-manet"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, plot, visualization, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Color it!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 28, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Holiday season is upon us and even the Orange team is in a festive mood. This is why we made a Color widget!</p>
 <a href="/blog/2015-12-color-it/__optimized-images__/color1.png" data-gallery="true"><img alt="" loading="lazy" width="385" height="445" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-12-color-it/__optimized-images__/color1.png"/></a>
 <p>This fascinating artsy widget will allow you to play with your data set in a new and exciting way. No more dull visualizations and default color schemes! Set your own colors the way YOU want it to! Care for some magical cyan-to-magenta? Or do you prefer a more festive red-to-green? How about several shades of gray? Color widget is your go-to stop for all things color (did you notice it’s our only widget with a colorful icon?). :)</p>
 <p>Coloring works with most visualization widgets, such as scatter plot, distributions, box plot, mosaic display and linear projection. Set the colors for discrete values and gradients for continuous values in this widget, and the same palletes will be used in all downstream widgets. As a bonus, the Color widget also allows you to edit the names of variables and values.</p>
 <a href="/blog/2015-12-color-it/__optimized-images__/color6.png" data-gallery="true"><img alt="" loading="lazy" width="1703" height="1108" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-12-color-it/__optimized-images__/color6.png"/></a>
-<p>Remember - the (blue) sky is the limit.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-12-28 10:47:00+00:00","draft":false,"title":"Color it!","blog":["orange3","plot","visualization","widget"],"oldUrl":"/blog/2015/12/28/color-it/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Holiday season is upon us and even the Orange team is in a festive mood. This is why we made a Color widget!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-color-it/__optimized-images__/color1.png\",\n      alt: \"\",\n      width: \"385\",\n      height: \"445\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This fascinating artsy widget will allow you to play with your data set in a new and exciting way. No more dull visualizations and default color schemes! Set your own colors the way YOU want it to! Care for some magical cyan-to-magenta? Or do you prefer a more festive red-to-green? How about several shades of gray? Color widget is your go-to stop for all things color (did you notice it’s our only widget with a colorful icon?). :)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Coloring works with most visualization widgets, such as scatter plot, distributions, box plot, mosaic display and linear projection. Set the colors for discrete values and gradients for continuous values in this widget, and the same palletes will be used in all downstream widgets. As a bonus, the Color widget also allows you to edit the names of variables and values.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-color-it/__optimized-images__/color6.png\",\n      alt: \"\",\n      width: \"1703\",\n      height: \"1108\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Remember - the (blue) sky is the limit.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"color-it"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Remember - the (blue) sky is the limit.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-12-28 10:47:00+00:00","draft":false,"title":"Color it!","blog":["orange3","plot","visualization","widget"],"oldUrl":"/blog/2015/12/28/color-it/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Holiday season is upon us and even the Orange team is in a festive mood. This is why we made a Color widget!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-color-it/__optimized-images__/color1.png\",\n      alt: \"\",\n      width: \"385\",\n      height: \"445\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This fascinating artsy widget will allow you to play with your data set in a new and exciting way. No more dull visualizations and default color schemes! Set your own colors the way YOU want it to! Care for some magical cyan-to-magenta? Or do you prefer a more festive red-to-green? How about several shades of gray? Color widget is your go-to stop for all things color (did you notice it’s our only widget with a colorful icon?). :)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Coloring works with most visualization widgets, such as scatter plot, distributions, box plot, mosaic display and linear projection. Set the colors for discrete values and gradients for continuous values in this widget, and the same palletes will be used in all downstream widgets. As a bonus, the Color widget also allows you to edit the names of variables and values.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-color-it/__optimized-images__/color6.png\",\n      alt: \"\",\n      width: \"1703\",\n      height: \"1108\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Remember - the (blue) sky is the limit.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"color-it"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Coming soon: Orange&#x27;s new interface</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Coming soon: Orange&#x27;s new interface"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2012-11-27 09:01:00+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="interface"/><meta name="next-head-count" content="17"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>python, development, linux</strong></p><h1 class="sc-c67a4900-0 loxVcf">Compiling Orange 2 on modern Linux</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Marko Toplak</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 10, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We abandoned Orange 2 in 2015 because we did not have enough resources to split between maintaining Orange 2 and building the new version from scratch. Orange was due for a rewrite for quite some time. The core of the pre-3 Orange was written mainly in C++. When Orange was conceived, extending Python with C was the only option to make it run fast enough. Orange was designed before NumPy and even before NumPy&#x27;s predecessors, Numarray and Numeric. The resulting code was hard to maintain. In the meantime, Python libraries such as NumPy and scikit-learn matured, so the rewrite seemed reasonable: Orange 3 was born.</p>
 <p>Currently, we need Orange 2 as a reference implementation for interaction analysis, something that Orange 3 still lacks. Two features positively interact if observing the features in combination yields more information than the sum of information of observing both features separately. The Interaction Graph widget from Orange 2, which uses the <a href="http://stat.columbia.edu/~jakulin/Int/">interaction gain measure</a>, is a great tool to discover such interacting features.</p>
 <p>As a Linux user, I admire the backward compatibility we learned to expect on Windows: if an application ever did work there, it probably still does. On Windows, an old Orange 2 build from <a href="https://download.biolab.si/download/files/">the download archive</a> probably still works. In contrast, old Mac OS Orange packages are likely problematic because recent Mac OS versions stopped supporting 32-bit binaries.</p>
@@ -220,4 +220,4 @@
 <p>The widgets that still fail to work are mainly visualizations. And for a good reason: Orange 2 visualizations were based on pyqwt5, a library that was already abandoned and hard to install even when Orange 2 was still being actively developed. I did not find any conda-installable packages of pyqwt5, and my first attempts at installing it from the source code failed.</p>
 <p>Therefore, I stop. I already have all the parts I need. Now, on to interactions analysis!</p>
 <a href="/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-interactions.png" data-gallery="true"><img alt="" loading="lazy" width="1201" height="857" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-interactions.png"/></a>
-<p>This was fun. If anyone attempts a similar journey, I&#x27;d love to hear about it, especially if visualizations are working.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Marko Toplak","date":"2022-01-10","draft":false,"title":"Compiling Orange 2 on modern Linux","thumbImage":"2022-01-10-orange2-about.png","frontPageImage":"2022-01-10-orange2-about.png","blog":["python","development","linux"],"shortExcerpt":"How to compile an ancient version of Orange on Ubuntu 20.04 or how to spend a cold winter evening.","longExcerpt":"How to compile an ancient version of Orange on Ubuntu 20.04 or how to spend a cold winter evening.","x2images":false,"oldUrl":"/blog/2022/2022-01-10-orange2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We abandoned Orange 2 in 2015 because we did not have enough resources to split between maintaining Orange 2 and building the new version from scratch. Orange was due for a rewrite for quite some time. The core of the pre-3 Orange was written mainly in C++. When Orange was conceived, extending Python with C was the only option to make it run fast enough. Orange was designed before NumPy and even before NumPy's predecessors, Numarray and Numeric. The resulting code was hard to maintain. In the meantime, Python libraries such as NumPy and scikit-learn matured, so the rewrite seemed reasonable: Orange 3 was born.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Currently, we need Orange 2 as a reference implementation for interaction analysis, something that Orange 3 still lacks. Two features positively interact if observing the features in combination yields more information than the sum of information of observing both features separately. The Interaction Graph widget from Orange 2, which uses the \", _jsx(_components.a, {\n        href: \"http://stat.columbia.edu/~jakulin/Int/\",\n        children: \"interaction gain measure\"\n      }), \", is a great tool to discover such interacting features.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As a Linux user, I admire the backward compatibility we learned to expect on Windows: if an application ever did work there, it probably still does. On Windows, an old Orange 2 build from \", _jsx(_components.a, {\n        href: \"https://download.biolab.si/download/files/\",\n        children: \"the download archive\"\n      }), \" probably still works. In contrast, old Mac OS Orange packages are likely problematic because recent Mac OS versions stopped supporting 32-bit binaries.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Linux/GNU users do not have the luxury of installing old binary Orange packages. They do not exist because building them presented a lot of work that would benefit a few users. Even if they had, they would be hard to run; while the Linux kernel is highly backward compatible, its userspace is not.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Therefore, we need to dig into the code, starting with a checkout of Orange 2 code for the git repository:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"git \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"clone\"\n        }), \" git@github.com:biolab/orange2.git\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"cd\"\n        }), \" orange2\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The source code includes the INSTALL.txt, which suggests installing a bunch of Python libraries with the Linux distribution's package manager and then compiling Orange. Now, the problems start. The instructions say they were tested with Ubuntu 14.04, which is not more than seven years old. Curiously, a quick \", _jsx(_components.code, {\n        children: \"git blame\"\n      }), \" confirms that I was the author of the instructions, so Ubuntu 14.04 seems to be the latest distribution I have ever used to install Orange 2.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here the problems begin. The required libraries no longer exist as packages in Ubuntu 20.04 that I am using, and neither does Python 2. Fortunately, Anaconda comes to the rescue. We can use it to create a new Python 2 environment:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"conda create --name py2 -c defaults --override-channels python=2.7\\nconda activate py2\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then, we can install the needed libraries with \", _jsx(_components.code, {\n        children: \"conda\"\n      }), \". I found the list of required packages in the \", _jsx(_components.code, {\n        children: \"setup.py\"\n      }), \" file.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"conda install -c defaults --override-channels numpy scipy setuptools\\nconda install -c conda-forge --override-channels pyqt=4.11\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I needed quite some time to figure out which combination of packages and sources is installable. We can now try to do a development installation of Orange:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"python setup.py develop\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"That fails:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"source\"\n        }), \"/include/stat.hpp:33:27: error: \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'double abs(double)'\"\n        }), \" conflicts with a previous declaration\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Such problems are expected when we compile old code with a newer compiler or libraries. I tried installing older compiler versions, but my distribution only went back to GCC-7, which was already too new. So I had to fix the code. The following change fixed the error:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"--- a/source/include/stat.hpp\\n--- b/source/include/stat.hpp\\n@@ -30,8 +30,8 @@ using namespace std;\\n \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#endif\"\n        }), \"\\n\\n \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#ifndef _MSC_VER_70\"\n        }), \"\\n-inline double abs(double x)\\n-{ \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"return\"\n        }), \" fabs(x); }\\n+//inline double abs(double x)\\n+//{ \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"return\"\n        }), \" fabs(x); }\\n \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#endif\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"There were other errors with return types of C++ functions called from Python. After about \", _jsx(_components.a, {\n        href: \"/blog/2022-01-orange2/2022-01-10-orange2-0001.patch\",\n        children: \"10 changed lines\"\n      }), \" and quite some waiting, Orange was finally installed! The compilation took 10 minutes on my newish laptop. So a lot of code had to be compiled. And someone had to write it! Amazingly, most of the C++ code of Orange 2 was written by a single developer, Janez Demšar.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Well, now Orange is installed, but does it work? Let's try to run it:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"orange-canvas\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nope, not quite:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"  ...\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeCanvas/main.py\\\"\"\n        }), \", line 23, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    from Orange.OrangeCanvas.application.canvasmain import CanvasMainWindow\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeCanvas/application/canvasmain.py\\\"\"\n        }), \", line 28, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    from PyQt4.QtWebKit import QWebView\\nImportError: libQtWebKit.so.4: cannot open shared object file: No such file or directory\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It seems that the installed Qt package lacks WebKit. I could not find any prebuilt Qt4 with it, and I really did not want to go into compiling Qt and PyQt myself! Remembering that the web interface is mostly used in two places in Orange, help and reports, I tried making WebKit import and its usage optional: see changes to \", _jsx(_components.a, {\n        href: \"/blog/2022-01-orange2/2022-01-10-orange2-0002.patch\",\n        children: \"canvasmain.py\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, \", _jsx(_components.code, {\n        children: \"orange-canvas\"\n      }), \" finally opened. But where are the widgets? All widget categories are empty:\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-01-orange2/2022-01-10-orange2-without-widgets.png\",\n      width: \"774\",\n      height: \"530\",\n      src: \"/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-without-widgets.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To find out why, we need to run with debugging output:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"orange-canvas -l4 --force-discovery\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We see new exceptions, namely the repeated problems when widgets try to import the reporting functionality:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"  ...\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeWidgets/OWWidget.py\\\"\"\n        }), \", line 319, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    import OWReport\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeWidgets/OWReport.py\\\"\"\n        }), \", line 34, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    from PyQt4.QtWebKit import *\\nImportError: libQtWebKit.so.4: cannot open shared object file: No such file or directory\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"By making reports optional with changes to \", _jsx(_components.a, {\n        href: \"/blog/2022-01-orange2/2022-01-10-orange2-0003.patch\",\n        children: \"OWReport.py\"\n      }), \", some widgets finally appear. Among them, we are lucky to find File and Interaction Graph widgets, precisely the ones we need. For the Interaction Graph to work properly, I also needed to install \", _jsx(_components.code, {\n        children: \"graphviz\"\n      }), \":\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"sudo apt install graphviz\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widgets that still fail to work are mainly visualizations. And for a good reason: Orange 2 visualizations were based on pyqwt5, a library that was already abandoned and hard to install even when Orange 2 was still being actively developed. I did not find any conda-installable packages of pyqwt5, and my first attempts at installing it from the source code failed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Therefore, I stop. I already have all the parts I need. Now, on to interactions analysis!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-01-orange2/2022-01-10-orange2-interactions.png\",\n      width: \"1201\",\n      height: \"857\",\n      src: \"/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-interactions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This was fun. If anyone attempts a similar journey, I'd love to hear about it, especially if visualizations are working.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":464,"height":309,"src":"/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-about.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"compiling-orange-2-on-modern-linux"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>This was fun. If anyone attempts a similar journey, I&#x27;d love to hear about it, especially if visualizations are working.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Marko Toplak","date":"2022-01-10","draft":false,"title":"Compiling Orange 2 on modern Linux","thumbImage":"2022-01-10-orange2-about.png","frontPageImage":"2022-01-10-orange2-about.png","blog":["python","development","linux"],"shortExcerpt":"How to compile an ancient version of Orange on Ubuntu 20.04 or how to spend a cold winter evening.","longExcerpt":"How to compile an ancient version of Orange on Ubuntu 20.04 or how to spend a cold winter evening.","x2images":false,"oldUrl":"/blog/2022/2022-01-10-orange2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We abandoned Orange 2 in 2015 because we did not have enough resources to split between maintaining Orange 2 and building the new version from scratch. Orange was due for a rewrite for quite some time. The core of the pre-3 Orange was written mainly in C++. When Orange was conceived, extending Python with C was the only option to make it run fast enough. Orange was designed before NumPy and even before NumPy's predecessors, Numarray and Numeric. The resulting code was hard to maintain. In the meantime, Python libraries such as NumPy and scikit-learn matured, so the rewrite seemed reasonable: Orange 3 was born.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Currently, we need Orange 2 as a reference implementation for interaction analysis, something that Orange 3 still lacks. Two features positively interact if observing the features in combination yields more information than the sum of information of observing both features separately. The Interaction Graph widget from Orange 2, which uses the \", _jsx(_components.a, {\n        href: \"http://stat.columbia.edu/~jakulin/Int/\",\n        children: \"interaction gain measure\"\n      }), \", is a great tool to discover such interacting features.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As a Linux user, I admire the backward compatibility we learned to expect on Windows: if an application ever did work there, it probably still does. On Windows, an old Orange 2 build from \", _jsx(_components.a, {\n        href: \"https://download.biolab.si/download/files/\",\n        children: \"the download archive\"\n      }), \" probably still works. In contrast, old Mac OS Orange packages are likely problematic because recent Mac OS versions stopped supporting 32-bit binaries.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Linux/GNU users do not have the luxury of installing old binary Orange packages. They do not exist because building them presented a lot of work that would benefit a few users. Even if they had, they would be hard to run; while the Linux kernel is highly backward compatible, its userspace is not.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Therefore, we need to dig into the code, starting with a checkout of Orange 2 code for the git repository:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"git \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"clone\"\n        }), \" git@github.com:biolab/orange2.git\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"cd\"\n        }), \" orange2\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The source code includes the INSTALL.txt, which suggests installing a bunch of Python libraries with the Linux distribution's package manager and then compiling Orange. Now, the problems start. The instructions say they were tested with Ubuntu 14.04, which is not more than seven years old. Curiously, a quick \", _jsx(_components.code, {\n        children: \"git blame\"\n      }), \" confirms that I was the author of the instructions, so Ubuntu 14.04 seems to be the latest distribution I have ever used to install Orange 2.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here the problems begin. The required libraries no longer exist as packages in Ubuntu 20.04 that I am using, and neither does Python 2. Fortunately, Anaconda comes to the rescue. We can use it to create a new Python 2 environment:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"conda create --name py2 -c defaults --override-channels python=2.7\\nconda activate py2\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then, we can install the needed libraries with \", _jsx(_components.code, {\n        children: \"conda\"\n      }), \". I found the list of required packages in the \", _jsx(_components.code, {\n        children: \"setup.py\"\n      }), \" file.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"conda install -c defaults --override-channels numpy scipy setuptools\\nconda install -c conda-forge --override-channels pyqt=4.11\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I needed quite some time to figure out which combination of packages and sources is installable. We can now try to do a development installation of Orange:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"python setup.py develop\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"That fails:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"source\"\n        }), \"/include/stat.hpp:33:27: error: \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'double abs(double)'\"\n        }), \" conflicts with a previous declaration\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Such problems are expected when we compile old code with a newer compiler or libraries. I tried installing older compiler versions, but my distribution only went back to GCC-7, which was already too new. So I had to fix the code. The following change fixed the error:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"--- a/source/include/stat.hpp\\n--- b/source/include/stat.hpp\\n@@ -30,8 +30,8 @@ using namespace std;\\n \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#endif\"\n        }), \"\\n\\n \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#ifndef _MSC_VER_70\"\n        }), \"\\n-inline double abs(double x)\\n-{ \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"return\"\n        }), \" fabs(x); }\\n+//inline double abs(double x)\\n+//{ \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"return\"\n        }), \" fabs(x); }\\n \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#endif\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"There were other errors with return types of C++ functions called from Python. After about \", _jsx(_components.a, {\n        href: \"/blog/2022-01-orange2/2022-01-10-orange2-0001.patch\",\n        children: \"10 changed lines\"\n      }), \" and quite some waiting, Orange was finally installed! The compilation took 10 minutes on my newish laptop. So a lot of code had to be compiled. And someone had to write it! Amazingly, most of the C++ code of Orange 2 was written by a single developer, Janez Demšar.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Well, now Orange is installed, but does it work? Let's try to run it:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"orange-canvas\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nope, not quite:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"  ...\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeCanvas/main.py\\\"\"\n        }), \", line 23, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    from Orange.OrangeCanvas.application.canvasmain import CanvasMainWindow\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeCanvas/application/canvasmain.py\\\"\"\n        }), \", line 28, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    from PyQt4.QtWebKit import QWebView\\nImportError: libQtWebKit.so.4: cannot open shared object file: No such file or directory\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It seems that the installed Qt package lacks WebKit. I could not find any prebuilt Qt4 with it, and I really did not want to go into compiling Qt and PyQt myself! Remembering that the web interface is mostly used in two places in Orange, help and reports, I tried making WebKit import and its usage optional: see changes to \", _jsx(_components.a, {\n        href: \"/blog/2022-01-orange2/2022-01-10-orange2-0002.patch\",\n        children: \"canvasmain.py\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, \", _jsx(_components.code, {\n        children: \"orange-canvas\"\n      }), \" finally opened. But where are the widgets? All widget categories are empty:\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-01-orange2/2022-01-10-orange2-without-widgets.png\",\n      width: \"774\",\n      height: \"530\",\n      src: \"/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-without-widgets.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To find out why, we need to run with debugging output:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"orange-canvas -l4 --force-discovery\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We see new exceptions, namely the repeated problems when widgets try to import the reporting functionality:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-bash\",\n        children: [\"  ...\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeWidgets/OWWidget.py\\\"\"\n        }), \", line 319, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    import OWReport\\n  File \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/home/marko/dev/orange2/Orange/OrangeWidgets/OWReport.py\\\"\"\n        }), \", line 34, \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \u003cmodule\u003e\\n    from PyQt4.QtWebKit import *\\nImportError: libQtWebKit.so.4: cannot open shared object file: No such file or directory\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"By making reports optional with changes to \", _jsx(_components.a, {\n        href: \"/blog/2022-01-orange2/2022-01-10-orange2-0003.patch\",\n        children: \"OWReport.py\"\n      }), \", some widgets finally appear. Among them, we are lucky to find File and Interaction Graph widgets, precisely the ones we need. For the Interaction Graph to work properly, I also needed to install \", _jsx(_components.code, {\n        children: \"graphviz\"\n      }), \":\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"sudo apt install graphviz\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widgets that still fail to work are mainly visualizations. And for a good reason: Orange 2 visualizations were based on pyqwt5, a library that was already abandoned and hard to install even when Orange 2 was still being actively developed. I did not find any conda-installable packages of pyqwt5, and my first attempts at installing it from the source code failed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Therefore, I stop. I already have all the parts I need. Now, on to interactions analysis!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-01-orange2/2022-01-10-orange2-interactions.png\",\n      width: \"1201\",\n      height: \"857\",\n      src: \"/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-interactions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This was fun. If anyone attempts a similar journey, I'd love to hear about it, especially if visualizations are working.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":464,"height":309,"src":"/blog/2022-01-orange2/__optimized-images__/2022-01-10-orange2-about.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"compiling-orange-2-on-modern-linux"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/computing-joint-entropy-in-python/index.html b/blog/computing-joint-entropy-in-python/index.html
index 895840c24..82025141a 100644
--- a/blog/computing-joint-entropy-in-python/index.html
+++ b/blog/computing-joint-entropy-in-python/index.html
@@ -1,4 +1,4 @@
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Computing joint entropy (in Python)</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Computing joint entropy (in Python)"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2012-06-15 13:21:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="python"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -213,4 +213,4 @@
         return = np.sum(-p * np.log2(p) if p &gt; 0 else 0 for p in
             (np.mean(reduce(np.logical_and, (predictions == c for predictions, c in zip(X, classes))))
                 for classes in itertools.product(*[set(x) for x in X])))
-</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-06-15 13:21:00+00:00","draft":false,"title":"Computing joint entropy (in Python)","blog":["orange3","python"],"oldUrl":"/blog/2012/06/15/computing-joint-entropy-in-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"How I wrote a beautiful, general, and super fast joint entropy method (in Python).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n    return = np.sum(-p * np.log2(p) if p \u003e 0 else 0 for p in\\n        (np.mean(reduce(np.logical_and, (predictions == c for predictions, c in zip(X, classes))))\\n            for classes in itertools.product(*[set(x) for x in X])))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I started with the method to compute the entropy of a single variable. Input is a numpy array with discrete values (either integers or strings).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import numpy as np\\n\\n    def entropy(X):\\n        probs = [np.mean(X == c) for c in set(X)]\\n        return np.sum(-p * np.log2(p) for p in probs)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In my next version I extended it to compute the joint entropy of two variables:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(X, Y):\\n    probs = []\\n    for c1 in set(X):\\n        for c2 in set(Y):\\n            probs.append(np.mean(np.logical_and(X == c1, Y == c2)))\\n\\n    return np.sum(-p * np.log2(p) for p in probs)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now wait a minute, it looks like we have a recursion here. I couldn't stop myself of writing en extended general function to compute the joint entropy of n variables.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X, **kwargs):\\n        predictions = parse_arg(X[0])\\n        H = kwargs[\\\"H\\\"] if \\\"H\\\" in kwargs else 0\\n        v = kwargs[\\\"v\\\"] if \\\"v\\\" in kwargs else np.array([True] * len(predictions))\\n\\n        for c in set(predictions):\\n            if len(X) \u003e 1:\\n                H = entropy(*X[1:], v=np.logical_and(v, predictions == c), H=H)\\n            else:\\n                p = np.mean(np.logical_and(v, predictions == c))\\n                H += -p * np.log2(p) if p \u003e 0 else 0\\n        return H\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It was the ugliest recursive function I've ever written. I couldn't stop coding, I was hooked. Besides, this method was slow as hell and I need a faster version for my reasearch. I need my data tommorow, not next month. I googled if Python has something that would help me deal with the recursive part. I fould this great method: itertools.product, I's just what we need. It takes lists and returns a cartesian product of their values. It's the \\\"nested for loops\\\" in one function.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n        n_insctances = len(X[0])\\n        H = 0\\n        for classes in itertools.product(*[set(x) for x in X]):\\n            v = np.array([True] * n_insctances)\\n            for predictions, c in zip(X, classes):\\n                v = np.logical_and(v, predictions == c)\\n            p = np.mean(v)\\n            H += -p * np.log2(p) if p \u003e 0 else 0\\n        return H\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"No resursion, but still slow. It's time to rewrite loops to the Python-like style. As a sharp eye has already noticed, the second for loop with the np.logical_and inside is perfect for the reduce method.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n        n_insctances = len(X[0])\\n        H = 0\\n        for classes in itertools.product(*[set(x) for x in X]):\\n            v = reduce(np.logical_and, (predictions, c for predictions, c in zip(X, classes)))\\n            p = np.mean(v)\\n            H += -p * np.log2(p) if p \u003e 0 else 0\\n        return H\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, we have to remove just one more list comprehension and we have a beautiful, general, and super fast joint etropy method.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n        return = np.sum(-p * np.log2(p) if p \u003e 0 else 0 for p in\\n            (np.mean(reduce(np.logical_and, (predictions == c for predictions, c in zip(X, classes))))\\n                for classes in itertools.product(*[set(x) for x in X])))\\n\"\n      })\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"computing-joint-entropy-in-python"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-06-15 13:21:00+00:00","draft":false,"title":"Computing joint entropy (in Python)","blog":["orange3","python"],"oldUrl":"/blog/2012/06/15/computing-joint-entropy-in-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"How I wrote a beautiful, general, and super fast joint entropy method (in Python).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n    return = np.sum(-p * np.log2(p) if p \u003e 0 else 0 for p in\\n        (np.mean(reduce(np.logical_and, (predictions == c for predictions, c in zip(X, classes))))\\n            for classes in itertools.product(*[set(x) for x in X])))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I started with the method to compute the entropy of a single variable. Input is a numpy array with discrete values (either integers or strings).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import numpy as np\\n\\n    def entropy(X):\\n        probs = [np.mean(X == c) for c in set(X)]\\n        return np.sum(-p * np.log2(p) for p in probs)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In my next version I extended it to compute the joint entropy of two variables:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(X, Y):\\n    probs = []\\n    for c1 in set(X):\\n        for c2 in set(Y):\\n            probs.append(np.mean(np.logical_and(X == c1, Y == c2)))\\n\\n    return np.sum(-p * np.log2(p) for p in probs)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now wait a minute, it looks like we have a recursion here. I couldn't stop myself of writing en extended general function to compute the joint entropy of n variables.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X, **kwargs):\\n        predictions = parse_arg(X[0])\\n        H = kwargs[\\\"H\\\"] if \\\"H\\\" in kwargs else 0\\n        v = kwargs[\\\"v\\\"] if \\\"v\\\" in kwargs else np.array([True] * len(predictions))\\n\\n        for c in set(predictions):\\n            if len(X) \u003e 1:\\n                H = entropy(*X[1:], v=np.logical_and(v, predictions == c), H=H)\\n            else:\\n                p = np.mean(np.logical_and(v, predictions == c))\\n                H += -p * np.log2(p) if p \u003e 0 else 0\\n        return H\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It was the ugliest recursive function I've ever written. I couldn't stop coding, I was hooked. Besides, this method was slow as hell and I need a faster version for my reasearch. I need my data tommorow, not next month. I googled if Python has something that would help me deal with the recursive part. I fould this great method: itertools.product, I's just what we need. It takes lists and returns a cartesian product of their values. It's the \\\"nested for loops\\\" in one function.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n        n_insctances = len(X[0])\\n        H = 0\\n        for classes in itertools.product(*[set(x) for x in X]):\\n            v = np.array([True] * n_insctances)\\n            for predictions, c in zip(X, classes):\\n                v = np.logical_and(v, predictions == c)\\n            p = np.mean(v)\\n            H += -p * np.log2(p) if p \u003e 0 else 0\\n        return H\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"No resursion, but still slow. It's time to rewrite loops to the Python-like style. As a sharp eye has already noticed, the second for loop with the np.logical_and inside is perfect for the reduce method.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n        n_insctances = len(X[0])\\n        H = 0\\n        for classes in itertools.product(*[set(x) for x in X]):\\n            v = reduce(np.logical_and, (predictions, c for predictions, c in zip(X, classes)))\\n            p = np.mean(v)\\n            H += -p * np.log2(p) if p \u003e 0 else 0\\n        return H\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, we have to remove just one more list comprehension and we have a beautiful, general, and super fast joint etropy method.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    def entropy(*X):\\n        return = np.sum(-p * np.log2(p) if p \u003e 0 else 0 for p in\\n            (np.mean(reduce(np.logical_and, (predictions == c for predictions, c in zip(X, classes))))\\n                for classes in itertools.product(*[set(x) for x in X])))\\n\"\n      })\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"computing-joint-entropy-in-python"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/confusion-matrix-for-regression/index.html b/blog/confusion-matrix-for-regression/index.html
index 591998c8a..c3ee668bf 100644
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Confusion matrix for regression?</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Confusion matrix for regression?"/><meta property="og:description" content="How to display regression error in Orange?"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2022-05-20"/><meta property="article:author" content="Ajda Pretnar Žagar"/><meta property="article:tag" content="regression"/><meta property="article:tag" content="confusion matrix"/><meta property="article:tag" content="scatter plot"/><meta property="article:tag" content="prediction error"/><meta property="og:image" content="/blog/2022-05-regression-results/__optimized-images__/2022-05-20-cf-regression.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>regression, confusion matrix, scatter plot, prediction error</strong></p><h1 class="sc-c67a4900-0 loxVcf">Confusion matrix for regression?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar Žagar</strong></p><p class="sc-b9a2839b-4 ehbERY">May 20, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It is easy to inspect misclassifications in the Confusion Matrix widget when building classification models. One can even click on misclassified instances, output them and observe them in various visualizations. But what about regression? Predicting numeric values doesn&#x27;t even allow connecting Confusion Matrix, nor would it make sense. So how can one inspect prediction error for regression tasks?</p>
 <p>Let us take the well-known <em>housing</em> data set from the File widget. The dataset has 506 instances of houses described with 13 variables and a regression target variable MEDV (median value of homes in 1000$).</p>
 <p>We can quickly build a simple workflow with Test and Score and Linear Regression, which estimates model accuracy and outputs predictions.</p>
@@ -166,4 +166,4 @@
 <p>Alternatively, we can observe the absolute error in the feature space. Let us select <strong>LSTAT</strong> and <strong>RM</strong> variables in the scatter plot. Now, color by <strong>Abs Error</strong> and set the same variable for size. We can observe two correlated variables (LSTAT and RM) and inspect where the failed predictions lie. They are more frequent in the lower and higher LSTAT values.</p>
 <a href="/blog/2022-05-regression-results/__optimized-images__/2022-05-20_scatter-plot2.png" data-gallery="true"><img alt="" loading="lazy" width="1085" height="721" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-05-regression-results/__optimized-images__/2022-05-20_scatter-plot2.png"/></a>
 <p>Now you have no more excuses not to check prediction errors for regression tasks! :)</p>
-<a href="/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow2.png" data-gallery="true"><img alt="" loading="lazy" width="708" height="242" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-05-20","draft":false,"title":"Confusion matrix for regression?","thumbImage":"2022-05-20-cf-regression.png","frontPageImage":"2022-05-20-cf-regression.png","blog":["regression","confusion matrix","scatter plot","prediction error"],"shortExcerpt":"How to display regression error in Orange?","longExcerpt":"Confusion matrix shows classification error, but what is a suitable alternative for observing regression errors in Orange?","x2images":true,"oldUrl":"/blog/2022/2022-05-20-regression-results/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    code: \"code\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {Figure, WindowScreenshot} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It is easy to inspect misclassifications in the Confusion Matrix widget when building classification models. One can even click on misclassified instances, output them and observe them in various visualizations. But what about regression? Predicting numeric values doesn't even allow connecting Confusion Matrix, nor would it make sense. So how can one inspect prediction error for regression tasks?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us take the well-known \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" data set from the File widget. The dataset has 506 instances of houses described with 13 variables and a regression target variable MEDV (median value of homes in 1000$).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can quickly build a simple workflow with Test and Score and Linear Regression, which estimates model accuracy and outputs predictions.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_workflow1.png\",\n      src: \"10%\",\n      width: \"596\",\n      height: \"324\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Looking at predictions in a Data Table, we can see the true value in the grey column (MEDV) and the predicted value in the beige column (Linear Regression). But it is nigh impossible to extract any meaningful information just by looking at true and predicted values in a table. A visualization would be nice.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_data-table.png\",\n      width: \"662\",\n      height: \"493\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us connect Feature Constructor to Test and Score. We will create two new variables in the Feature Constructor, both numeric. Click on \", _jsx(_components.em, {\n        children: \"New\"\n      }), \" --\u003e \", _jsx(_components.em, {\n        children: \"Numeric\"\n      }), \", give it a name, say \", _jsx(_components.em, {\n        children: \"Error\"\n      }), \" and define the function for computing the prediction error. We will pass it a simple subtraction \", _jsx(_components.code, {\n        children: \"Linear_Regression-MEDV\"\n      }), \", which subtracts the true value from the predicted value. In this way, we will get negative values for underpredicted instances and positive values for overpredicted instances.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For a more in-depth inspection, create a variable \", _jsx(_components.em, {\n        children: \"Abs Error\"\n      }), \", computing the absolute error with \", _jsx(_components.code, {\n        children: \"abs(Linear_Regression-MEDV)\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_feat-const.png\",\n      width: \"851\",\n      height: \"396\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_feat-const.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, we can inspect the predictions in a Scatter Plot. Set \", _jsx(_components.strong, {\n        children: \"MEDV\"\n      }), \" for x-axis and \", _jsx(_components.strong, {\n        children: \"Linear Regression\"\n      }), \" for y-axis. Color by \", _jsx(_components.strong, {\n        children: \"Error\"\n      }), \" and set the size to \", _jsx(_components.strong, {\n        children: \"Abs Error\"\n      }), \". Since we are using simple linear regression, the results are more or less as expected. Everything above the imaginary regression line will be colored yellow (overprediction) and below it blue (underpredictions). The larger the point, the larger the prediction error. We can see some strange predictions in the top right corner of the plot. We can select them and inspect them downstream.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_scatter-plot.png\",\n      width: \"1080\",\n      height: \"718\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_scatter-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Alternatively, we can observe the absolute error in the feature space. Let us select \", _jsx(_components.strong, {\n        children: \"LSTAT\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"RM\"\n      }), \" variables in the scatter plot. Now, color by \", _jsx(_components.strong, {\n        children: \"Abs Error\"\n      }), \" and set the same variable for size. We can observe two correlated variables (LSTAT and RM) and inspect where the failed predictions lie. They are more frequent in the lower and higher LSTAT values.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_scatter-plot2.png\",\n      width: \"1085\",\n      height: \"721\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_scatter-plot2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now you have no more excuses not to check prediction errors for regression tasks! :)\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_workflow2.png\",\n      width: \"708\",\n      height: \"242\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow2.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-05-regression-results/__optimized-images__/2022-05-20-cf-regression.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"confusion-matrix-for-regression"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<a href="/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow2.png" data-gallery="true"><img alt="" loading="lazy" width="708" height="242" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-05-20","draft":false,"title":"Confusion matrix for regression?","thumbImage":"2022-05-20-cf-regression.png","frontPageImage":"2022-05-20-cf-regression.png","blog":["regression","confusion matrix","scatter plot","prediction error"],"shortExcerpt":"How to display regression error in Orange?","longExcerpt":"Confusion matrix shows classification error, but what is a suitable alternative for observing regression errors in Orange?","x2images":true,"oldUrl":"/blog/2022/2022-05-20-regression-results/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    code: \"code\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {Figure, WindowScreenshot} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It is easy to inspect misclassifications in the Confusion Matrix widget when building classification models. One can even click on misclassified instances, output them and observe them in various visualizations. But what about regression? Predicting numeric values doesn't even allow connecting Confusion Matrix, nor would it make sense. So how can one inspect prediction error for regression tasks?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us take the well-known \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" data set from the File widget. The dataset has 506 instances of houses described with 13 variables and a regression target variable MEDV (median value of homes in 1000$).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can quickly build a simple workflow with Test and Score and Linear Regression, which estimates model accuracy and outputs predictions.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_workflow1.png\",\n      src: \"10%\",\n      width: \"596\",\n      height: \"324\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Looking at predictions in a Data Table, we can see the true value in the grey column (MEDV) and the predicted value in the beige column (Linear Regression). But it is nigh impossible to extract any meaningful information just by looking at true and predicted values in a table. A visualization would be nice.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_data-table.png\",\n      width: \"662\",\n      height: \"493\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us connect Feature Constructor to Test and Score. We will create two new variables in the Feature Constructor, both numeric. Click on \", _jsx(_components.em, {\n        children: \"New\"\n      }), \" --\u003e \", _jsx(_components.em, {\n        children: \"Numeric\"\n      }), \", give it a name, say \", _jsx(_components.em, {\n        children: \"Error\"\n      }), \" and define the function for computing the prediction error. We will pass it a simple subtraction \", _jsx(_components.code, {\n        children: \"Linear_Regression-MEDV\"\n      }), \", which subtracts the true value from the predicted value. In this way, we will get negative values for underpredicted instances and positive values for overpredicted instances.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For a more in-depth inspection, create a variable \", _jsx(_components.em, {\n        children: \"Abs Error\"\n      }), \", computing the absolute error with \", _jsx(_components.code, {\n        children: \"abs(Linear_Regression-MEDV)\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_feat-const.png\",\n      width: \"851\",\n      height: \"396\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_feat-const.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, we can inspect the predictions in a Scatter Plot. Set \", _jsx(_components.strong, {\n        children: \"MEDV\"\n      }), \" for x-axis and \", _jsx(_components.strong, {\n        children: \"Linear Regression\"\n      }), \" for y-axis. Color by \", _jsx(_components.strong, {\n        children: \"Error\"\n      }), \" and set the size to \", _jsx(_components.strong, {\n        children: \"Abs Error\"\n      }), \". Since we are using simple linear regression, the results are more or less as expected. Everything above the imaginary regression line will be colored yellow (overprediction) and below it blue (underpredictions). The larger the point, the larger the prediction error. We can see some strange predictions in the top right corner of the plot. We can select them and inspect them downstream.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_scatter-plot.png\",\n      width: \"1080\",\n      height: \"718\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_scatter-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Alternatively, we can observe the absolute error in the feature space. Let us select \", _jsx(_components.strong, {\n        children: \"LSTAT\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"RM\"\n      }), \" variables in the scatter plot. Now, color by \", _jsx(_components.strong, {\n        children: \"Abs Error\"\n      }), \" and set the same variable for size. We can observe two correlated variables (LSTAT and RM) and inspect where the failed predictions lie. They are more frequent in the lower and higher LSTAT values.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_scatter-plot2.png\",\n      width: \"1085\",\n      height: \"721\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_scatter-plot2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now you have no more excuses not to check prediction errors for regression tasks! :)\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-regression-results/2022-05-20_workflow2.png\",\n      width: \"708\",\n      height: \"242\",\n      src: \"/blog/2022-05-regression-results/__optimized-images__/2022-05-20_workflow2.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-05-regression-results/__optimized-images__/2022-05-20-cf-regression.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"confusion-matrix-for-regression"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/contact-us/index.html b/blog/contact-us/index.html
index 7062c096a..38a6a1535 100644
--- a/blog/contact-us/index.html
+++ b/blog/contact-us/index.html
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 <p>To replicate our cookie mining, you will need:</p>
 <ol>
@@ -194,4 +194,4 @@ <h2>3. Cookie Wish and Nearest Neighbors in the Cookie Vector Space</h2>
 <a href="/blog/2023-12-cookie-mining-2023/__optimized-images__/neighbors.png" data-gallery="true"><img alt="" loading="lazy" width="658" height="540" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-12-cookie-mining-2023/__optimized-images__/neighbors.png"/></a>
 <p>And that&#x27;s it. This was not the easiest workflow, but we hope you enjoyed it and especially <a href="https://www.youtube.com/watch?v=UMGY9Uf3I6g&amp;list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7&amp;index=4">the video</a>. There, on YouTube, do not forget to <a href="https://www.youtube.com/orangedatamining">subscribe to our Orange Data Mining channel</a>.</p>
 <p><strong>Merry Christmas and a Happy New Year!</strong></p>
-<a href="/blog/2023-12-cookie-mining-2023/__optimized-images__/just-cookies.png" data-gallery="true"><img alt="" loading="lazy" width="838" height="288" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-12-cookie-mining-2023/__optimized-images__/just-cookies.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2023-12-22","draft":false,"title":"Cookie Mining","url":"cookie-mining-2023","thumbImage":"cookie-mining-thumb.jpg","frontPageImage":"cookie-mining-thumb.jpg","blog":["text mining","images"],"shortExcerpt":"A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering.","longExcerpt":"A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering."},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ol: \"ol\",\n    li: \"li\",\n    h2: \"h2\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We just released a \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=UMGY9Uf3I6g\u0026list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7\u0026index=4\",\n        children: \"video\"\n      }), \" about cookie mining. Yes, mining data from cookies. The video goes through the technicalities rather quickly and deliberately doesn't dive into the intricacies of Orange - it's Christmas time and we're focusing on cookies. To accompany the video, here is a blog that dives into the workflow we used and explains its inner workings.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To replicate our cookie mining, you will need:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Orange, :)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Orange's Text, Image Analytics, and Prototypes add-ons installed (currently, ChatGPT widgets are in the Prototypes add-on),\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"the \", _jsx(_components.a, {\n          href: \"http://file.biolab.si/datasets/2023-cookies.xlsx\",\n          children: \"cookie data file\"\n        }), \" with a list of cookie names,\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"the \", _jsx(_components.a, {\n          href: \"http://file.biolab.si/datasets/2023-cookies.zip\",\n          children: \"images of cookies\"\n        }), \",\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"OpenAI's API key to the ChatGPT. Alternatively, you can start with the \", _jsx(_components.a, {\n          href: \"http://file.biolab.si/datasets/2023-cookies-w-description.xlsx\",\n          children: \"data file\"\n        }), \" which already contains cookie descriptions and bypass the use of the ChatGPT Constructor widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is the workflow we constructed in \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=UMGY9Uf3I6g\u0026list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7\u0026index=4\",\n        children: \"the video\"\n      }), \":\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/workflow.png\",\n      width: \"631\",\n      height: \"335\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The workflow consist of three mail branches.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"1. Cookie Description Embedding and Clustering\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the top branch, which starts with a File widget, we load the cookie names from \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/datasets/2023-cookies.xlsx\",\n        children: \"the Excel file\"\n      }), \" and pass them to the ChatGPT Constructor. This takes the cookie name, one line at a time, and constructs a ChatGPT prompt, where the start of the prompt is the text we entered in the ChatGPT Constructor, followed by the cookie name attribute from the data file. The output of ChatGPT Constructor is then a data table with an extra row containing cookie descriptions.Here is the workflow and widgets that illustrate this part of our cookie analysis pipeline.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/cookie-description.png\",\n      width: \"848\",\n      height: \"620\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-description.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Some cookie descriptions are long, and we can read them more easily in the Corpus Viewer. To use it, we need to mark that our data table contains the text corpus and specify that the text we want to mine is stored in the \\\"Text\\\" attribute, i.e. the attribute constructed by the ChatGPT Constructor.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/corpus.png\",\n      width: \"848\",\n      height: \"428\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/corpus.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We pass the corpus data to the Document Embedding widget, where we use BERT sentence embedding to describe each cookie description with its embedding, i.e. with a numeric vector. Then we use the Data Table widget to check the result of the embedding.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/embedding.png\",\n      width: \"756\",\n      height: \"416\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/embedding.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Note that while we set the ChatGPT temperature parameter to 0, which tells ChatGPT to always choose the most likely word when constructing a text sequence, ChatGPT would still randomly choose the best continuation among the top-ranked equally likely words. Since we cannot control the random seeds in ChatGPT, this means that you may get slightly different cookie descriptions than ours, and there is no way to exactly replicate our results.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We feed the cookie embedding into the hierarchical clustering pipeline, which must first estimate the pairwise cookie distances and then feed the distances into the Hierarchical Clustering widget. We used cosine distance and Ward linkage.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/clustering.png\",\n      width: \"786\",\n      height: \"494\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/clustering.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you are like me, cookie names alone would not tell you much. We need cookie images! Our goal is to be able to select a cookie cluster and see what those cookies actually look like.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"2. Cluster Exploration with Images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have prepared a folder with images of our cookies and conveniently named the files with the cookie names.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/folder.png\",\n      width: \"264\",\n      height: \"190\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/folder.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can load the cookie images into Orange with the Import Images widget and check the loaded data with the Image Viewer widget.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/images.png\",\n      width: \"736\",\n      height: \"484\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/images.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"They all look great! Cookies, I mean :). Note that Orange does not carry images with the data at all, but instead constructs the data table when using Import Images, which stores the location of image files in one of the fields. These locations are then used by the image viewer when displaying images. We can also check if this is indeed the case by observing the data passed by the Import Images widget, as I have done below.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/image-data.png\",\n      width: \"602\",\n      height: \"248\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/image-data.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are now ready to merge the text data with the image data. We will do this after the text embedding and tell the Merge Data widget to match the rows of the two files using the cookie name attribute from the text record and the image name attribute from the image record.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/merge.png\",\n      width: \"413\",\n      height: \"389\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/merge.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, with the merged dataset, we can do the clustering and observe the images of the cookie groups. We can also try to summarize the descriptions of the cookies selected in the dendrogram.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/cluster-explanation.png\",\n      width: \"774\",\n      height: \"416\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/cluster-explanation.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Oh, joy! A visual browser of the cookie world!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As a final touch, we can ask the ChatGPT Summarize widget to construct a summary from all the descriptions of the cookies we selected in the hierarchical clustering dendrogram.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/summarize.png\",\n      width: \"859\",\n      height: \"544\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/summarize.png\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"3. Cookie Wish and Nearest Neighbors in the Cookie Vector Space\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There's only one piece left. Write a wish and figure out which cookie is the best match. To write the wish, we use Create Corpus. We pass the text to the document embedding widget, and then find nearest neighbors in the cookie description embedding space. Again, we use cosine distance and limit the number of neighbors to three. Finally, we use the Image Viewer to display the images of the cookies whose description best matches the text we wrote in Create Corpus.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/neighbors.png\",\n      width: \"658\",\n      height: \"540\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/neighbors.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"And that's it. This was not the easiest workflow, but we hope you enjoyed it and especially \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=UMGY9Uf3I6g\u0026list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7\u0026index=4\",\n        children: \"the video\"\n      }), \". There, on YouTube, do not forget to \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/orangedatamining\",\n        children: \"subscribe to our Orange Data Mining channel\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Merry Christmas and a Happy New Year!\"\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/just-cookies.png\",\n      width: \"838\",\n      height: \"288\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/just-cookies.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":512,"height":512,"src":"/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-mining-thumb.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"cookie-mining-2023"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<a href="/blog/2023-12-cookie-mining-2023/__optimized-images__/just-cookies.png" data-gallery="true"><img alt="" loading="lazy" width="838" height="288" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-12-cookie-mining-2023/__optimized-images__/just-cookies.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2023-12-22","draft":false,"title":"Cookie Mining","url":"cookie-mining-2023","thumbImage":"cookie-mining-thumb.jpg","frontPageImage":"cookie-mining-thumb.jpg","blog":["text mining","images"],"shortExcerpt":"A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering.","longExcerpt":"A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering."},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ol: \"ol\",\n    li: \"li\",\n    h2: \"h2\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We just released a \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=UMGY9Uf3I6g\u0026list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7\u0026index=4\",\n        children: \"video\"\n      }), \" about cookie mining. Yes, mining data from cookies. The video goes through the technicalities rather quickly and deliberately doesn't dive into the intricacies of Orange - it's Christmas time and we're focusing on cookies. To accompany the video, here is a blog that dives into the workflow we used and explains its inner workings.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To replicate our cookie mining, you will need:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Orange, :)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Orange's Text, Image Analytics, and Prototypes add-ons installed (currently, ChatGPT widgets are in the Prototypes add-on),\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"the \", _jsx(_components.a, {\n          href: \"http://file.biolab.si/datasets/2023-cookies.xlsx\",\n          children: \"cookie data file\"\n        }), \" with a list of cookie names,\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"the \", _jsx(_components.a, {\n          href: \"http://file.biolab.si/datasets/2023-cookies.zip\",\n          children: \"images of cookies\"\n        }), \",\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"OpenAI's API key to the ChatGPT. Alternatively, you can start with the \", _jsx(_components.a, {\n          href: \"http://file.biolab.si/datasets/2023-cookies-w-description.xlsx\",\n          children: \"data file\"\n        }), \" which already contains cookie descriptions and bypass the use of the ChatGPT Constructor widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is the workflow we constructed in \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=UMGY9Uf3I6g\u0026list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7\u0026index=4\",\n        children: \"the video\"\n      }), \":\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/workflow.png\",\n      width: \"631\",\n      height: \"335\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The workflow consist of three mail branches.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"1. Cookie Description Embedding and Clustering\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the top branch, which starts with a File widget, we load the cookie names from \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/datasets/2023-cookies.xlsx\",\n        children: \"the Excel file\"\n      }), \" and pass them to the ChatGPT Constructor. This takes the cookie name, one line at a time, and constructs a ChatGPT prompt, where the start of the prompt is the text we entered in the ChatGPT Constructor, followed by the cookie name attribute from the data file. The output of ChatGPT Constructor is then a data table with an extra row containing cookie descriptions.Here is the workflow and widgets that illustrate this part of our cookie analysis pipeline.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/cookie-description.png\",\n      width: \"848\",\n      height: \"620\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-description.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Some cookie descriptions are long, and we can read them more easily in the Corpus Viewer. To use it, we need to mark that our data table contains the text corpus and specify that the text we want to mine is stored in the \\\"Text\\\" attribute, i.e. the attribute constructed by the ChatGPT Constructor.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/corpus.png\",\n      width: \"848\",\n      height: \"428\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/corpus.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We pass the corpus data to the Document Embedding widget, where we use BERT sentence embedding to describe each cookie description with its embedding, i.e. with a numeric vector. Then we use the Data Table widget to check the result of the embedding.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/embedding.png\",\n      width: \"756\",\n      height: \"416\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/embedding.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Note that while we set the ChatGPT temperature parameter to 0, which tells ChatGPT to always choose the most likely word when constructing a text sequence, ChatGPT would still randomly choose the best continuation among the top-ranked equally likely words. Since we cannot control the random seeds in ChatGPT, this means that you may get slightly different cookie descriptions than ours, and there is no way to exactly replicate our results.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We feed the cookie embedding into the hierarchical clustering pipeline, which must first estimate the pairwise cookie distances and then feed the distances into the Hierarchical Clustering widget. We used cosine distance and Ward linkage.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/clustering.png\",\n      width: \"786\",\n      height: \"494\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/clustering.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you are like me, cookie names alone would not tell you much. We need cookie images! Our goal is to be able to select a cookie cluster and see what those cookies actually look like.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"2. Cluster Exploration with Images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have prepared a folder with images of our cookies and conveniently named the files with the cookie names.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/folder.png\",\n      width: \"264\",\n      height: \"190\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/folder.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can load the cookie images into Orange with the Import Images widget and check the loaded data with the Image Viewer widget.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/images.png\",\n      width: \"736\",\n      height: \"484\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/images.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"They all look great! Cookies, I mean :). Note that Orange does not carry images with the data at all, but instead constructs the data table when using Import Images, which stores the location of image files in one of the fields. These locations are then used by the image viewer when displaying images. We can also check if this is indeed the case by observing the data passed by the Import Images widget, as I have done below.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/image-data.png\",\n      width: \"602\",\n      height: \"248\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/image-data.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are now ready to merge the text data with the image data. We will do this after the text embedding and tell the Merge Data widget to match the rows of the two files using the cookie name attribute from the text record and the image name attribute from the image record.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/merge.png\",\n      width: \"413\",\n      height: \"389\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/merge.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, with the merged dataset, we can do the clustering and observe the images of the cookie groups. We can also try to summarize the descriptions of the cookies selected in the dendrogram.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/cluster-explanation.png\",\n      width: \"774\",\n      height: \"416\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/cluster-explanation.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Oh, joy! A visual browser of the cookie world!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As a final touch, we can ask the ChatGPT Summarize widget to construct a summary from all the descriptions of the cookies we selected in the hierarchical clustering dendrogram.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/summarize.png\",\n      width: \"859\",\n      height: \"544\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/summarize.png\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"3. Cookie Wish and Nearest Neighbors in the Cookie Vector Space\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There's only one piece left. Write a wish and figure out which cookie is the best match. To write the wish, we use Create Corpus. We pass the text to the document embedding widget, and then find nearest neighbors in the cookie description embedding space. Again, we use cosine distance and limit the number of neighbors to three. Finally, we use the Image Viewer to display the images of the cookies whose description best matches the text we wrote in Create Corpus.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/neighbors.png\",\n      width: \"658\",\n      height: \"540\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/neighbors.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"And that's it. This was not the easiest workflow, but we hope you enjoyed it and especially \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=UMGY9Uf3I6g\u0026list=PLmNPvQr9Tf-Yirm1aN3kHGnhMofrLmEy7\u0026index=4\",\n        children: \"the video\"\n      }), \". There, on YouTube, do not forget to \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/orangedatamining\",\n        children: \"subscribe to our Orange Data Mining channel\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Merry Christmas and a Happy New Year!\"\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-12-cookie-mining-2023/just-cookies.png\",\n      width: \"838\",\n      height: \"288\",\n      src: \"/blog/2023-12-cookie-mining-2023/__optimized-images__/just-cookies.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":512,"height":512,"src":"/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-mining-thumb.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"cookie-mining-2023"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Cox regression in Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Cox regression in Orange"/><meta property="og:description" content="Orange built-in methods for testing and scoring the predictive models now support survival-related models like Cox regression."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-01-27"/><meta property="article:author" content="Jaka Kokošar"/><meta property="article:tag" content="survival analysis"/><meta property="article:tag" content="cox regression"/><meta property="og:image" content="/blog/2023-01-cox-regression-in-orange/__optimized-images__/2023-01-27-cox-test-and-score-thumbImage.png"/><meta property="og:image:width" content="650"/><meta property="og:image:height" content="434"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -157,4 +157,4 @@
 <a href="/blog/2023-01-cox-regression-in-orange/__optimized-images__/2023-01-27-cox-regression.png" data-gallery="true"><img alt="" loading="lazy" width="2616" height="884" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-01-cox-regression-in-orange/__optimized-images__/2023-01-27-cox-regression.png"/></a>
 <p>The illustration above demonstrates how to perform a Cox regression analysis by linking the German Breast Cancer Study Group 2 data from the <strong>Datasets</strong> widget to the <a href="/widget-catalog/survival-analysis/cox-regression/">Cox regression widget</a> and displaying the results in a <strong>Data table</strong>. The documentation of <a href="https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html#interpretation">Lifelines</a>, the python library utilized by the survival widgets in Orange, provides a clear explanation of how to interpret the presented results.</p>
 <p>The survival analysis <a href="https://github.com/biolab/orange3-survival-analysis/">add-on</a> in Orange seamlessly integrates with the rest of the widgets, enabling the construction of standard Orange workflows. For example, in the image below, we demonstrate how we can use the cross-validation strategy to test and score the Cox regression model based on the Concordance index as a measure of predictive accuracy.</p>
-<a href="/blog/2023-01-cox-regression-in-orange/__optimized-images__/2023-01-27-cox-test-and-score.png" data-gallery="true"><img alt="" loading="lazy" width="2240" height="1496" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-01-cox-regression-in-orange/__optimized-images__/2023-01-27-cox-test-and-score.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Jaka Kokošar","date":"2023-01-27","draft":false,"title":"Cox regression in Orange","thumbImage":"2023-01-27-cox-test-and-score-thumbImage.png","frontPageImage":"2023-01-27-cox-test-and-score.png","blog":["survival analysis","cox regression"],"shortExcerpt":"Orange built-in methods for testing and scoring the predictive models now support survival-related models like Cox regression.","longExcerpt":"Orange built-in methods for testing and scoring the predictive models now support survival-related models like Cox regression.","oldUrl":"/blog/2023/2023-01-27-cox-regression-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In our \", _jsx(_components.a, {\n        href: \"/blog/2022-05-25-KaplanMeier\",\n        children: \"previous blog post\"\n      }), \", we discussed the basic concepts of survival analysis, the types of problems it aims to solve and briefly introduced the Kaplan-Meier method. 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@@ -188,4 +188,4 @@
 <p>One final step:</p>
 <pre><code>    Table.save(table, &quot;fruit.tab&quot;)
 </code></pre>
-<p>Your data is safely stored to your computer (in the Python folder)! Good job!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-08-07 13:57:49+00:00","draft":false,"title":"Creating a new data table in Orange through Python","blog":["data","examples","python"],"oldUrl":"/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"IMPORT DATA\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One of the first tasks in Orange data analysis is of course loading your data. If you are using Orange through Python, this is as easy as riding a bike:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    data = Orange.data.Table(“iris”)\\n    print (data)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This will return a neat data table of the famous Iris data set in the console.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"CREATE YOUR OWN DATA TABLE\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What if you want to create your own data table from scratch? Even this is surprisingly simple. 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For discrete attributes call \", _jsx(_components.strong, {\n        children: \"DiscreteVariable\"\n      }), \" and set the name and the possible values, while for a continuous variable call \", _jsx(_components.strong, {\n        children: \"ContinuousVariable\"\n      }), \" and set only the attribute name.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    color = DiscreteVariable(“color”, values=[“orange”, “green”, “yellow”])\\n    \\n    calories = ContinuousVariable(“calories”)\\n    \\n    fiber = ContinuousVariable(“fiber”)]\\n    \\n    fruit = DiscreteVariable(\\\"fruit”, values=[”orange\\\", “apple”, “peach”])\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then set the domain for your data table. See how we set class variable with \", _jsx(_components.strong, {\n        children: \"class_vars\"\n      }), \"?\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    domain = Domain([color, calories, fiber], class_vars=fruit)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Time to input your data!\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    data = Table(domain, [\\n    \\n    [“green”, 4, 1.2, “apple”],\\n    \\n    [\\\"orange\\\", 5, 1.1, \\\"orange\\\"],\\n    \\n    [\\\"yellow\\\", 4, 1.0, \\\"peach\\\"]])\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And now print what you have created!\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print(data)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One final step:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Table.save(table, \\\"fruit.tab\\\")\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Your data is safely stored to your computer (in the Python folder)! 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\ No newline at end of file
+<p>Your data is safely stored to your computer (in the Python folder)! Good job!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-08-07 13:57:49+00:00","draft":false,"title":"Creating a new data table in Orange through Python","blog":["data","examples","python"],"oldUrl":"/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"IMPORT DATA\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One of the first tasks in Orange data analysis is of course loading your data. If you are using Orange through Python, this is as easy as riding a bike:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    data = Orange.data.Table(“iris”)\\n    print (data)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This will return a neat data table of the famous Iris data set in the console.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"CREATE YOUR OWN DATA TABLE\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What if you want to create your own data table from scratch? Even this is surprisingly simple. 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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Dask all Folks: Preparing Large Datasets</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Dask all Folks: Preparing Large Datasets"/><meta property="og:description" content="Preparing large HDF5 datasets that load into Orange as on-disk data."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-10-24"/><meta property="article:author" content="Noah Novšak"/><meta property="article:tag" content="dask"/><meta property="article:tag" content="development"/><meta property="og:image" content="/blog/2023-10-dask-all-folks/__optimized-images__/2023-10-24-dask-all-folks.png"/><meta property="og:image:width" content="726"/><meta property="og:image:height" content="416"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Dask all Folks: Preparing Large Datasets</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Dask all Folks: Preparing Large Datasets"/><meta property="og:description" content="Preparing large HDF5 datasets that load into Orange as on-disk data."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-10-24"/><meta property="article:author" content="Noah Novšak"/><meta property="article:tag" content="dask"/><meta property="article:tag" content="development"/><meta property="og:image" content="/blog/2023-10-dask-all-folks/__optimized-images__/2023-10-24-dask-all-folks.png"/><meta property="og:image:width" content="726"/><meta property="og:image:height" content="416"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dask, development</strong></p><h1 class="sc-c67a4900-0 loxVcf">Dask all Folks: Preparing Large Datasets</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Noah Novšak</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 24, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Today, we will delve into the exciting world of Dask once again, and explore how to finally bring your own data into Orange. Or, more precisely, into an <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">experimental version of Orange that supports Dask</a>.</p>
 <p>If you&#x27;ve already set up your data as an Orange Table and you would just like to re-encode it to better support dask, then all you have to do is export it again as a <code>.hdf5</code> file. This is done either with the <em>Save Data</em> widget (choose &quot;Orange on-disk data&quot; as the file type) or in <em>python</em> like so:</p>
 <pre><code class="hljs language-python">table = Table(<span class="hljs-string">&#x27;my_data.tab&#x27;</span>)
@@ -206,4 +206,4 @@
     domain = f[<span class="hljs-string">&#x27;domain&#x27;</span>]
     domain.create_dataset(<span class="hljs-string">&#x27;attributes_args&#x27;</span>, data=attributes_args)
 </code></pre>
-<p>That&#x27;s it! Now you can create your own <code>.hdf5</code> datasets and start processing them in Orange. Remember, you will need the experimental Orange version from the <em>dask</em> branch: <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">see installation instructions</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2023-10-24","draft":false,"title":"Dask all Folks: Preparing Large Datasets","thumbImage":"2023-10-24-dask-all-folks.png","frontPageImage":"2023-10-24-dask-all-folks.png","blog":["dask","development"],"shortExcerpt":"Preparing large HDF5 datasets that load into Orange as on-disk data.","longExcerpt":"Preparing large HDF5 datasets that load into Orange as on-disk data. This post outlines the specifics of the HDF5 format used by Orange and provides Python code that will help you prepare your own large datasets.","oldUrl":"/blog/2023/2023-10-24-dask-all-folks/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    em: \"em\",\n    pre: \"pre\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Today, we will delve into the exciting world of Dask once again, and explore how to finally bring your own data into Orange. Or, more precisely, into an \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"experimental version of Orange that supports Dask\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If you've already set up your data as an Orange Table and you would just like to re-encode it to better support dask, then all you have to do is export it again as a \", _jsx(_components.code, {\n        children: \".hdf5\"\n      }), \" file. This is done either with the \", _jsx(_components.em, {\n        children: \"Save Data\"\n      }), \" widget (choose \\\"Orange on-disk data\\\" as the file type) or in \", _jsx(_components.em, {\n        children: \"python\"\n      }), \" like so:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"table = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.tab'\"\n        }), \")\\ntable.save(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \")\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Simple as that, end of blog, see you next time!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Well, not quite. For everyone that has a bunch of data in some other format, it's not quite as simple. So here's a short guide on preparing your data for Orange.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First of all I'll assume your data can be read in python into something resembling a \", _jsx(_components.a, {\n        href: \"https://numpy.org/\",\n        children: \"numpy\"\n      }), \" array. If that is not the case, your first step will be to make it so. Next, in addition to the array you will also need to make a \", _jsx(_components.em, {\n        children: \"domain\"\n      }), \" for your data. That is where we store all the extra information that describes the contents of your data (feature names, types, target variables, etc.).\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"table = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'heart_disease'\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"table.domain.attributes\\n(ContinuousVariable(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'age'\"\n        }), \", number_of_decimals=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"),\\n DiscreteVariable(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'gender'\"\n        }), \", values=(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'female'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'male'\"\n        }), \")),\\n...\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Usually in a \", _jsx(_components.code, {\n        children: \".csv\"\n      }), \" or \", _jsx(_components.code, {\n        children: \".tab\"\n      }), \" file we would write this information in the header (the first or first three rows). But the \", _jsx(_components.a, {\n        href: \"https://www.hdfgroup.org/solutions/hdf5\",\n        children: \"HDF5\"\n      }), \" format is somewhat different. It is very flexible and allows information to be stored in many different ways. So we've had to come up with a consistent way to save this information that is as true to the internal structure of our table a possible and simple to implement even outside orange. What we've settled on (depicted in the diagram below) is to make separate arrays to store the raw data, and an extra group to hold the additional information.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-html\",\n        children: \"├── X (Data)\\n├── Y (Class Data)\\n├── metas (Meta Data)\\n│   ├── 0 (Column 1)\\n│   ├── 1 (Column 2)\\n│   └── ... (Additional columns)\\n└── domain\\n    ├── attributes/class_vars/metas (Domain)\\n    └── attributes_args/class_vars_args/metas_args (Optional)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now let's try to apply this idea on an actual dataset! The simplest approach is to use a library like \", _jsx(_components.a, {\n        href: \"https://www.h5py.org/\",\n        children: \"h5py\"\n      }), \", this way it boils down to just a couple lines of code. First we can save our data arrays, (note that due to data types the \", _jsx(_components.em, {\n        children: \"metas\"\n      }), \" array is split up and saved by columns).\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" h5py.File(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'w'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" f:\\n    f.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'X'\"\n        }), \", data=x_array)\\n    f.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Y'\"\n        }), \", data=y_array)\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data_metas.shape[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]:\\n        f.create_dataset(\", _jsxs(_components.span, {\n          className: \"hljs-string\",\n          children: [\"f'metas/\", _jsx(_components.span, {\n            className: \"hljs-subst\",\n            children: \"{i}\"\n          }), \"'\"]\n        }), \", data=metas_array[:, i])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The next step is to make and store our domain. We'll have to make separate arrays for each of our attributes, class variables and meta attributes, and save them with the appropriate names. These 'domain' arrays should consist of two columns, indicating the name and type of each feature (each row representing one column of data).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"attributes = np.array(\\n    [[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'age'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'c'\"\n        }), \"], \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# ContinuousVariable(name='age')\"\n        }), \"\\n     [\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'gender'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'d'\"\n        }), \"], \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# DiscreteVariable(name='gender')\"\n        }), \"\\n     ...],\\n    dtype=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'S'\"\n        }), \"\\n)\\nclass_vars = ...\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" h5py.File(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'r+'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" f:\\n    domain = f.create_group(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'domain'\"\n        }), \")\\n    domain.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'attributes'\"\n        }), \", data=attributes)\\n    domain.creat_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'class_vars'\"\n        }), \", data=class_vars)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Remember the \", _jsx(_components.em, {\n        children: \"heart-disease\"\n      }), \" table from the example above? Well you can see that the domain contains a little bit more than just the name and type of variables. Continuous variables can specify how accurate they are and discrete variables can also represent non-numeric data by specifying values. These parameters can be saved in our domain in seperate (optional) arrays. Because this additional information can be quite complex and varied though, we decided the most reasonable way to save them is to use \", _jsx(_components.a, {\n        href: \"https://www.json.org/json-en.html\",\n        children: \"json\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"attributes_args = np.array(\\n    [[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'{number_of_decimals: 0}'\"\n        }), \"],\\n     [\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'{values: (\\\"female\\\", \\\"male\\\")}'\"\n        }), \"],\\n     ...],\\n    dtype=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'S'\"\n        }), \"\\n)\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" h5py.File(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'r+'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" f:\\n    domain = f[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'domain'\"\n        }), \"]\\n    domain.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'attributes_args'\"\n        }), \", data=attributes_args)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"That's it! Now you can create your own \", _jsx(_components.code, {\n        children: \".hdf5\"\n      }), \" datasets and start processing them in Orange. Remember, you will need the experimental Orange version from the \", _jsx(_components.em, {\n        children: \"dask\"\n      }), \" branch: \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"see installation instructions\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":{"width":726,"height":416,"src":"/blog/2023-10-dask-all-folks/__optimized-images__/2023-10-24-dask-all-folks.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dask-all-folks-preparing-large-datasets"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>That&#x27;s it! Now you can create your own <code>.hdf5</code> datasets and start processing them in Orange. Remember, you will need the experimental Orange version from the <em>dask</em> branch: <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">see installation instructions</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2023-10-24","draft":false,"title":"Dask all Folks: Preparing Large Datasets","thumbImage":"2023-10-24-dask-all-folks.png","frontPageImage":"2023-10-24-dask-all-folks.png","blog":["dask","development"],"shortExcerpt":"Preparing large HDF5 datasets that load into Orange as on-disk data.","longExcerpt":"Preparing large HDF5 datasets that load into Orange as on-disk data. This post outlines the specifics of the HDF5 format used by Orange and provides Python code that will help you prepare your own large datasets.","oldUrl":"/blog/2023/2023-10-24-dask-all-folks/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    em: \"em\",\n    pre: \"pre\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Today, we will delve into the exciting world of Dask once again, and explore how to finally bring your own data into Orange. Or, more precisely, into an \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"experimental version of Orange that supports Dask\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If you've already set up your data as an Orange Table and you would just like to re-encode it to better support dask, then all you have to do is export it again as a \", _jsx(_components.code, {\n        children: \".hdf5\"\n      }), \" file. This is done either with the \", _jsx(_components.em, {\n        children: \"Save Data\"\n      }), \" widget (choose \\\"Orange on-disk data\\\" as the file type) or in \", _jsx(_components.em, {\n        children: \"python\"\n      }), \" like so:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"table = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.tab'\"\n        }), \")\\ntable.save(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \")\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Simple as that, end of blog, see you next time!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Well, not quite. For everyone that has a bunch of data in some other format, it's not quite as simple. So here's a short guide on preparing your data for Orange.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First of all I'll assume your data can be read in python into something resembling a \", _jsx(_components.a, {\n        href: \"https://numpy.org/\",\n        children: \"numpy\"\n      }), \" array. If that is not the case, your first step will be to make it so. Next, in addition to the array you will also need to make a \", _jsx(_components.em, {\n        children: \"domain\"\n      }), \" for your data. That is where we store all the extra information that describes the contents of your data (feature names, types, target variables, etc.).\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"table = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'heart_disease'\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"table.domain.attributes\\n(ContinuousVariable(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'age'\"\n        }), \", number_of_decimals=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"),\\n DiscreteVariable(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'gender'\"\n        }), \", values=(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'female'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'male'\"\n        }), \")),\\n...\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Usually in a \", _jsx(_components.code, {\n        children: \".csv\"\n      }), \" or \", _jsx(_components.code, {\n        children: \".tab\"\n      }), \" file we would write this information in the header (the first or first three rows). But the \", _jsx(_components.a, {\n        href: \"https://www.hdfgroup.org/solutions/hdf5\",\n        children: \"HDF5\"\n      }), \" format is somewhat different. It is very flexible and allows information to be stored in many different ways. So we've had to come up with a consistent way to save this information that is as true to the internal structure of our table a possible and simple to implement even outside orange. What we've settled on (depicted in the diagram below) is to make separate arrays to store the raw data, and an extra group to hold the additional information.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-html\",\n        children: \"├── X (Data)\\n├── Y (Class Data)\\n├── metas (Meta Data)\\n│   ├── 0 (Column 1)\\n│   ├── 1 (Column 2)\\n│   └── ... (Additional columns)\\n└── domain\\n    ├── attributes/class_vars/metas (Domain)\\n    └── attributes_args/class_vars_args/metas_args (Optional)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now let's try to apply this idea on an actual dataset! The simplest approach is to use a library like \", _jsx(_components.a, {\n        href: \"https://www.h5py.org/\",\n        children: \"h5py\"\n      }), \", this way it boils down to just a couple lines of code. First we can save our data arrays, (note that due to data types the \", _jsx(_components.em, {\n        children: \"metas\"\n      }), \" array is split up and saved by columns).\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" h5py.File(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'w'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" f:\\n    f.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'X'\"\n        }), \", data=x_array)\\n    f.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Y'\"\n        }), \", data=y_array)\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data_metas.shape[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]:\\n        f.create_dataset(\", _jsxs(_components.span, {\n          className: \"hljs-string\",\n          children: [\"f'metas/\", _jsx(_components.span, {\n            className: \"hljs-subst\",\n            children: \"{i}\"\n          }), \"'\"]\n        }), \", data=metas_array[:, i])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The next step is to make and store our domain. We'll have to make separate arrays for each of our attributes, class variables and meta attributes, and save them with the appropriate names. These 'domain' arrays should consist of two columns, indicating the name and type of each feature (each row representing one column of data).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"attributes = np.array(\\n    [[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'age'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'c'\"\n        }), \"], \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# ContinuousVariable(name='age')\"\n        }), \"\\n     [\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'gender'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'d'\"\n        }), \"], \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# DiscreteVariable(name='gender')\"\n        }), \"\\n     ...],\\n    dtype=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'S'\"\n        }), \"\\n)\\nclass_vars = ...\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" h5py.File(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'r+'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" f:\\n    domain = f.create_group(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'domain'\"\n        }), \")\\n    domain.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'attributes'\"\n        }), \", data=attributes)\\n    domain.creat_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'class_vars'\"\n        }), \", data=class_vars)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Remember the \", _jsx(_components.em, {\n        children: \"heart-disease\"\n      }), \" table from the example above? Well you can see that the domain contains a little bit more than just the name and type of variables. Continuous variables can specify how accurate they are and discrete variables can also represent non-numeric data by specifying values. These parameters can be saved in our domain in seperate (optional) arrays. Because this additional information can be quite complex and varied though, we decided the most reasonable way to save them is to use \", _jsx(_components.a, {\n        href: \"https://www.json.org/json-en.html\",\n        children: \"json\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"attributes_args = np.array(\\n    [[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'{number_of_decimals: 0}'\"\n        }), \"],\\n     [\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'{values: (\\\"female\\\", \\\"male\\\")}'\"\n        }), \"],\\n     ...],\\n    dtype=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'S'\"\n        }), \"\\n)\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" h5py.File(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'my_data.hdf5'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'r+'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" f:\\n    domain = f[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'domain'\"\n        }), \"]\\n    domain.create_dataset(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'attributes_args'\"\n        }), \", data=attributes_args)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"That's it! Now you can create your own \", _jsx(_components.code, {\n        children: \".hdf5\"\n      }), \" datasets and start processing them in Orange. Remember, you will need the experimental Orange version from the \", _jsx(_components.em, {\n        children: \"dask\"\n      }), \" branch: \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"see installation instructions\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":{"width":726,"height":416,"src":"/blog/2023-10-dask-all-folks/__optimized-images__/2023-10-24-dask-all-folks.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dask-all-folks-preparing-large-datasets"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/dask-spectroscopy/index.html b/blog/dask-spectroscopy/index.html
index d19ebc94c..648d125ef 100644
--- a/blog/dask-spectroscopy/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dask, spectroscopy</strong></p><h1 class="sc-c67a4900-0 loxVcf">Can Orange explore a 13 GB data set?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Marko Toplak</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 28, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>What was the largest file you ever worked with in Orange? Mine used to be a 2D spectral image of about 2 GB. I got it from a colleague to stress-test visualizations in the <a href="https://github.com/quasars/orange-spectroscopy">Spectroscopy add-on</a>. At that time, in 2018, I only had a laptop with 8 GB of working memory, so, apart from the stress tests, I could not do much with it. Running machine learning techniques was a no-go because intermediate results immediately filled my memory. Furthermore, in Orange, even simple operations such as normalization can multiply data in memory; after that, Orange needs twice the space because it also needs to store both the original and the normalized data.</p>
 <p>Things change. My current laptop has 16 GB of memory. But more importantly, my Orange version has a trick up its sleeve: support for on-disk data. Yours could support it, too, but you&#x27;ll need to use Orange from the <a href="https://github.com/biolab/orange3/tree/dask">experimental <code>dask</code> branch</a>. Installation is not for the faint-hearted, but if you are curious about testing these features, follow <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">our installation guide</a>.</p>
 <p>To see how it works, we will use a data set of tissue sections of breast cancer. The data set BRC961-BR1001
@@ -167,4 +167,4 @@
 <p>Because only a subset of Orange&#x27;s widget supports Dask (basic data manipulation and simpler machine learning techniques, for example, <strong>k-Means</strong>, <strong>Linear Regression</strong>, <strong>Logistic Regression</strong>) we may need to escape it. Fortunately, there is a way: the <strong>Dask Compute</strong> widget brings a table to memory. Performance-wise, it always makes sense to work in memory if we can afford it. Here, we selected one tissue sample, stored it in memory with <strong>Dask Compute</strong>, and can now work with this data that should be about 0.5 GB in size.</p>
 <a href="/blog/2023-10-dask-spectroscopy/__optimized-images__/compute.png" data-gallery="true"><img alt="" loading="lazy" width="1021" height="640" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-10-dask-spectroscopy/__optimized-images__/compute.png"/></a>
 <p>That&#x27;s it! To try this yourself, install the <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">experimental Orange version from the <code>dask</code> branch</a>. And remember, start experimenting with smaller files.</p>
-<p>Finally, we would like to thank the <a href="https://chanzuckerberg.com/">Chan Zuckerberg Initiative</a>, which supported the development of Orange for big data.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Marko Toplak","date":"2023-10-28","draft":false,"title":"Can Orange explore a 13 GB data set?","url":"dask-spectroscopy","thumbImage":"du.png","frontPageImage":"du.png","blog":["dask","spectroscopy"],"shortExcerpt":"An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.","longExcerpt":"An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set."},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"What was the largest file you ever worked with in Orange? Mine used to be a 2D spectral image of about 2 GB. I got it from a colleague to stress-test visualizations in the \", _jsx(_components.a, {\n        href: \"https://github.com/quasars/orange-spectroscopy\",\n        children: \"Spectroscopy add-on\"\n      }), \". At that time, in 2018, I only had a laptop with 8 GB of working memory, so, apart from the stress tests, I could not do much with it. Running machine learning techniques was a no-go because intermediate results immediately filled my memory. Furthermore, in Orange, even simple operations such as normalization can multiply data in memory; after that, Orange needs twice the space because it also needs to store both the original and the normalized data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Things change. My current laptop has 16 GB of memory. But more importantly, my Orange version has a trick up its sleeve: support for on-disk data. Yours could support it, too, but you'll need to use Orange from the \", _jsxs(_components.a, {\n        href: \"https://github.com/biolab/orange3/tree/dask\",\n        children: [\"experimental \", _jsx(_components.code, {\n          children: \"dask\"\n        }), \" branch\"]\n      }), \". Installation is not for the faint-hearted, but if you are curious about testing these features, follow \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"our installation guide\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To see how it works, we will use a data set of tissue sections of breast cancer. The data set BRC961-BR1001\\nwas \", _jsx(_components.a, {\n        href: \"https://stim.ee.uh.edu/resources/data-sets/\",\n        children: \"shared by David Mayerich\"\n      }), \". Originally, it was even larger than 100 GB, so we took a subset and converted it into a \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/dask-all-folks-preparing-large-datasets/\",\n        children: \"special HDF5 format that Orange can read with Dask\"\n      }), \". We obtained a 13 GB HDF5 file; you can download \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/orange-dask/\",\n        children: \"it here\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"When we load \", _jsx(_components.code, {\n        children: \"brc961-br1001-orange.hdf5\"\n      }), \" with the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget, it loads immediately! What? Whoever used Orange with even slightly bigger data will know loading files takes time. But here, with 13 GB, it was immediate! That is, well, because almost nothing happened: the file was loaded with Dask, meaning only its metadata was read, but the actual content was not read. With Dask, the paradigm is that data is only accessed when needed. We can observe that if we connect \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \" and scroll around. After scrolling, we see a slight delay before data is shown: only now the actual values are read (for a longer story about, see \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/orange-you-going-to-ask-about-dask/\",\n        children: \"Noah's blog\"\n      }), \").\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/hyper1.png\",\n      width: \"845\",\n      height: \"746\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/hyper1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Because our data is a spectral map, we view it in the \", _jsx(_components.strong, {\n        children: \"HyperSpectra\"\n      }), \" widget. I've set the widget to color by values of the Amide I peak (wavenumber 1650). On my laptop, it takes about a minute for the \", _jsx(_components.strong, {\n        children: \"HyperSpectra\"\n      }), \" to show both plots fully. Now, the data was being read but was only read in chunks, so it did not fill up our working memory. In the map, we see 18 tissue sections on a microscope slide. The slide contains no useful information, so we can remove it. It has low values for wavenumber 1650 (the Amide I peak). Let's use \", _jsx(_components.strong, {\n        children: \"Distributions\"\n      }), \" to plot that.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/distribution.png\",\n      width: \"935\",\n      height: \"505\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/distribution.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"That took a while, but a useful cutoff seems to be somewhere between 0 and 0.2. Therefore, let's be conservative and instruct \", _jsx(_components.strong, {\n        children: \"Select Rows\"\n      }), \" to select rows with wavenumber 1650 higher than 0.05. Now, we see the following.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/select-rows.png\",\n      width: \"1303\",\n      height: \"545\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/select-rows.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, let's perform principal component analysis (PCA). Because PCA is computationally more demanding, we will use \", _jsx(_components.strong, {\n        children: \"Data Sampler\"\n      }), \" to run \", _jsx(_components.strong, {\n        children: \"PCA\"\n      }), \" on a subset of 10000 rows (spectra). Then, we will apply (with \", _jsx(_components.strong, {\n        children: \"Apply Domain\"\n      }), \") the computed principal components to the whole data set and view the resulting PCA scores as an image. Here, we show \", _jsx(_components.a, {\n        href: \"/blog/2023-10-dask-spectroscopy/spectra-breast-cancer-pca.ows\",\n        children: \"the full workflow\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/spectra-breast-cancer-pca.png\",\n      width: \"1521\",\n      height: \"1035\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/spectra-breast-cancer-pca.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Because only a subset of Orange's widget supports Dask (basic data manipulation and simpler machine learning techniques, for example, \", _jsx(_components.strong, {\n        children: \"k-Means\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"Linear Regression\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"Logistic Regression\"\n      }), \") we may need to escape it. Fortunately, there is a way: the \", _jsx(_components.strong, {\n        children: \"Dask Compute\"\n      }), \" widget brings a table to memory. Performance-wise, it always makes sense to work in memory if we can afford it. Here, we selected one tissue sample, stored it in memory with \", _jsx(_components.strong, {\n        children: \"Dask Compute\"\n      }), \", and can now work with this data that should be about 0.5 GB in size.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/compute.png\",\n      width: \"1021\",\n      height: \"640\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/compute.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"That's it! To try this yourself, install the \", _jsxs(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: [\"experimental Orange version from the \", _jsx(_components.code, {\n          children: \"dask\"\n        }), \" branch\"]\n      }), \". And remember, start experimenting with smaller files.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, we would like to thank the \", _jsx(_components.a, {\n        href: \"https://chanzuckerberg.com/\",\n        children: \"Chan Zuckerberg Initiative\"\n      }), \", which supported the development of Orange for big data.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":591,"height":352,"src":"/blog/2023-10-dask-spectroscopy/__optimized-images__/du.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dask-spectroscopy"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Finally, we would like to thank the <a href="https://chanzuckerberg.com/">Chan Zuckerberg Initiative</a>, which supported the development of Orange for big data.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Marko Toplak","date":"2023-10-28","draft":false,"title":"Can Orange explore a 13 GB data set?","url":"dask-spectroscopy","thumbImage":"du.png","frontPageImage":"du.png","blog":["dask","spectroscopy"],"shortExcerpt":"An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.","longExcerpt":"An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set."},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"What was the largest file you ever worked with in Orange? Mine used to be a 2D spectral image of about 2 GB. I got it from a colleague to stress-test visualizations in the \", _jsx(_components.a, {\n        href: \"https://github.com/quasars/orange-spectroscopy\",\n        children: \"Spectroscopy add-on\"\n      }), \". At that time, in 2018, I only had a laptop with 8 GB of working memory, so, apart from the stress tests, I could not do much with it. Running machine learning techniques was a no-go because intermediate results immediately filled my memory. Furthermore, in Orange, even simple operations such as normalization can multiply data in memory; after that, Orange needs twice the space because it also needs to store both the original and the normalized data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Things change. My current laptop has 16 GB of memory. But more importantly, my Orange version has a trick up its sleeve: support for on-disk data. Yours could support it, too, but you'll need to use Orange from the \", _jsxs(_components.a, {\n        href: \"https://github.com/biolab/orange3/tree/dask\",\n        children: [\"experimental \", _jsx(_components.code, {\n          children: \"dask\"\n        }), \" branch\"]\n      }), \". Installation is not for the faint-hearted, but if you are curious about testing these features, follow \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"our installation guide\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To see how it works, we will use a data set of tissue sections of breast cancer. The data set BRC961-BR1001\\nwas \", _jsx(_components.a, {\n        href: \"https://stim.ee.uh.edu/resources/data-sets/\",\n        children: \"shared by David Mayerich\"\n      }), \". Originally, it was even larger than 100 GB, so we took a subset and converted it into a \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/dask-all-folks-preparing-large-datasets/\",\n        children: \"special HDF5 format that Orange can read with Dask\"\n      }), \". We obtained a 13 GB HDF5 file; you can download \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/orange-dask/\",\n        children: \"it here\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"When we load \", _jsx(_components.code, {\n        children: \"brc961-br1001-orange.hdf5\"\n      }), \" with the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget, it loads immediately! What? Whoever used Orange with even slightly bigger data will know loading files takes time. But here, with 13 GB, it was immediate! That is, well, because almost nothing happened: the file was loaded with Dask, meaning only its metadata was read, but the actual content was not read. With Dask, the paradigm is that data is only accessed when needed. We can observe that if we connect \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \" and scroll around. After scrolling, we see a slight delay before data is shown: only now the actual values are read (for a longer story about, see \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/orange-you-going-to-ask-about-dask/\",\n        children: \"Noah's blog\"\n      }), \").\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/hyper1.png\",\n      width: \"845\",\n      height: \"746\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/hyper1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Because our data is a spectral map, we view it in the \", _jsx(_components.strong, {\n        children: \"HyperSpectra\"\n      }), \" widget. I've set the widget to color by values of the Amide I peak (wavenumber 1650). On my laptop, it takes about a minute for the \", _jsx(_components.strong, {\n        children: \"HyperSpectra\"\n      }), \" to show both plots fully. Now, the data was being read but was only read in chunks, so it did not fill up our working memory. In the map, we see 18 tissue sections on a microscope slide. The slide contains no useful information, so we can remove it. It has low values for wavenumber 1650 (the Amide I peak). Let's use \", _jsx(_components.strong, {\n        children: \"Distributions\"\n      }), \" to plot that.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/distribution.png\",\n      width: \"935\",\n      height: \"505\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/distribution.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"That took a while, but a useful cutoff seems to be somewhere between 0 and 0.2. Therefore, let's be conservative and instruct \", _jsx(_components.strong, {\n        children: \"Select Rows\"\n      }), \" to select rows with wavenumber 1650 higher than 0.05. Now, we see the following.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/select-rows.png\",\n      width: \"1303\",\n      height: \"545\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/select-rows.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, let's perform principal component analysis (PCA). Because PCA is computationally more demanding, we will use \", _jsx(_components.strong, {\n        children: \"Data Sampler\"\n      }), \" to run \", _jsx(_components.strong, {\n        children: \"PCA\"\n      }), \" on a subset of 10000 rows (spectra). Then, we will apply (with \", _jsx(_components.strong, {\n        children: \"Apply Domain\"\n      }), \") the computed principal components to the whole data set and view the resulting PCA scores as an image. Here, we show \", _jsx(_components.a, {\n        href: \"/blog/2023-10-dask-spectroscopy/spectra-breast-cancer-pca.ows\",\n        children: \"the full workflow\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/spectra-breast-cancer-pca.png\",\n      width: \"1521\",\n      height: \"1035\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/spectra-breast-cancer-pca.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Because only a subset of Orange's widget supports Dask (basic data manipulation and simpler machine learning techniques, for example, \", _jsx(_components.strong, {\n        children: \"k-Means\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"Linear Regression\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"Logistic Regression\"\n      }), \") we may need to escape it. Fortunately, there is a way: the \", _jsx(_components.strong, {\n        children: \"Dask Compute\"\n      }), \" widget brings a table to memory. Performance-wise, it always makes sense to work in memory if we can afford it. Here, we selected one tissue sample, stored it in memory with \", _jsx(_components.strong, {\n        children: \"Dask Compute\"\n      }), \", and can now work with this data that should be about 0.5 GB in size.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-10-dask-spectroscopy/compute.png\",\n      width: \"1021\",\n      height: \"640\",\n      src: \"/blog/2023-10-dask-spectroscopy/__optimized-images__/compute.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"That's it! To try this yourself, install the \", _jsxs(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: [\"experimental Orange version from the \", _jsx(_components.code, {\n          children: \"dask\"\n        }), \" branch\"]\n      }), \". And remember, start experimenting with smaller files.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, we would like to thank the \", _jsx(_components.a, {\n        href: \"https://chanzuckerberg.com/\",\n        children: \"Chan Zuckerberg Initiative\"\n      }), \", which supported the development of Orange for big data.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":591,"height":352,"src":"/blog/2023-10-dask-spectroscopy/__optimized-images__/du.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dask-spectroscopy"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dask, survival analysis</strong></p><h1 class="sc-c67a4900-0 loxVcf">From Data Portals to Portals of Doom: Avoiding it with Dask</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Jaka Kokošar</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 08, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Since its inception, The Cancer Genome Atlas (<a href="https://www.cancer.gov/ccg/research/genome-sequencing/tcga">TCGA</a>) project has been a treasure trove of data for biomedical researchers. The data TCGA provides is essential for researchers who want to analyze potential biomarkers. But there&#x27;s a hitch. Not every researcher is a data whisperer.</p>
 <p>Luckily, there are several web-based tools that researchers can add to their toolkits. These tools make it easy to access and analyze survival data from TCGA. Notable examples are <a href="https://kmplot.com/analysis/">Kaplan-Meier Plotter</a> and <a href="http://gepia.cancer-pku.cn/">GEPIA</a>, among <a href="https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00068/full">others</a>.</p>
 <a href="/blog/2023-11-dask-survival-analysis/__optimized-images__/gepia-example.png" data-gallery="true"><img alt="" loading="lazy" width="2730" height="3122" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-11-dask-survival-analysis/__optimized-images__/gepia-example.png"/></a>
@@ -177,4 +177,4 @@
 <a href="/blog/2023-11-dask-survival-analysis/__optimized-images__/workflow2.png" data-gallery="true"><img alt="" loading="lazy" width="2642" height="1574" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-11-dask-survival-analysis/__optimized-images__/workflow2.png"/></a>
 <p>And since we have the whole TCGA datasets loaded, I could branch the same workflow and repeat the same procedure on different types of cancer and compare results side by side!</p>
 <p>A <a href="https://orangedatamining.com/download/">stable Orange release</a> will suffice for smaller data sets. For more options, check <a href="https://orangedatamining.com/examples/?tag=Survival+Analysis">the library of survival analysis workflows</a>. You will find workflows that range from simple survival curve estimation to more complex analysis using Cox regression are readily available to experiment with.</p>
-<p>Thats it! You can download and try <a href="/blog/2023-11-dask-survival-analysis/full-workflow.ows">the full workflow</a>. Remember: for big data sets, use the <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">experimental Orange version</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Jaka Kokošar","date":"2023-11-08","draft":false,"title":"From Data Portals to Portals of Doom: Avoiding it with Dask","url":"dask-survival-analysis","thumbImage":"orange-not-responding-thumb.png","frontPageImage":"orange-not-responding-thumb.png","blog":["dask","survival analysis"],"shortExcerpt":"Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source's authenticity and enabling custom workflows.","longExcerpt":"Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source's authenticity and enabling custom workflows."},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Since its inception, The Cancer Genome Atlas (\", _jsx(_components.a, {\n        href: \"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\",\n        children: \"TCGA\"\n      }), \") project has been a treasure trove of data for biomedical researchers. The data TCGA provides is essential for researchers who want to analyze potential biomarkers. But there's a hitch. Not every researcher is a data whisperer.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Luckily, there are several web-based tools that researchers can add to their toolkits. These tools make it easy to access and analyze survival data from TCGA. Notable examples are \", _jsx(_components.a, {\n        href: \"https://kmplot.com/analysis/\",\n        children: \"Kaplan-Meier Plotter\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"http://gepia.cancer-pku.cn/\",\n        children: \"GEPIA\"\n      }), \", among \", _jsx(_components.a, {\n        href: \"https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00068/full\",\n        children: \"others\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/gepia-example.png\",\n      width: \"2730\",\n      height: \"3122\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/gepia-example.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In their very essence, these tools allow you to enter a gene name and output the results of the analysis on pre-curated TCGA datasets. For example, in the image above, I have entered the gene name TP53 and selected the cancer type \\\"lower-grade glioma\\\" (TCGA-LGG). We see the Kaplan-Meier plot for the high- and low-risk patient groups and corresponding p-values, which quantify the difference in survival between the groups. For details, refer to a review paper by \", _jsx(_components.a, {\n        href: \"https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00068/full\",\n        children: \"Zheng and others\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Looks good; where is the catch? Well, we shouldn't blindly trust the underlying source of survival data of those web-based tools. Curated datasets between tools can vary; more in \", _jsx(_components.a, {\n        href: \"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8726696/\",\n        children: \"Idogawa et al.\"\n      }), \". And this is no small thing. Adding fuel to the fire here, I will let the reader guess how many of those tools are open-source.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Still, despite their limitations, these tools are useful. However, I would like to analyze data from sources I trust. Therefore, I have prepared \", _jsx(_components.a, {\n        href: \"https://github.com/jakakokosar/tcga-data\",\n        children: \"notebooks\"\n      }), \" that can download gene expression and survival data for all cancer types in the TCGA project. I see the irony here: I am not asking you to trust \", _jsx(_components.a, {\n        href: \"https://github.com/jakakokosar/tcga-data\",\n        children: \"my code\"\n      }), \"; if you can, verify it. Or, download from the official \", _jsx(_components.a, {\n        href: \"https://portal.gdc.cancer.gov/\",\n        children: \"GDC data portal\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The combined dataset from TCGA consists of approximately 10K samples and 60K gene expression values. The size of the file on my disk is around 3GB. You might think this is not much, but try loading it in Pandas. You will need around 30GB of system memory and 17 minutes just to read the file.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" pandas \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" pd\\n\\ndf = pd.read_csv(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'data/TCGA-combined.csv'\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# peak memory: 30722.37 MiB, increment: 30597.50 MiB\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead of coding, could we use any existing visual analytics tools? Kaplan-Meier plotter stands out because it allows me to upload my own data (a rare find among all the other tools). That is just what I was looking for!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/km-plotter-upload.png\",\n      width: \"1728\",\n      height: \"426\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/km-plotter-upload.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The option to upload my own data is cool, but there is a 4MB limitation. What about Orange?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/orange-not-responding.png\",\n      width: \"1762\",\n      height: \"1272\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Well, things quickly get out of hand. Orange typically handles files that are within the range of a few hundred megabytes just fine. This, of course, also depends on the specifications of the machine that Orange is running on. To support much larger files, we extended Orange with on-disk data support, where the complete data is never loaded into memory. We used Dask as the backend for the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"experimental Orange version\"\n      }), \". For more, refer to the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/dask-spectroscopy/\",\n        children: \"previous blog post\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that I can work with large datasets in Orange, I have a similar tool to those web-server-like ones: the difference is that I have to bring my own data and construct my analysis pipelines from scratch. But this is also advantageous: I am not limited to task-specific analyses that those other tools provide, and I can fully trust the data source.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here, I stratify lower-grade glioma patients (TCGA-LGG) into high- and low-risk groups according to the expression of gene TP53. Then, I use the Kaplan-Meier widget to compute and plot survival curves and test how significant the difference in survival between the groups is using the log-rank test. All this without my computer breaking a sweat!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/workflow.png\",\n      width: \"2450\",\n      height: \"1900\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With the workflow I constructed above, I could click through a list of genes and observe the results. But in my data, I have a lot of genes. Clicking so many becomes a tedious task. I could automate this process with the Rank Survival Features widget. The output is the ranked list of genes based on the p-value from the log-rank test.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/workflow2.png\",\n      width: \"2642\",\n      height: \"1574\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/workflow2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And since we have the whole TCGA datasets loaded, I could branch the same workflow and repeat the same procedure on different types of cancer and compare results side by side!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/download/\",\n        children: \"stable Orange release\"\n      }), \" will suffice for smaller data sets. For more options, check \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/examples/?tag=Survival+Analysis\",\n        children: \"the library of survival analysis workflows\"\n      }), \". You will find workflows that range from simple survival curve estimation to more complex analysis using Cox regression are readily available to experiment with.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Thats it! You can download and try \", _jsx(_components.a, {\n        href: \"/blog/2023-11-dask-survival-analysis/full-workflow.ows\",\n        children: \"the full workflow\"\n      }), \". Remember: for big data sets, use the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"experimental Orange version\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":1396,"height":854,"src":"/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dask-survival-analysis"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Thats it! You can download and try <a href="/blog/2023-11-dask-survival-analysis/full-workflow.ows">the full workflow</a>. Remember: for big data sets, use the <a href="https://github.com/biolab/orange3/wiki/Orange-with-Dask">experimental Orange version</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Jaka Kokošar","date":"2023-11-08","draft":false,"title":"From Data Portals to Portals of Doom: Avoiding it with Dask","url":"dask-survival-analysis","thumbImage":"orange-not-responding-thumb.png","frontPageImage":"orange-not-responding-thumb.png","blog":["dask","survival analysis"],"shortExcerpt":"Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source's authenticity and enabling custom workflows.","longExcerpt":"Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source's authenticity and enabling custom workflows."},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Since its inception, The Cancer Genome Atlas (\", _jsx(_components.a, {\n        href: \"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\",\n        children: \"TCGA\"\n      }), \") project has been a treasure trove of data for biomedical researchers. The data TCGA provides is essential for researchers who want to analyze potential biomarkers. But there's a hitch. Not every researcher is a data whisperer.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Luckily, there are several web-based tools that researchers can add to their toolkits. These tools make it easy to access and analyze survival data from TCGA. Notable examples are \", _jsx(_components.a, {\n        href: \"https://kmplot.com/analysis/\",\n        children: \"Kaplan-Meier Plotter\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"http://gepia.cancer-pku.cn/\",\n        children: \"GEPIA\"\n      }), \", among \", _jsx(_components.a, {\n        href: \"https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00068/full\",\n        children: \"others\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/gepia-example.png\",\n      width: \"2730\",\n      height: \"3122\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/gepia-example.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In their very essence, these tools allow you to enter a gene name and output the results of the analysis on pre-curated TCGA datasets. For example, in the image above, I have entered the gene name TP53 and selected the cancer type \\\"lower-grade glioma\\\" (TCGA-LGG). We see the Kaplan-Meier plot for the high- and low-risk patient groups and corresponding p-values, which quantify the difference in survival between the groups. For details, refer to a review paper by \", _jsx(_components.a, {\n        href: \"https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00068/full\",\n        children: \"Zheng and others\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Looks good; where is the catch? Well, we shouldn't blindly trust the underlying source of survival data of those web-based tools. Curated datasets between tools can vary; more in \", _jsx(_components.a, {\n        href: \"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8726696/\",\n        children: \"Idogawa et al.\"\n      }), \". And this is no small thing. Adding fuel to the fire here, I will let the reader guess how many of those tools are open-source.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Still, despite their limitations, these tools are useful. However, I would like to analyze data from sources I trust. Therefore, I have prepared \", _jsx(_components.a, {\n        href: \"https://github.com/jakakokosar/tcga-data\",\n        children: \"notebooks\"\n      }), \" that can download gene expression and survival data for all cancer types in the TCGA project. I see the irony here: I am not asking you to trust \", _jsx(_components.a, {\n        href: \"https://github.com/jakakokosar/tcga-data\",\n        children: \"my code\"\n      }), \"; if you can, verify it. Or, download from the official \", _jsx(_components.a, {\n        href: \"https://portal.gdc.cancer.gov/\",\n        children: \"GDC data portal\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The combined dataset from TCGA consists of approximately 10K samples and 60K gene expression values. The size of the file on my disk is around 3GB. You might think this is not much, but try loading it in Pandas. You will need around 30GB of system memory and 17 minutes just to read the file.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" pandas \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" pd\\n\\ndf = pd.read_csv(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'data/TCGA-combined.csv'\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# peak memory: 30722.37 MiB, increment: 30597.50 MiB\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead of coding, could we use any existing visual analytics tools? Kaplan-Meier plotter stands out because it allows me to upload my own data (a rare find among all the other tools). That is just what I was looking for!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/km-plotter-upload.png\",\n      width: \"1728\",\n      height: \"426\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/km-plotter-upload.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The option to upload my own data is cool, but there is a 4MB limitation. What about Orange?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/orange-not-responding.png\",\n      width: \"1762\",\n      height: \"1272\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Well, things quickly get out of hand. Orange typically handles files that are within the range of a few hundred megabytes just fine. This, of course, also depends on the specifications of the machine that Orange is running on. To support much larger files, we extended Orange with on-disk data support, where the complete data is never loaded into memory. We used Dask as the backend for the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"experimental Orange version\"\n      }), \". For more, refer to the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/dask-spectroscopy/\",\n        children: \"previous blog post\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that I can work with large datasets in Orange, I have a similar tool to those web-server-like ones: the difference is that I have to bring my own data and construct my analysis pipelines from scratch. But this is also advantageous: I am not limited to task-specific analyses that those other tools provide, and I can fully trust the data source.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here, I stratify lower-grade glioma patients (TCGA-LGG) into high- and low-risk groups according to the expression of gene TP53. Then, I use the Kaplan-Meier widget to compute and plot survival curves and test how significant the difference in survival between the groups is using the log-rank test. All this without my computer breaking a sweat!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/workflow.png\",\n      width: \"2450\",\n      height: \"1900\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With the workflow I constructed above, I could click through a list of genes and observe the results. But in my data, I have a lot of genes. Clicking so many becomes a tedious task. I could automate this process with the Rank Survival Features widget. The output is the ranked list of genes based on the p-value from the log-rank test.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-11-dask-survival-analysis/workflow2.png\",\n      width: \"2642\",\n      height: \"1574\",\n      src: \"/blog/2023-11-dask-survival-analysis/__optimized-images__/workflow2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And since we have the whole TCGA datasets loaded, I could branch the same workflow and repeat the same procedure on different types of cancer and compare results side by side!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/download/\",\n        children: \"stable Orange release\"\n      }), \" will suffice for smaller data sets. For more options, check \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/examples/?tag=Survival+Analysis\",\n        children: \"the library of survival analysis workflows\"\n      }), \". You will find workflows that range from simple survival curve estimation to more complex analysis using Cox regression are readily available to experiment with.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Thats it! You can download and try \", _jsx(_components.a, {\n        href: \"/blog/2023-11-dask-survival-analysis/full-workflow.ows\",\n        children: \"the full workflow\"\n      }), \". Remember: for big data sets, use the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/wiki/Orange-with-Dask\",\n        children: \"experimental Orange version\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":1396,"height":854,"src":"/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dask-survival-analysis"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Data Fusion Add-on for Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Data Fusion Add-on for Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-06-05 06:46:56+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="bioinformatics"/><meta property="article:tag" content="data-fusion"/><meta property="article:tag" content="orange3"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, bioinformatics, data-fusion, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Fusion Add-on for Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 05, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange is about to get even more exciting! We have created a prototype add-on for data fusion, which will certainly be of interest to many users. Data fusion brings large heterogeneous data sets together to create sensible clusters of related data instances and provides a platform for <strong>predictive modelling</strong> and <strong>recommendation systems</strong>.</p>
 <p>This widget set can be used either to recommend you the next movie to watch based on your demographic characteristics, movies you gave high scores to, your preferred genre, etc. or to suggest you a set of genes that might be relevant for a particular biological function or process. We envision the add-on to be useful for predictive modeling dealing with <strong>large heterogeneous data compendia</strong>, such as life sciences.</p>
 <p>The prototype set will be available for download next week, but we are happy to give you a sneak peek below.</p>
@@ -163,4 +163,4 @@
 <li>In <strong>Data Table</strong> we see the latent data matrix of Users. The algorithm infers low-dimensional user profiles by collective consideration of entire data collection, i.e. movie ratings and actor information. In our scenario the algorithm has  transformed 855 movie titles into 70 movie groupings, i.e. latent components.</li>
 </ol>
 <a href="/blog/2015-06-data-fusion-add-on-for-orange/__optimized-images__/fusion.png" data-gallery="true"><img alt="" loading="lazy" width="800" height="640" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-06-data-fusion-add-on-for-orange/__optimized-images__/fusion.png"/></a>
-<p>Data fusion visualized</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-06-05 06:46:56+00:00","draft":false,"title":"Data Fusion Add-on for Orange","blog":["addons","bioinformatics","data-fusion","orange3"],"oldUrl":"/blog/2015/06/05/data-fusion-add-on-for-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange is about to get even more exciting! 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+<p>Data fusion visualized</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-06-05 06:46:56+00:00","draft":false,"title":"Data Fusion Add-on for Orange","blog":["addons","bioinformatics","data-fusion","orange3"],"oldUrl":"/blog/2015/06/05/data-fusion-add-on-for-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange is about to get even more exciting! 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics, data-fusion, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Fusion Tutorial at the [BC]^2</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MARINKAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 08, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We are excited to host a three-hour tutorial on data fusion at the <a href="http://www.bc2.ch/2015/">Basel Computational Biology Conference</a>. To this end we have prepared a series of <a href="http://helikoid.si/bc215/bc2-handouts.pdf">short lectures notes</a> that accompany the recently developed <a href="/blog/2015/06/05/data-fusion-add-on-for-orange/">Data Fusion Add-on for Orange</a>.</p>
 <a href="/blog/2015-06-data-fusion-tutorial-at-the-bc2/__optimized-images__/scheme.png" data-gallery="true"><img alt="" loading="lazy" width="754" height="382" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-06-data-fusion-tutorial-at-the-bc2/__optimized-images__/scheme.png"/></a>
 <p>We design the tutorial for data mining researchers and molecular biologists with interest in large-scale data integration. In the tutorial we focus on collective latent factor models, a popular class of approaches for data fusion. We demonstrate the effectiveness of these approaches on several hands-on case studies from recommendation systems and molecular biology.</p>
 <p>This is a high-risk event. I mean, for us, lecturers. Ok, no bricks will probably fall down. But, in the part of the tutorial, this is the first time we are showing Orange&#x27;s data fusion add-on. And not just showing: part of the tutorial is a hands-on session.</p>
 <p>We would like to acknowledge <a href="http://www.biolab.si/en/">Biolab</a> members for pushing the widgets through the development pipeline under extreme time constraints. Special thanks to Anze, Ales, Jernej, Andrej, Marko, Aleksandar and all other members of the lab.</p>
-<p>This post was contributed by Marinka and Blaz.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARINKAZ","date":"2015-06-08 15:13:22+00:00","draft":false,"title":"Data Fusion Tutorial at the [BC]^2","blog":["bioinformatics","data-fusion","orange3"],"oldUrl":"/blog/2015/06/08/data-fusion-tutorial-at-the-bc2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We are excited to host a three-hour tutorial on data fusion at the \", _jsx(_components.a, {\n        href: \"http://www.bc2.ch/2015/\",\n        children: \"Basel Computational Biology Conference\"\n      }), \". 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+<p>This post was contributed by Marinka and Blaz.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARINKAZ","date":"2015-06-08 15:13:22+00:00","draft":false,"title":"Data Fusion Tutorial at the [BC]^2","blog":["bioinformatics","data-fusion","orange3"],"oldUrl":"/blog/2015/06/08/data-fusion-tutorial-at-the-bc2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We are excited to host a three-hour tutorial on data fusion at the \", _jsx(_components.a, {\n        href: \"http://www.bc2.ch/2015/\",\n        children: \"Basel Computational Biology Conference\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dataloading, performance</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data loading speedups</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MARKO</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 28, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange has been loading data faster since the end of February, especially if there are many attributes in the file.</p>
 <p>Quick comparisons between the old new versions, measured on my computer:</p>
 <ul>
@@ -162,4 +162,4 @@
 <li>reuse of a buffer for parsing,</li>
 <li>skipping type detection for attributes with known types, and</li>
 <li>by keeping attributes in a different data structure internally.</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-03-28 13:04:00+00:00","draft":false,"title":"Data loading speedups","blog":["dataloading","performance"],"oldUrl":"/blog/2011/03/28/data-loading-speedups/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange has been loading data faster since the end of February, especially if there are many attributes in the file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Quick comparisons between the old new versions, measured on my computer:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"adult.tab (32561 examples, 15 attributes): old version = 1.41s, new version = 0.86s.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"DLBCL.tab (77 examples, 7071 attributes): old version = 2.72s, new version = 0.93s.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"GDS1962.tab (104 examples, 31837 attributes): old version = 33.5s, new version = 6.6s.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The speedups were obtained with:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"reuse of a buffer for parsing,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"skipping type detection for attributes with known types, and\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"by keeping attributes in a different data structure internally.\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-loading-speedups"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-03-28 13:04:00+00:00","draft":false,"title":"Data loading speedups","blog":["dataloading","performance"],"oldUrl":"/blog/2011/03/28/data-loading-speedups/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange has been loading data faster since the end of February, especially if there are many attributes in the file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Quick comparisons between the old new versions, measured on my computer:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"adult.tab (32561 examples, 15 attributes): old version = 1.41s, new version = 0.86s.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"DLBCL.tab (77 examples, 7071 attributes): old version = 2.72s, new version = 0.93s.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"GDS1962.tab (104 examples, 31837 attributes): old version = 33.5s, new version = 6.6s.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The speedups were obtained with:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"reuse of a buffer for parsing,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"skipping type detection for attributes with known types, and\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"by keeping attributes in a different data structure internally.\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-loading-speedups"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/data-mining-and-machine-learning-for-economists/index.html b/blog/data-mining-and-machine-learning-for-economists/index.html
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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Data Mining and Machine Learning for Economists</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Data Mining and Machine Learning for Economists"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-07-17 09:20:50+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="education"/><meta property="article:tag" content="geo"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Data Mining and Machine Learning for Economists</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Data Mining and Machine Learning for Economists"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-07-17 09:20:50+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="education"/><meta property="article:tag" content="geo"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -172,4 +172,4 @@
 <p>Choropleth shows us which countries were in the selected cluster (red). We used Selected as attribute and colored by Mode.</p>
 <p>The final workflow:</p>
 <a href="/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-11.10.26.png" data-gallery="true"><img alt="" loading="lazy" width="1520" height="486" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-11.10.26.png"/></a>
-<p>We always try to keep our workshops fresh and interesting and visualizations are the best way to achieve this. Till the next workshop!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-07-17 09:20:50+00:00","draft":false,"title":"Data Mining and Machine Learning for Economists","blog":["addons","clustering","education","geo","workshop"],"oldUrl":"/blog/2018/07/17/data-mining-and-machine-learning-for-economists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    strong: \"strong\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Last week Blaž, Marko and I held a week long introductory Data Mining and Machine Learning course at the Ljubljana Doctoral Summer School 2018. We got a room full of dedicated students and we embarked on a journey through standard and advanced machine learning techniques, all presented of course in Orange. 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We will use Distances with Euclidean distance and use Ward linkage in Hierarchical Clustering.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-10.04.18.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"679\"\n      }), \"\\nIn Datasets widget we have selected the HDI data set.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-10.04.29.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"793\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The HDI data set contains information on 188 countries, which are described with 66 features. The data set can be used for regression, but we will perform clustering to discover countries, similar by the proposed parameters.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We got our results in a dendrogram. Interestingly, the United States seems similar to Cuba. Let us select this cluster and inspect what the most significant feature for this cluster. We will use the Data output of Hierarchical Clustering which append a column indicating whether the data instances was selected or not. Then we will use Box Plot, group by Selected and check Order by relevance. It seems like these countries have the longest life expectancy at age 59. Go ahead and inspect other clusters by yourself!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-10.52.05.png\",\n      alt: \"\",\n      width: \"1490\",\n      height: \"1206\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Select an interesting cluster in Hierarchical Clustering.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-10.10.28.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"887\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And inspect the results in a box plot. 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Remember to use the same output as before, that is Data to Data.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/Screen-Shot-2018-07-17-at-10.52.29.png\",\n      src: \"500\",\n      width: \"816\",\n      height: \"598\",\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-10.52.29.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Use Encode to convert a column with region identifiers (in our case Country) to latitude/longitude pairs.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, let us display these countries on a map with Choropleth widget. Beautiful. It is so easy to explore country data, when you see it on a map. You can try coloring also by HDI or any other feature.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-10.55.34.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"826\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Choropleth shows us which countries were in the selected cluster (red). We used Selected as attribute and colored by Mode.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The final workflow:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-07-data-mining-and-machine-learning-for-economists/__optimized-images__/Screen-Shot-2018-07-17-at-11.10.26.png\",\n      alt: \"\",\n      width: \"1520\",\n      height: \"486\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We always try to keep our workshops fresh and interesting and visualizations are the best way to achieve this. 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+<p>We always try to keep our workshops fresh and interesting and visualizations are the best way to achieve this. Till the next workshop!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-07-17 09:20:50+00:00","draft":false,"title":"Data Mining and Machine Learning for Economists","blog":["addons","clustering","education","geo","workshop"],"oldUrl":"/blog/2018/07/17/data-mining-and-machine-learning-for-economists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    strong: \"strong\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Last week Blaž, Marko and I held a week long introductory Data Mining and Machine Learning course at the Ljubljana Doctoral Summer School 2018. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, business intelligence, classification, education, examples, python, scripting, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining Course at Higher School of Economics, Moscow</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">May 03, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Janez and I have recently returned from a two-week stay in Moscow, Russian Federation, where we were teaching data mining to MA students of <a href="https://www.hse.ru/en/ma/sna/">Applied Statistics</a>. This is a new Master&#x27;s course that attracts the best students from different backgrounds and teaches them statistical methods for work in the industry.</p>
 <p>It was a real pleasure working at HSE. The students were proactive by asking questions and really challenged us to do our best.</p>
 <a href="/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/20180420_213316.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="730" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/20180420_213316.jpg"/></a>
@@ -212,4 +212,4 @@
 ax.set_ylabel(<span class="hljs-string">&#x27;Cost&#x27;</span>)
 </code></pre>
 <a href="/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/image-1.png" data-gallery="true"><img alt="" loading="lazy" width="411" height="266" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/image-1.png"/></a>
-<p>You can download the IPython Notebook here: [download id=&quot;2053&quot;].</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-03 11:37:28+00:00","draft":false,"title":"Data Mining Course at Higher School of Economics, Moscow","blog":["analysis","business intelligence","classification","education","examples","python","scripting","workshop"],"oldUrl":"/blog/2018/05/03/data-mining-course-at-higher-school-of-economics-moscow/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Janez and I have recently returned from a two-week stay in Moscow, Russian Federation, where we were teaching data mining to MA students of \", _jsx(_components.a, {\n        href: \"https://www.hse.ru/en/ma/sna/\",\n        children: \"Applied Statistics\"\n      }), \". This is a new Master's course that attracts the best students from different backgrounds and teaches them statistical methods for work in the industry.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It was a real pleasure working at HSE. The students were proactive by asking questions and really challenged us to do our best.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/20180420_213316.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"730\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One of the things we did was compute minimum cost of misclassifications. The story goes like this. Sara is a doctor and has data on 303 patients with \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" (Orange's heart-disease.tab data set). She used some classifiers and now has to decide how many patients to send for further tests. 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Now she wants to find out, how many patients to send for tests (what probability threshold to choose) so that her cost is the lowest.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, import all the libraries we will need:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" matplotlib.pyplot \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" plt\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Table\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.classification \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" NaiveBayesLearner, TreeLearner\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.evaluation \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" CrossValidation\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then load heart disease data (and print a sample).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"heart = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"heart_disease\\\"\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \"(heart[:\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \"])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, train classifiers and select probabilities of Naive Bayes for a patient being sick.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"scores = CrossValidation(heart, [NaiveBayesLearner(), TreeLearner()])\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#take probabilites of class 1 (sick) of NaiveBayesLearner\"\n        }), \"\\np1 = scores.probabilities[\", _jsx(_components.span, {\n          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_jsx(_components.p, {\n      children: \"Set counts, where we declare 0 patients being sick (threshold \u003e1).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"fp = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#start with threshold above 1 (no one is sick)\"\n        }), \"\\nfn = np.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"sum\"\n        }), \"(y)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For each threshold, compute the cost associated with each type of mistake.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"ps = []\\ncosts = []\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#compute costs of classifying i patients as sick\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" np.argsort(p1)[::-\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]:\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" y[i] == \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \":\\n        fp += \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"else\"\n        }), \":\\n        fn -= \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"\\n    ps.append(p1[i])\\n    costs.append(fp * fp_cost + fn * fn_cost)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the end, we get a list of probability thresholds and associated costs. 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+<p>You can download the IPython Notebook here: [download id=&quot;2053&quot;].</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-03 11:37:28+00:00","draft":false,"title":"Data Mining Course at Higher School of Economics, Moscow","blog":["analysis","business intelligence","classification","education","examples","python","scripting","workshop"],"oldUrl":"/blog/2018/05/03/data-mining-course-at-higher-school-of-economics-moscow/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Janez and I have recently returned from a two-week stay in Moscow, Russian Federation, where we were teaching data mining to MA students of \", _jsx(_components.a, {\n        href: \"https://www.hse.ru/en/ma/sna/\",\n        children: \"Applied Statistics\"\n      }), \". This is a new Master's course that attracts the best students from different backgrounds and teaches them statistical methods for work in the industry.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It was a real pleasure working at HSE. The students were proactive by asking questions and really challenged us to do our best.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/20180420_213316.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"730\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One of the things we did was compute minimum cost of misclassifications. The story goes like this. Sara is a doctor and has data on 303 patients with \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" (Orange's heart-disease.tab data set). She used some classifiers and now has to decide how many patients to send for further tests. Naive Bayes classifier, for example, returned probabilities of a patient being sick (column Naive Bayes 1). For each threshold in probabilites, she will compute how many false positives (patients declared sick when healthy) and how many false negatives (patients declared healthy when sick) a classifiers returns. Each mistake is associated with a cost. Now she wants to find out, how many patients to send for tests (what probability threshold to choose) so that her cost is the lowest.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, import all the libraries we will need:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" matplotlib.pyplot \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" plt\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Table\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.classification \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" NaiveBayesLearner, TreeLearner\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.evaluation \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" CrossValidation\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then load heart disease data (and print a sample).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"heart = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"heart_disease\\\"\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \"(heart[:\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \"])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, train classifiers and select probabilities of Naive Bayes for a patient being sick.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"scores = CrossValidation(heart, [NaiveBayesLearner(), TreeLearner()])\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#take probabilites of class 1 (sick) of NaiveBayesLearner\"\n        }), \"\\np1 = scores.probabilities[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"][:, \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#take actual class values\"\n        }), \"\\ny = scores.actual\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#cost of false positive (patient classified as sick when healthy)\"\n        }), \"\\nfp_cost = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"500\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#cost of false negative (patient classified as healthy when sick)\"\n        }), \"\\nfn_cost = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"800\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Set counts, where we declare 0 patients being sick (threshold \u003e1).\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"fp = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#start with threshold above 1 (no one is sick)\"\n        }), \"\\nfn = np.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"sum\"\n        }), \"(y)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For each threshold, compute the cost associated with each type of mistake.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"ps = []\\ncosts = []\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#compute costs of classifying i patients as sick\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" np.argsort(p1)[::-\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]:\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" y[i] == \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \":\\n        fp += \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"else\"\n        }), \":\\n        fn -= \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"\\n    ps.append(p1[i])\\n    costs.append(fp * fp_cost + fn * fn_cost)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the end, we get a list of probability thresholds and associated costs. Now let us find the minimum cost and its probability of a patient being sick.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"costs = np.array(costs)\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"#find probability of a patient being sick at lowest cost\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \"(ps[costs.argmin()])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This means the threshold that minimizes our cost for a given classifier is 0.620655. Sara would send all the patients with a probability of being sick higher or equal than 0.620655  for further tests.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At the end, we can also plot the cost to patients sent curve.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"fig, ax = plt.subplots()\\nplt.plot(ps, costs)\\nax.set_xlabel(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Patients sent'\"\n        }), \")\\nax.set_ylabel(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Cost'\"\n        }), \")\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-data-mining-course-at-higher-school-of-economics-moscow/__optimized-images__/image-1.png\",\n      alt: \"\",\n      width: \"411\",\n      height: \"266\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can download the IPython Notebook here: [download id=\\\"2053\\\"].\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-course-at-higher-school-of-economics-moscow"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, tutorial, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining Course in Houston #2</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 15, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This was already the second installment of Introduction to Data Mining Course at <a href="https://www.bcm.edu">Baylor College of Medicine in Houston, Texas</a>. Just <a href="/blog/2015/10/09/data-mining-course-in-houston/">like the last year</a>, the course was packed. About 50 graduate students, post-docs and a few faculty attended, making the course one of the largest elective PhD courses from over a hundred offered at this prestigious medical school.</p>
 <a href="/blog/2016-09-data-mining-in-houston-2/__optimized-images__/houston-class-2016.jpg" data-gallery="true"><img alt="" loading="lazy" width="960" height="720" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-09-data-mining-in-houston-2/__optimized-images__/houston-class-2016.jpg"/></a>
 <p>The course was designed for students with little or no experience in data science. It consisted of seven two-hour lectures, each followed by a homework assignment. We (Blaz and Janez) lectured on data visualization, classification, regression, clustering, data projection and image analytics. We paid special attention to the problems of overfitting, use of regularization, and proper ways of testing and scoring of modeling methods.</p>
 <p>The course was hands-on. The lectures were practical. They typically started with some data set and explained data mining techniques through designing data analysis workflows in Orange. Besides some standard machine learning and bioinformatics data sets, we have also <a href="/blog/2013/12/20/paint-your-data/">painted the data</a> to explore, say, the benefits of different classification techniques or design data sets where k-means clustering would fail.</p>
 <p>This year, the course benefited from several new Orange widgets. The recently published <a href="/blog/2016/08/12/interactive-k-means/">interactive k-means</a> widget was used to explain the inner working of this clustering algorithm, and polynomial classification widget was helpful in discussion of <a href="/blog/2016/08/16/polynomial-classification/">decision boundaries of classification algorithms</a>. <a href="/blog/2016/03/23/all-i-see-is-silhouette/">Silhouette plot</a> was used to show how to evaluate and explore the results of clustering. And finally, we explained concepts from deep learning using image embedding to show how already trained networks can be used for clustering and classification of images.</p>
-<a href="/blog/2016-09-data-mining-in-houston-2/__optimized-images__/image-analytics-cows.png" data-gallery="true"><img alt="" loading="lazy" width="1027" height="859" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-09-data-mining-in-houston-2/__optimized-images__/image-analytics-cows.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-09-15 13:38:30+00:00","draft":false,"title":"Data Mining Course in Houston #2","blog":["orange3","tutorial","workshop"],"oldUrl":"/blog/2016/09/15/data-mining-in-houston-2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This was already the second installment of Introduction to Data Mining Course at \", _jsx(_components.a, {\n        href: \"https://www.bcm.edu\",\n        children: \"Baylor College of Medicine in Houston, Texas\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dataloading, education, orange3, visualization, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining Course in Houston</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 09, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We have just completed an Introduction to Data Mining, a graduate course at <a href="https://www.bcm.edu">Baylor College of Medicine</a> in Texas, Houston. The course was given in September and consisted of seven two-hour lectures, each one followed with a homework assignment. The course was attended by about 40 students and some faculty and research staff.</p>
 <a href="/blog/2015-10-data-mining-course-in-houston/__optimized-images__/dm-course-baylor.jpg" data-gallery="true"><img alt="" loading="lazy" width="1643" height="800" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-data-mining-course-in-houston/__optimized-images__/dm-course-baylor.jpg"/></a>
 <p>This was a challenging course. The audience was new to data mining, and we decided to teach them with the newest, third version of Orange. We also experimented with two course instructors (Blaz and Janez), who, instead of splitting the course into two parts, taught simultaneously, one on the board and the other one helping the students with hands-on exercises. To check whether this worked fine, we ran a student survey at the end of the course. We used <a href="https://github.com/biolab/orange3-prototypes">Google Sheets</a> and then examined the results with students in the class. Using Orange, of course.</p>
@@ -158,4 +158,4 @@
 <a href="/blog/2015-10-data-mining-course-in-houston/__optimized-images__/bcm-course-toafriend.png" data-gallery="true"><img alt="" loading="lazy" width="772" height="528" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-data-mining-course-in-houston/__optimized-images__/bcm-course-toafriend.png"/></a>
 <p>and the teaching style.</p>
 <a href="/blog/2015-10-data-mining-course-in-houston/__optimized-images__/bcm-course-teachingstyle.png" data-gallery="true"><img alt="" loading="lazy" width="772" height="528" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-data-mining-course-in-houston/__optimized-images__/bcm-course-teachingstyle.png"/></a>
-<p>The course took advantage of several new widgets in Orange 3, including those for data preprocessing and polynomial regression. The core development team put a lot of effort during the summer to debug and polish this newest version of Orange. Also thanks to the financial support by <a href="http://axleproject.eu/">AXLE EU FP7</a> and <a href="http://cordis.europa.eu/project/rcn/94265_en.html">CARE-MI EU FP7</a>** grants and grants by the <a href="https://www.arrs.gov.si/en/novo.asp">Slovene Research agency</a>, we were able to finish everything in time.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2015-10-09 06:51:23+00:00","draft":false,"title":"Data Mining Course in Houston","blog":["dataloading","education","orange3","visualization","workshop"],"oldUrl":"/blog/2015/10/09/data-mining-course-in-houston/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have just completed an Introduction to Data Mining, a graduate course at \", _jsx(_components.a, {\n        href: \"https://www.bcm.edu\",\n        children: \"Baylor College of Medicine\"\n      }), \" in Texas, Houston. 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+<p>The course took advantage of several new widgets in Orange 3, including those for data preprocessing and polynomial regression. The core development team put a lot of effort during the summer to debug and polish this newest version of Orange. Also thanks to the financial support by <a href="http://axleproject.eu/">AXLE EU FP7</a> and <a href="http://cordis.europa.eu/project/rcn/94265_en.html">CARE-MI EU FP7</a>** grants and grants by the <a href="https://www.arrs.gov.si/en/novo.asp">Slovene Research agency</a>, we were able to finish everything in time.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2015-10-09 06:51:23+00:00","draft":false,"title":"Data Mining Course in Houston","blog":["dataloading","education","orange3","visualization","workshop"],"oldUrl":"/blog/2015/10/09/data-mining-course-in-houston/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have just completed an Introduction to Data Mining, a graduate course at \", _jsx(_components.a, {\n        href: \"https://www.bcm.edu\",\n        children: \"Baylor College of Medicine\"\n      }), \" in Texas, Houston. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>covid-19, feature construction, line plot</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining COVID-19 Epidemics: Part 1</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 02, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>These days we are all following the statistics of COVID-19, looking at how our own country is faring and how it&#x27;s comparing with other countries. Luckily, only a few have a statistically meaningful number of deaths (which solemnly reminds us of the difference between statistical and practical significance!), so we concentrate on the number of confirmed cases.</p>
 <p>You&#x27;re reading the first and most basic blog post from a series in which we will investigate this data using Orange.
 <strong>Most</strong> people are capable of doing <strong>something</strong> in Excel(-like programs), and <strong>some</strong> can do <strong>everything</strong> in Python with pandas and jupyter. I&#x27;ll show you how <strong>many</strong> people can do <strong>many</strong> things in Orange.</p>
@@ -263,4 +263,4 @@ <h2>Where from here?</h2>
 <p>This data is related to countries. Hence it would be nice to put it on a map. It also deals with time, and core Orange is not well-equipped for it. In the two follow-up posts, we will explore the add-ons for geographical data and for time series.</p>
 <hr/>
 <p>Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. <a href="https://orange.biolab.si/download">Download Orange</a>, and load and explore your own data sets!</p>
-<p>In addition to a variety of learning materials posted online in the form of <a href="https://orange.biolab.si/blog/">blog posts</a>, <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">tutorial videos</a>, we&#x27;ve created a <a href="https://discord.gg/FWrfeXV">Discord server. Join the community, tell us what you think!</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2020-04-02","draft":false,"title":"Data Mining COVID-19 Epidemics: Part 1","thumbImage":"2020-04-02-europe-scatter-line-plot.png","frontPageImage":"2020-04-02-thumb.png","blog":["covid-19","feature construction","line plot"],"shortExcerpt":"Basic tricks for loading and analysing COVID-19 data.","longExcerpt":"Basic tricks for loading and analysing COVID-19 data.","x2images":true,"oldUrl":"/blog/2020/2020-04-02-covid-19-basic/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\",\n    code: \"code\",\n    pre: \"pre\",\n    span: \"span\",\n    hr: \"hr\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot, WindowScreenshot, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"These days we are all following the statistics of COVID-19, looking at how our own country is faring and how it's comparing with other countries. Luckily, only a few have a statistically meaningful number of deaths (which solemnly reminds us of the difference between statistical and practical significance!), so we concentrate on the number of confirmed cases.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You're reading the first and most basic blog post from a series in which we will investigate this data using Orange.\\n\", _jsx(_components.strong, {\n        children: \"Most\"\n      }), \" people are capable of doing \", _jsx(_components.strong, {\n        children: \"something\"\n      }), \" in Excel(-like programs), and \", _jsx(_components.strong, {\n        children: \"some\"\n      }), \" can do \", _jsx(_components.strong, {\n        children: \"everything\"\n      }), \" in Python with pandas and jupyter. I'll show you how \", _jsx(_components.strong, {\n        children: \"many\"\n      }), \" people can do \", _jsx(_components.strong, {\n        children: \"many\"\n      }), \" things in Orange.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Today, we will see how to get this data into Orange, draw some basic curves, and relate it to other data sources. Don't expect anything dramatic; this will be more about showing some creative ways of connecting a few widgets, and starting to explore the data about COVID-19 epidemics.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Getting the data\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"John Hopkins University collated some COVID-19 information in a machine-readable format and published it \", _jsx(_components.a, {\n        href: \"https://github.com/CSSEGISandData/COVID-19\",\n        children: \"on Github\"\n      }), \". We will examine the table with \", _jsx(_components.a, {\n        href: \"https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",\n        children: \"confirmed cases by regions and countries\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To get the numbers behind the above table, click \\\"Raw\\\". Or this link: \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",\n        children: \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n      }), \". Save the page and you'll have a file to play with.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-01.png\",\n      width: \"170\",\n      height: \"212\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-01.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Easier still, Orange's File widget can load the data directly from the web, and handles a basic .csv file (for more fine-grained options, use CSV File Import).\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-file-widget.png\",\n      width: \"1280\",\n      height: \"1070\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-file-widget.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, for starters, add the File widget to the canvas and copy the above link to the URL field. You can then connect it to a Data Table widget and check that everything's loaded OK. You should see a table with rows corresponding to regions and countries, and columns corresponding to dates, with two additional columns detailing region locations (latitude, longitude).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Note that this is live data, so your results will differ from those we show here.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Plot the usual plots\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-02.png\",\n      width: \"368\",\n      height: \"160\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-02.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Connect the File to a Line Plot widget to see the graph. By default, Line Plot shows means and ranges, while we are interested in raw lines. Let's click the checkboxes accordingly.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-lineplot.png\",\n      width: \"1706\",\n      height: \"1110\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-lineplot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pretty boring. Anybody can do this in Excel. Orange allows us to play with these curves, though.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-line.gif\",\n      width: \"1710\",\n      height: \"1120\",\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-line.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The curve that grows rapidly but then flattens out -- is probably China, right? To check this, select it. It may be hard to click the curve, so select it by dragging a line across it.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-03.png\",\n      width: \"602\",\n      height: \"164\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-03.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then, connect the Line Plot widget to Data Table, and it will show the data for this curve. You'll learn that this is, of course, the Hubei province – where it all started.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-steep-curves.png\",\n      width: \"1468\",\n      height: \"852\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-steep-curves.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What are the fast-rising curves? Select them by dragging a line across them, and check the Data Table widget: these are the US, Iran, Italy, Spain, Germany and France.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-04.png\",\n      width: \"584\",\n      height: \"266\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-04.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can do it the other way around: connect File to Data Table and Data Table to Line Plot, such that the Data Table sends its selected subset of data.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-country.png\",\n      width: \"1342\",\n      height: \"852\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-select-country.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the Data Table, you can now find and click your favourite country. Line Plot will now highlight the curve for Slovenia (or whichever country you chose). You can also select multiple countries, like all the Chinese provinces. Or all European countries.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Well, the latter is a bit difficult; there is no data about the continent. There's a trick, though.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-05.png\",\n      width: \"592\",\n      height: \"290\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-05.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Replace the Data Table widget with a Scatter Plot widget, and choose Long and Lat for x and y respectively. Zoom in on Europe and select the points by dragging across them. Line Plot will highlight the curves belonging to the countries chosen in the Scatter Plot.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-europe-scatter-line-plot.png\",\n      width: \"2644\",\n      height: \"1606\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-europe-scatter-line-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the case of Slovenia (and possibly your choice as well), we couldn't really see its curve because it's negligent in comparison with others. The only thing we've learned is that the 327-million-man US have more cases of COVID-19 than a 2-million-man Slovenia. The same goes for Chinese provinces: the only one that sticks out is Hubei. Also, the \\\"map\\\" we improvised in Scatter Plot was a poor-man's map. Orange has proper geographical support (Orange3-Geo add-on), we'll look at it in-depth in a follow-up post.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The message here regards the essential difference between plotting curves and real data mining: static curves are dead; data mining is \", _jsx(_components.strong, {\n        children: \"interactive\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Connecting data sets\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let's solve the problem of negligible curves for small countries. This is not just about populations: the biggest problem is that confirmed cases are the result of testing, and testing strategies vary by country. Icelanders and Koreans test like crazy, while the number of tests in the US was (initially) so small it was comparable to that of Slovenia (and with it, the number of confirmed cases). The number of tests could thus \", _jsx(_components.strong, {\n        children: \"normalize\"\n      }), \" the number of confirmed cases, but this wouldn't do either; the testing isn't equivalent – some countries test more at random, while others save tests for groups at greater risk. And regardless, exact data about the number of tests is not readily available.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, let's fallback to the number of cases per million inhabitants. Neither this nor the population of countries are present in the data provided by John Hopkins University. We do, however, have another data set at arm's reach: the population of countries (for year 2015) appears in the Worldbank's Human development index (HDI) data.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-01.png\",\n      width: \"162\",\n      height: \"194\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-01.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To load it, use the Datasets widget, find and double-click the HDI data set.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-merge-data.png\",\n      width: \"903\",\n      height: \"370\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-merge-data.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we're working with two data sets.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-02.png\",\n      width: \"406\",\n      height: \"366\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-02.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's merge them using the Merge Data widget: connect both the File widget (with the John Hopkins data), and the Datasets widget (with the HDI data) to Merge Data. Make sure to match the \\\"Country/Region\\\" feature in COVID-19 data with the \\\"Country\\\" feature in HDI. It is also important to connect the widgets in this order, so File provides the main data and the Datasets widget augments it with additional annotation columns. If you do it incorrectly, double-click the connections to fix it.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The match is imperfect: some countries appear with different names, for instance, \\\"\", _jsx(_components.em, {\n        children: \"Russia\"\n      }), \"\\\" from John Hopkins doesn't match the \\\"\", _jsx(_components.em, {\n        children: \"Russian Federation\"\n      }), \"\\\" from HDI, and the John Hopkin's \\\"\", _jsx(_components.em, {\n        children: \"US\"\n      }), \"\\\" and doesn't match HDI's \\\"\", _jsx(_components.em, {\n        children: \"United States\"\n      }), \"\\\".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The population of Liechtenstein in millions with one decimal is 0.0; similar for Palau.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-03.png\",\n      width: \"658\",\n      height: \"354\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-03.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To remove countries with unknown or zero population, we continue with Select Rows, where we set the condition \\\"Total Population (millions) 2015 is greater than 0\\\".\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-rows.png\",\n      width: \"1700\",\n      height: \"546\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-select-rows.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now for some constructive business.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-04.png\",\n      width: \"942\",\n      height: \"356\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-04.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To get the number of cases on some particular day, we use the Feature Constructor widget, where we can input a formula to compute new data columns. In \\\"New\\\" we select Numeric, type \", _jsx(_components.code, {\n        children: \"cases per million\"\n      }), \" as the name of the new column, Select Feature \\\"3_29_20\\\" (or whichever date we'd like), add \", _jsx(_components.code, {\n        children: \"/\"\n      }), \", and then select \\\"Total_Population__millions__2015\\\".\\nAs a shortcut, you can just copy \", _jsx(_components.code, {\n        children: \"_3_29_20 / Total_Population__millions__2015\"\n      }), \" into the \\\"Expression...\\\" line. (Note that an underscore precedes \", _jsx(_components.code, {\n        children: \"3_29_20\"\n      }), \": this way Orange knows that this is not a number but a name of a column.)\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-construct-features.png\",\n      width: \"706\",\n      height: \"290\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-construct-features.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Things got crowded, so perhaps we can remove some columns.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-05.png\",\n      width: \"1160\",\n      height: \"356\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-05.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We add Select Columns, open it, and remove all columns we don't need right now. We can just select all features (Ctrl-A or Cmd-A) and drag them to the left, and then drag \\\"cases per million\\\" back to features.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-columns.png\",\n      width: \"998\",\n      height: \"986\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-select-columns.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, add and connect a Data Table.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-1-day.png\",\n      width: \"1278\",\n      height: \"416\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-1-day.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sort by the last column and, voilá, you've ranked countries the number of cases per a million inhabitants.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-1-day-table.png\",\n      width: \"264\",\n      height: \"605\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-1-day-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Missing in action: China. The data for China is split into provinces, yet every row is divided by the country's entire population. France seems to have a similar problem: it is given as a single country, but followed by external territories whose numbers are divided by the entire population. Also missing in action: Russia / Russian Federation, and US / United States, as we've previously explained.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Fixing these glitches will require some manual work.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Editing the data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To fix the non-unique country names, we could open one of the two files in Excel and fix discrepancies, but there's no need for it: this is easier done in Orange, which will help us identify the missing countries. They are removed in the Select Rows widget. We hence connect a Data Table to Select Rows, double click the connection and do some editing: click the line between Matching Data and Data to remove it, and drag a line from Unmatched Data to Data.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-rows-link-edit.gif\",\n      width: \"1830\",\n      height: \"1004\",\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-rows-link-edit.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This way, we can see which countries were filtered out. They seem to be absent due to either not being present in HDI, or having too small of a population.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's insert an Edit Domain widget between the Datasets widget and Merge Data.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-06.png\",\n      width: \"1566\",\n      height: \"436\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-06.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We find and select column \", _jsx(_components.code, {\n        children: \"Country\"\n      }), \". Now change its type from Text to Categorical. Note, this is conceptually wrong: \\\"\", _jsx(_components.em, {\n        children: \"Country\"\n      }), \"\\\" \", _jsx(_components.em, {\n        children: \"is\"\n      }), \" a text variable, it contains a name and not a \\\"category\\\" of country, like the continent or the direction of writing. However, Edit Domain only lets us map its values if we change it to Categorical. And having it as categorical won't hurt.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-edit-domain-hdi.png\",\n      width: \"641\",\n      height: \"502\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-edit-domain-hdi.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"After changing the variable type, the widget will show another box, Values, with a mapping of values. Let's look at the countries in the new excluded rows Data Table widget. We see, for instance, \\\"Bosnia and Herzegovina\\\". We need to match to how John Hopkins data refers to countries, so go back to Edit Domain, and find \\\"Bosnia and Herz.\\\". Double click it and change it to \\\"Bosnia and Herzegovina\\\".\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Similarly, Czech Rep. should become Czechia, Macedonia is now officially North Macedonia, Russian Federation is Russia, United States is US and Viet Nam is Vietnam. John Hopkins has \\\"Korea, South\\\" and HDI includes just Korea. Based on its high human development index and GDP, we can safely assume that this is the northern one. (Kidding, map this Korea to Korea, South, of course. There's no virus in the other Korea because Kim anticipated it before it even appeared and already invented a cure long ago.)\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now that I've taught you how to fish, I give you a fish for today: after editing these and all the other countries whose matches I found, I saved it to a \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/datasets/HDI-fixed-for-jhu-covid.csv\",\n        children: \"file you can download and use\"\n      }), \". Replace the Datasets widget with a File, and load it.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Analysis beyond population\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Comparing this data with the World Bank's data is great because it allows us to relate the epidemiological data to data about particular countries.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-07.png\",\n      width: \"1388\",\n      height: \"376\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-07.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Select Columns, we removed most of the features. Bring back all those that come from HDI. Now connect, say, a Scatter Plot and observe the relation between the number of cases per million and the number of physicians per ten thousand, which is the closest approximation to the capabilities of health systems.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-scatter-physicians.png\",\n      width: \"2076\",\n      height: \"1110\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-scatter-physicians.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The conclusion here would be that you may want to find yourself in Qatar, Cuba or Greece right now. People are often surprised to see Cuba in such contexts, though Cuba has an excellent public health system and pretty good (though perhaps not entirely unbiased) free education. Seeing this graph, it shouldn't come as a surprise that \", _jsx(_components.a, {\n        href: \"https://www.reuters.com/article/us-health-coronavirus-cuba/cuban-doctors-head-to-italy-to-battle-coronavirus-idUSKBN219051\",\n        children: \"Cuban doctors are heading to Italy and other countries to help fight the epidemics\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A seemingly related measure, Public health expenditure doesn't work here. It is expressed as a percentage of GDP, so it is adjusted for physicians' salaries, but not necessarily for the prices of (imported) equipment. Furthermore, higher spending may reflect a more expensive but not necessarily a more efficient health system.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another potentially interesting factor to follow will be the age of the population. HDI tells us the number of people older than 65 years per 100 people between 15 and 64.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-scatter-dependency.png\",\n      width: \"2076\",\n      height: \"1110\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-scatter-dependency.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I should have used future tense; the population age \", _jsx(_components.strong, {\n        children: \"will\"\n      }), \" be interesting to observe with respect to mortality rate, but not enough countries have a sufficient number of deaths. Let's hope it stays this way.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will eventually be able to (or -- let's hope we won't) explore the relations between epidemics and other sociodemographic data. Generally, the problem with analyzing this data per country is currently that the disease has mostly spread (or been detected?) in developed countries, resulting in many correlations like the speed of spreading and percentage of urban populations, which may or may not reflect actual causation.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Back to curves\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What about curves showing the number of confirmed cases per million? We computed the cases per million for a single day, not for all days. While Orange is a general-purpose data mining tool, some operations are too specific to be implemented in widgets. For such cases, we can program some processing in Python, using the Python Script widget.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-03-01.png\",\n      width: \"1306\",\n      height: \"342\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-03-01.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The start of the sequence is the same as before: we merge the two data sources and remove countries with unknown or zero population.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Select Columns, we move all variables except daily data from features to metas.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Python Script, we type the following program and run it:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\nnormalized = in_data.X / in_data[:, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Total Population (millions) 2015\\\"\"\n        }), \"].metas\\n\\nout_data = Orange.data.Table.from_numpy(\\n    in_data.domain,\\n    normalized, in_data.Y, in_data.metas)\\n\"]\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-python-script.png\",\n      width: \"1306\",\n      height: \"1272\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-python-script.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"With \", _jsx(_components.code, {\n        children: \"in_data[:, \\\"Total Population (millions) 2015\\\"]\"\n      }), \" we take the table corresponding to the column with the population. Since population is now a meta attribute, we take \", _jsx(_components.code, {\n        children: \".metas\"\n      }), \". We divide \", _jsx(_components.code, {\n        children: \"in_data.X\"\n      }), \" with this column. Finally, we construct a new table named \", _jsx(_components.code, {\n        children: \"out_data\"\n      }), \", with the same domain (that is, the same variables), the normalized data, and the original data for target variable(s) and metas.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We get a similar line plot as before, just with proper scale. Observe it. There are two countries with 3000-4000 confirmed cases per million?! Which? Check yourself!\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-lineplot-3.png\",\n      width: \"1194\",\n      height: \"956\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-lineplot-3.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Would you like to see a plot with a logarithmic axis? And Line Plot doesn't support it? (Yet?) The beauty of using the Python script widget is that we can transform the data any way we want. Change the line that computes the normalized data to\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"normalized = np.log10(in_data.X / in_data[:, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Total Population (millions) 2015\\\"\"\n        }), \"].metas)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And we have a logarithmic axis (just with wrong labels).\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Where from here?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This data is related to countries. Hence it would be nice to put it on a map. It also deals with time, and core Orange is not well-equipped for it. In the two follow-up posts, we will explore the add-ons for geographical data and for time series.\"\n    }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download\",\n        children: \"Download Orange\"\n      }), \", and load and explore your own data sets!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In addition to a variety of learning materials posted online in the form of \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/\",\n        children: \"blog posts\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"tutorial videos\"\n      }), \", we've created a \", _jsx(_components.a, {\n        href: \"https://discord.gg/FWrfeXV\",\n        children: \"Discord server. Join the community, tell us what you think!\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":2644,"height":1606,"src":"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-europe-scatter-line-plot.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-covid-19-epidemics-part-1"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>In addition to a variety of learning materials posted online in the form of <a href="https://orange.biolab.si/blog/">blog posts</a>, <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">tutorial videos</a>, we&#x27;ve created a <a href="https://discord.gg/FWrfeXV">Discord server. Join the community, tell us what you think!</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2020-04-02","draft":false,"title":"Data Mining COVID-19 Epidemics: Part 1","thumbImage":"2020-04-02-europe-scatter-line-plot.png","frontPageImage":"2020-04-02-thumb.png","blog":["covid-19","feature construction","line plot"],"shortExcerpt":"Basic tricks for loading and analysing COVID-19 data.","longExcerpt":"Basic tricks for loading and analysing COVID-19 data.","x2images":true,"oldUrl":"/blog/2020/2020-04-02-covid-19-basic/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\",\n    code: \"code\",\n    pre: \"pre\",\n    span: \"span\",\n    hr: \"hr\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot, WindowScreenshot, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"These days we are all following the statistics of COVID-19, looking at how our own country is faring and how it's comparing with other countries. Luckily, only a few have a statistically meaningful number of deaths (which solemnly reminds us of the difference between statistical and practical significance!), so we concentrate on the number of confirmed cases.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You're reading the first and most basic blog post from a series in which we will investigate this data using Orange.\\n\", _jsx(_components.strong, {\n        children: \"Most\"\n      }), \" people are capable of doing \", _jsx(_components.strong, {\n        children: \"something\"\n      }), \" in Excel(-like programs), and \", _jsx(_components.strong, {\n        children: \"some\"\n      }), \" can do \", _jsx(_components.strong, {\n        children: \"everything\"\n      }), \" in Python with pandas and jupyter. I'll show you how \", _jsx(_components.strong, {\n        children: \"many\"\n      }), \" people can do \", _jsx(_components.strong, {\n        children: \"many\"\n      }), \" things in Orange.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Today, we will see how to get this data into Orange, draw some basic curves, and relate it to other data sources. Don't expect anything dramatic; this will be more about showing some creative ways of connecting a few widgets, and starting to explore the data about COVID-19 epidemics.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Getting the data\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"John Hopkins University collated some COVID-19 information in a machine-readable format and published it \", _jsx(_components.a, {\n        href: \"https://github.com/CSSEGISandData/COVID-19\",\n        children: \"on Github\"\n      }), \". We will examine the table with \", _jsx(_components.a, {\n        href: \"https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",\n        children: \"confirmed cases by regions and countries\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To get the numbers behind the above table, click \\\"Raw\\\". Or this link: \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",\n        children: \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n      }), \". Save the page and you'll have a file to play with.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-01.png\",\n      width: \"170\",\n      height: \"212\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-01.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Easier still, Orange's File widget can load the data directly from the web, and handles a basic .csv file (for more fine-grained options, use CSV File Import).\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-file-widget.png\",\n      width: \"1280\",\n      height: \"1070\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-file-widget.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, for starters, add the File widget to the canvas and copy the above link to the URL field. You can then connect it to a Data Table widget and check that everything's loaded OK. You should see a table with rows corresponding to regions and countries, and columns corresponding to dates, with two additional columns detailing region locations (latitude, longitude).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Note that this is live data, so your results will differ from those we show here.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Plot the usual plots\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-02.png\",\n      width: \"368\",\n      height: \"160\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-02.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Connect the File to a Line Plot widget to see the graph. By default, Line Plot shows means and ranges, while we are interested in raw lines. Let's click the checkboxes accordingly.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-lineplot.png\",\n      width: \"1706\",\n      height: \"1110\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-lineplot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pretty boring. Anybody can do this in Excel. Orange allows us to play with these curves, though.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-line.gif\",\n      width: \"1710\",\n      height: \"1120\",\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-line.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The curve that grows rapidly but then flattens out -- is probably China, right? To check this, select it. It may be hard to click the curve, so select it by dragging a line across it.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-03.png\",\n      width: \"602\",\n      height: \"164\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-03.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then, connect the Line Plot widget to Data Table, and it will show the data for this curve. You'll learn that this is, of course, the Hubei province – where it all started.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-steep-curves.png\",\n      width: \"1468\",\n      height: \"852\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-steep-curves.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What are the fast-rising curves? Select them by dragging a line across them, and check the Data Table widget: these are the US, Iran, Italy, Spain, Germany and France.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-04.png\",\n      width: \"584\",\n      height: \"266\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-04.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can do it the other way around: connect File to Data Table and Data Table to Line Plot, such that the Data Table sends its selected subset of data.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-country.png\",\n      width: \"1342\",\n      height: \"852\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-select-country.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the Data Table, you can now find and click your favourite country. Line Plot will now highlight the curve for Slovenia (or whichever country you chose). You can also select multiple countries, like all the Chinese provinces. Or all European countries.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Well, the latter is a bit difficult; there is no data about the continent. There's a trick, though.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-01-05.png\",\n      width: \"592\",\n      height: \"290\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-01-05.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Replace the Data Table widget with a Scatter Plot widget, and choose Long and Lat for x and y respectively. Zoom in on Europe and select the points by dragging across them. Line Plot will highlight the curves belonging to the countries chosen in the Scatter Plot.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-europe-scatter-line-plot.png\",\n      width: \"2644\",\n      height: \"1606\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-europe-scatter-line-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the case of Slovenia (and possibly your choice as well), we couldn't really see its curve because it's negligent in comparison with others. The only thing we've learned is that the 327-million-man US have more cases of COVID-19 than a 2-million-man Slovenia. The same goes for Chinese provinces: the only one that sticks out is Hubei. Also, the \\\"map\\\" we improvised in Scatter Plot was a poor-man's map. Orange has proper geographical support (Orange3-Geo add-on), we'll look at it in-depth in a follow-up post.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The message here regards the essential difference between plotting curves and real data mining: static curves are dead; data mining is \", _jsx(_components.strong, {\n        children: \"interactive\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Connecting data sets\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let's solve the problem of negligible curves for small countries. This is not just about populations: the biggest problem is that confirmed cases are the result of testing, and testing strategies vary by country. Icelanders and Koreans test like crazy, while the number of tests in the US was (initially) so small it was comparable to that of Slovenia (and with it, the number of confirmed cases). The number of tests could thus \", _jsx(_components.strong, {\n        children: \"normalize\"\n      }), \" the number of confirmed cases, but this wouldn't do either; the testing isn't equivalent – some countries test more at random, while others save tests for groups at greater risk. And regardless, exact data about the number of tests is not readily available.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, let's fallback to the number of cases per million inhabitants. Neither this nor the population of countries are present in the data provided by John Hopkins University. We do, however, have another data set at arm's reach: the population of countries (for year 2015) appears in the Worldbank's Human development index (HDI) data.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-01.png\",\n      width: \"162\",\n      height: \"194\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-01.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To load it, use the Datasets widget, find and double-click the HDI data set.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-merge-data.png\",\n      width: \"903\",\n      height: \"370\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-merge-data.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we're working with two data sets.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-02.png\",\n      width: \"406\",\n      height: \"366\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-02.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's merge them using the Merge Data widget: connect both the File widget (with the John Hopkins data), and the Datasets widget (with the HDI data) to Merge Data. Make sure to match the \\\"Country/Region\\\" feature in COVID-19 data with the \\\"Country\\\" feature in HDI. It is also important to connect the widgets in this order, so File provides the main data and the Datasets widget augments it with additional annotation columns. If you do it incorrectly, double-click the connections to fix it.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The match is imperfect: some countries appear with different names, for instance, \\\"\", _jsx(_components.em, {\n        children: \"Russia\"\n      }), \"\\\" from John Hopkins doesn't match the \\\"\", _jsx(_components.em, {\n        children: \"Russian Federation\"\n      }), \"\\\" from HDI, and the John Hopkin's \\\"\", _jsx(_components.em, {\n        children: \"US\"\n      }), \"\\\" and doesn't match HDI's \\\"\", _jsx(_components.em, {\n        children: \"United States\"\n      }), \"\\\".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The population of Liechtenstein in millions with one decimal is 0.0; similar for Palau.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-03.png\",\n      width: \"658\",\n      height: \"354\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-03.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To remove countries with unknown or zero population, we continue with Select Rows, where we set the condition \\\"Total Population (millions) 2015 is greater than 0\\\".\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-rows.png\",\n      width: \"1700\",\n      height: \"546\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-select-rows.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now for some constructive business.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-04.png\",\n      width: \"942\",\n      height: \"356\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-04.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To get the number of cases on some particular day, we use the Feature Constructor widget, where we can input a formula to compute new data columns. In \\\"New\\\" we select Numeric, type \", _jsx(_components.code, {\n        children: \"cases per million\"\n      }), \" as the name of the new column, Select Feature \\\"3_29_20\\\" (or whichever date we'd like), add \", _jsx(_components.code, {\n        children: \"/\"\n      }), \", and then select \\\"Total_Population__millions__2015\\\".\\nAs a shortcut, you can just copy \", _jsx(_components.code, {\n        children: \"_3_29_20 / Total_Population__millions__2015\"\n      }), \" into the \\\"Expression...\\\" line. (Note that an underscore precedes \", _jsx(_components.code, {\n        children: \"3_29_20\"\n      }), \": this way Orange knows that this is not a number but a name of a column.)\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-construct-features.png\",\n      width: \"706\",\n      height: \"290\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-construct-features.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Things got crowded, so perhaps we can remove some columns.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-05.png\",\n      width: \"1160\",\n      height: \"356\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-05.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We add Select Columns, open it, and remove all columns we don't need right now. We can just select all features (Ctrl-A or Cmd-A) and drag them to the left, and then drag \\\"cases per million\\\" back to features.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-columns.png\",\n      width: \"998\",\n      height: \"986\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-select-columns.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, add and connect a Data Table.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-1-day.png\",\n      width: \"1278\",\n      height: \"416\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-1-day.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sort by the last column and, voilá, you've ranked countries the number of cases per a million inhabitants.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-1-day-table.png\",\n      width: \"264\",\n      height: \"605\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-1-day-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Missing in action: China. The data for China is split into provinces, yet every row is divided by the country's entire population. France seems to have a similar problem: it is given as a single country, but followed by external territories whose numbers are divided by the entire population. Also missing in action: Russia / Russian Federation, and US / United States, as we've previously explained.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Fixing these glitches will require some manual work.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Editing the data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To fix the non-unique country names, we could open one of the two files in Excel and fix discrepancies, but there's no need for it: this is easier done in Orange, which will help us identify the missing countries. They are removed in the Select Rows widget. We hence connect a Data Table to Select Rows, double click the connection and do some editing: click the line between Matching Data and Data to remove it, and drag a line from Unmatched Data to Data.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-rows-link-edit.gif\",\n      width: \"1830\",\n      height: \"1004\",\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-select-rows-link-edit.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This way, we can see which countries were filtered out. They seem to be absent due to either not being present in HDI, or having too small of a population.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's insert an Edit Domain widget between the Datasets widget and Merge Data.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-06.png\",\n      width: \"1566\",\n      height: \"436\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-06.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We find and select column \", _jsx(_components.code, {\n        children: \"Country\"\n      }), \". Now change its type from Text to Categorical. Note, this is conceptually wrong: \\\"\", _jsx(_components.em, {\n        children: \"Country\"\n      }), \"\\\" \", _jsx(_components.em, {\n        children: \"is\"\n      }), \" a text variable, it contains a name and not a \\\"category\\\" of country, like the continent or the direction of writing. However, Edit Domain only lets us map its values if we change it to Categorical. And having it as categorical won't hurt.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-edit-domain-hdi.png\",\n      width: \"641\",\n      height: \"502\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-edit-domain-hdi.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"After changing the variable type, the widget will show another box, Values, with a mapping of values. Let's look at the countries in the new excluded rows Data Table widget. We see, for instance, \\\"Bosnia and Herzegovina\\\". We need to match to how John Hopkins data refers to countries, so go back to Edit Domain, and find \\\"Bosnia and Herz.\\\". Double click it and change it to \\\"Bosnia and Herzegovina\\\".\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Similarly, Czech Rep. should become Czechia, Macedonia is now officially North Macedonia, Russian Federation is Russia, United States is US and Viet Nam is Vietnam. John Hopkins has \\\"Korea, South\\\" and HDI includes just Korea. Based on its high human development index and GDP, we can safely assume that this is the northern one. (Kidding, map this Korea to Korea, South, of course. There's no virus in the other Korea because Kim anticipated it before it even appeared and already invented a cure long ago.)\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now that I've taught you how to fish, I give you a fish for today: after editing these and all the other countries whose matches I found, I saved it to a \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/datasets/HDI-fixed-for-jhu-covid.csv\",\n        children: \"file you can download and use\"\n      }), \". Replace the Datasets widget with a File, and load it.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Analysis beyond population\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Comparing this data with the World Bank's data is great because it allows us to relate the epidemiological data to data about particular countries.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-02-07.png\",\n      width: \"1388\",\n      height: \"376\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-02-07.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Select Columns, we removed most of the features. Bring back all those that come from HDI. Now connect, say, a Scatter Plot and observe the relation between the number of cases per million and the number of physicians per ten thousand, which is the closest approximation to the capabilities of health systems.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-scatter-physicians.png\",\n      width: \"2076\",\n      height: \"1110\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-scatter-physicians.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The conclusion here would be that you may want to find yourself in Qatar, Cuba or Greece right now. People are often surprised to see Cuba in such contexts, though Cuba has an excellent public health system and pretty good (though perhaps not entirely unbiased) free education. Seeing this graph, it shouldn't come as a surprise that \", _jsx(_components.a, {\n        href: \"https://www.reuters.com/article/us-health-coronavirus-cuba/cuban-doctors-head-to-italy-to-battle-coronavirus-idUSKBN219051\",\n        children: \"Cuban doctors are heading to Italy and other countries to help fight the epidemics\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A seemingly related measure, Public health expenditure doesn't work here. It is expressed as a percentage of GDP, so it is adjusted for physicians' salaries, but not necessarily for the prices of (imported) equipment. Furthermore, higher spending may reflect a more expensive but not necessarily a more efficient health system.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another potentially interesting factor to follow will be the age of the population. HDI tells us the number of people older than 65 years per 100 people between 15 and 64.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-scatter-dependency.png\",\n      width: \"2076\",\n      height: \"1110\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-scatter-dependency.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I should have used future tense; the population age \", _jsx(_components.strong, {\n        children: \"will\"\n      }), \" be interesting to observe with respect to mortality rate, but not enough countries have a sufficient number of deaths. Let's hope it stays this way.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will eventually be able to (or -- let's hope we won't) explore the relations between epidemics and other sociodemographic data. Generally, the problem with analyzing this data per country is currently that the disease has mostly spread (or been detected?) in developed countries, resulting in many correlations like the speed of spreading and percentage of urban populations, which may or may not reflect actual causation.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Back to curves\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What about curves showing the number of confirmed cases per million? We computed the cases per million for a single day, not for all days. While Orange is a general-purpose data mining tool, some operations are too specific to be implemented in widgets. For such cases, we can program some processing in Python, using the Python Script widget.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-workflow-03-01.png\",\n      width: \"1306\",\n      height: \"342\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-workflow-03-01.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The start of the sequence is the same as before: we merge the two data sources and remove countries with unknown or zero population.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Select Columns, we move all variables except daily data from features to metas.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In Python Script, we type the following program and run it:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\nnormalized = in_data.X / in_data[:, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Total Population (millions) 2015\\\"\"\n        }), \"].metas\\n\\nout_data = Orange.data.Table.from_numpy(\\n    in_data.domain,\\n    normalized, in_data.Y, in_data.metas)\\n\"]\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-python-script.png\",\n      width: \"1306\",\n      height: \"1272\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-python-script.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"With \", _jsx(_components.code, {\n        children: \"in_data[:, \\\"Total Population (millions) 2015\\\"]\"\n      }), \" we take the table corresponding to the column with the population. Since population is now a meta attribute, we take \", _jsx(_components.code, {\n        children: \".metas\"\n      }), \". We divide \", _jsx(_components.code, {\n        children: \"in_data.X\"\n      }), \" with this column. Finally, we construct a new table named \", _jsx(_components.code, {\n        children: \"out_data\"\n      }), \", with the same domain (that is, the same variables), the normalized data, and the original data for target variable(s) and metas.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We get a similar line plot as before, just with proper scale. Observe it. There are two countries with 3000-4000 confirmed cases per million?! Which? Check yourself!\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-basic/2020-04-02-lineplot-3.png\",\n      width: \"1194\",\n      height: \"956\",\n      src: \"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-lineplot-3.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Would you like to see a plot with a logarithmic axis? And Line Plot doesn't support it? (Yet?) The beauty of using the Python script widget is that we can transform the data any way we want. Change the line that computes the normalized data to\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"normalized = np.log10(in_data.X / in_data[:, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Total Population (millions) 2015\\\"\"\n        }), \"].metas)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And we have a logarithmic axis (just with wrong labels).\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Where from here?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This data is related to countries. Hence it would be nice to put it on a map. It also deals with time, and core Orange is not well-equipped for it. In the two follow-up posts, we will explore the add-ons for geographical data and for time series.\"\n    }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download\",\n        children: \"Download Orange\"\n      }), \", and load and explore your own data sets!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In addition to a variety of learning materials posted online in the form of \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/\",\n        children: \"blog posts\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"tutorial videos\"\n      }), \", we've created a \", _jsx(_components.a, {\n        href: \"https://discord.gg/FWrfeXV\",\n        children: \"Discord server. Join the community, tell us what you think!\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":2644,"height":1606,"src":"/blog/2020-04-covid-19-basic/__optimized-images__/2020-04-02-europe-scatter-line-plot.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-covid-19-epidemics-part-1"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>covid-19, visualization, addons, geo, time</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining COVID-19 Epidemics: Part 2</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Robert Cvitkovič</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 13, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Previously on <a href="https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/">Data Mining COVID-19 Epidemics: Part 1</a> we fired up a COVID-19 epidemics data set and looked at some basic visualizations. If you haven&#x27;t got your hands dirty with it yet, check that out first.</p>
 <p>In this post, we&#x27;ll try putting the data on a map. We&#x27;ll also expand on the &quot;data mining is interactive&quot; mantra by creating some animations showing the epidemics spread throughout the world.</p>
 <h2>Prerequisite: installing add-ons</h2>
@@ -224,4 +224,4 @@ <h2>Where from here?</h2>
 <hr/>
 <p>Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. <a href="https://orange.biolab.si/download">Download Orange</a>, and load and explore your own data sets!</p>
 <p>In addition to a variety of learning materials posted online in the form of
-<a href="https://orange.biolab.si/blog/">blog posts</a>, <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">tutorial videos</a>, we&#x27;ve created a Discord server. <a href="https://discord.gg/FWrfeXV">Join the community, tell us what you think!</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Robert Cvitkovič","date":"2020-04-13","draft":false,"title":"Data Mining COVID-19 Epidemics: Part 2","thumbImage":"2020-04-09-choropleth.png","frontPageImage":"2020-04-09-choropleth.png","blog":["covid-19","visualization","addons","geo","time"],"shortExcerpt":"Visualizing COVID-19 data using maps.","longExcerpt":"Visualizing COVID-19 data using area and point maps and interactive timeseries.","oldUrl":"/blog/2020/2020-04-09-covid-19-part-2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\",\n    strong: \"strong\",\n    h3: \"h3\",\n    hr: \"hr\"\n  }, _provideComponents(), props.components), {Figure, WorkflowScreenshot, WindowScreenshot, Video} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!Video) _missingMdxReference(\"Video\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Previously on \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/\",\n        children: \"Data Mining COVID-19 Epidemics: Part 1\"\n      }), \" we fired up a COVID-19 epidemics data set and looked at some basic visualizations. If you haven't got your hands dirty with it yet, check that out first.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this post, we'll try putting the data on a map. We'll also expand on the \\\"data mining is interactive\\\" mantra by creating some animations showing the epidemics spread throughout the world.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Prerequisite: installing add-ons\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange already comes with a great selection of widgets that work with all sorts of data. But sometimes we're faced with a particular data set, such as textual data, time-series, or geographical data, which requires widgets with specific functionality – add-ons to the rescue! Add-ons are packages of widgets specially designed to handle specific data types.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We'll be working with the Geo and Timeseries add-ons. To install them, open \", _jsx(_components.em, {\n        children: \"Options\"\n      }), \" in the menu bar and select \", _jsx(_components.em, {\n        children: \"Add-ons\"\n      }), \". A new window will open, showing a list of officially supported add-ons. Select Orange3-Geo and Orange3-TimeSeries and click \", _jsx(_components.em, {\n        children: \"OK\"\n      }), \". After installation,  restart Orange. Check out the new sections in the widget toolbox; you should see some cool new widgets.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-install-addon.png\",\n      width: \"652\",\n      height: \"612\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-install-addon.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Note:\"\n      }), \" the two add-ons have been revamped very recently. If you already have them, use the Add-ons dialog to upgrade them to the latest version.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"From Scatter Plot to Geo Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You should already be familiar with the Scatter Plot widget. In the previous blog post, we used it as an impromptu map by putting latitude and longitude on the axes.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-scatter-plot.png\",\n      width: \"1435\",\n      height: \"811\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-scatter-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A better solution is to use Geo Map from the Orange3-Geo add-on. It provides a variety of maps as backgrounds. With them, it's much easier to associate points with locations. To start, first load the \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",\n        children: \"COVID-19 data set\"\n      }), \" using the File widget, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/\",\n        children: \"as shown in the previous post\"\n      }), \". Then, add and connect a Geo Map widget.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-01.png\",\n      width: \"237\",\n      height: \"125\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-01.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Latitude and longitude are automatically selected. Below, you can define similar point properties, as in the scatter plot. The size of a point can intuitively represent the number of cases, so let's select the latest date in the \", _jsx(_components.em, {\n        children: \"Size\"\n      }), \" dropdown. If you're feeling adventurous, try changing the map type to satellite or topological.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-full-geomap.png\",\n      width: \"1368\",\n      height: \"905\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-full-geomap.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Just like in Scatter Plot, you can scroll to zoom in and out, and click/drag to select data points. As opposed to using Scatter Plot, showing the points over a map of Europe makes it easier to select the countries we want to inspect.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-eu-geomap.png\",\n      width: \"1362\",\n      height: \"897\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-eu-geomap.png\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"The chloro... chorophl... chloropet... map with areas colored in\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The map is nice and informative, and it's easy to distinguish larger epidemics from smaller ones. Still, those similarly-sized points on the map represent entire countries from big Russia to small Slovenia. We could, alternatively, represent countries by coloring their surface area. This is a job for Choropleth Map. Let's add it and connect it.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-02.png\",\n      width: \"243\",\n      height: \"130\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-02.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget is similar to Geo Map, but instead of displaying data as points, it aggregates them into regions (e.g., Country, State, Province), and displays those instead. Under \", _jsx(_components.em, {\n        children: \"Attribute\"\n      }), \", select the latest date and under \", _jsx(_components.em, {\n        children: \"Agg.\"\n      }), \", select \", _jsx(_components.em, {\n        children: \"sum\"\n      }), \". The colored regions now show the sum of confirmed COVID-19 cases for each country.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Try choosing different dates. Notice how China initially experiences rapid growth, followed by a period of stagnation, and how in the past week, the USA overtook all other countries' confirmed COVID-19 case count.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-choropleth.png\",\n      width: \"1354\",\n      height: \"891\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-choropleth.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A warning that \", _jsx(_components.em, {\n        children: \"7 points are not in any region\"\n      }), \" refers to some small islands and cruise ships included in the data.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"If a picture paints a thousand words, an animation paints a million\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When working with time-series data, the coolest insights arise from observing trends – how data changes depending on time. Let's animate our maps!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We'll be using a special widget from Orange3-Timeseries: Time Slice, which lets you select data in a time window. It also features a playback function that slowly moves the time window, outputting corresponding data as it goes.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Transpose: part I\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Time Slice requires time instances in the form of rows, while our data specifies them in columns. Let's start again with the File widget that loads the data. Move \", _jsx(_components.em, {\n        children: \"Lat\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Long\"\n      }), \" to meta attributes by double-clicking  \", _jsx(_components.em, {\n        children: \"feature\"\n      }), \" in the \", _jsx(_components.em, {\n        children: \"Role\"\n      }), \" column and selecting \", _jsx(_components.em, {\n        children: \"meta\"\n      }), \". This will tell Orange to exclude them from data manipulations.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-file.gif\",\n      width: \"600\",\n      height: \"590\",\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-file.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To flip rows and columns, we use the Transpose widget. Connect it to the File widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-03.png\",\n      width: \"233\",\n      height: \"116\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-03.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Set \", _jsx(_components.em, {\n        children: \"From variable\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"Country/Region\"\n      }), \". Ignore the warning that indices were added to variables with the same name; it won't hurt us.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-transpose-1.png\",\n      width: \"226\",\n      height: \"296\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-transpose-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To better understand what just happened, connect a Data Table to Transpose and see the result: rows and columns were swapped. Columns are named after countries (because we chose \", _jsx(_components.em, {\n        children: \"Country/Region\"\n      }), \" in the Transpose widget), while a new column, \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \", contains the original names of the columns.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-table-1.png\",\n      width: \"1179\",\n      height: \"671\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-table-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Right now, Orange is treating values in \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \" as strings. We need to tell Orange that the feature contains timestamps. Let's connect an Edit Domain widget.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-04.png\",\n      width: \"378\",\n      height: \"116\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-04.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Scroll to the bottom of \", _jsx(_components.em, {\n        children: \"Variables\"\n      }), \" and select \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \". In \", _jsx(_components.em, {\n        children: \"Edit\"\n      }), \", change its type to \", _jsx(_components.em, {\n        children: \"Time\"\n      }), \". While you're at it, change its name to something meaningful, like \", _jsx(_components.em, {\n        children: \"date\"\n      }), \". Finally, click \", _jsx(_components.em, {\n        children: \"Apply\"\n      })]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-edit-domain.gif\",\n      width: \"660\",\n      height: \"660\",\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-edit-domain.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The data is now ready to be processed by Time Slice.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-05.png\",\n      width: \"505\",\n      height: \"120\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-05.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-time-slice.png\",\n      width: \"514\",\n      height: \"504\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-time-slice.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The top indicates the currently selected slice of data. You can change the interval by interacting with the histogram, or by adjusting the start and end dates directly below. Let's select a day's slice between the 25th and 26th January 2020.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With a click of the play button, the slice moves, and the data output updates accordingly. To verify that it works, connect a Data Table, and see it in action.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Transpose: part II\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Make sure to stop the Time Slice playback before proceeding. We're almost at the finish line; we just need to transform the data back into its original form. To do so, connect another Transpose widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-06.png\",\n      width: \"660\",\n      height: \"131\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-06.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Under \", _jsx(_components.em, {\n        children: \"Future names\"\n      }), \" select \", _jsx(_components.em, {\n        children: \"Generic\"\n      }), \", which will name the new column \", _jsx(_components.em, {\n        children: \"Feature 1\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-transpose-2.png\",\n      width: \"236\",\n      height: \"303\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-transpose-2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's connect a Data Table for a final check of our data before putting it on a map.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-table-2.png\",\n      width: \"997\",\n      height: \"654\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-table-2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Looking good!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The final step entails connecting the Choropleth Map, selecting \", _jsx(_components.em, {\n        children: \"Feature 1\"\n      }), \" as \", _jsx(_components.em, {\n        children: \"Attribute\"\n      }), \", and \", _jsx(_components.em, {\n        children: \"Sum\"\n      }), \" as the \", _jsx(_components.em, {\n        children: \"Agg\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-07.png\",\n      width: \"787\",\n      height: \"121\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-07.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To observe the animation, open up both Choropleth and Time Slice at the same time.\", _jsx(\"br\", {}), \"\\nProtip: pressing Ctrl/CMD+Up reveals the workflow, Ctrl/CMD+Down reveals widget windows.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"All that's left is to hit play.\"\n    }), \"\\n\", _jsx(Video, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-animation-choropleth.mp4\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we're familiar with Time Slice, let's use it with Geo Map too. For bonus points, let's also remove countries with zero confirmed cases. That way, the number of points on the map will increase as the virus spreads throughout the world. For this, we'll use a Select Rows widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-08.png\",\n      width: \"903\",\n      height: \"140\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-08.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Under Conditions, select \", _jsx(_components.em, {\n        children: \"Feature 1\"\n      }), \", select \", _jsx(_components.em, {\n        children: \"is greater than\"\n      }), \", and type in \", _jsx(_components.em, {\n        children: \"0\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-select-rows.gif\",\n      width: \"600\",\n      height: \"436\",\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-select-rows.gif\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Add and connect a Geo Map widget. Zoom out, so you see the whole world, and on the left side, check \", _jsx(_components.em, {\n        children: \"Freeze map\"\n      }), \". This will prevented the map from jumping around as new points are added.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-09.png\",\n      width: \"924\",\n      height: \"144\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-09.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Press play once again – let's see what we've built!\"\n    }), \"\\n\", _jsx(Video, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-animation-geomap.mp4\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Where from here?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the blog, we explained how to turn Orange into an interactive data exploration tool for COVID-19 data! With a few widgets we were able to determine the change in time for the confirmed cases and plot the results on a map.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Time Slice isn't the only widget in the Orange3-Timeseries add-on. Stay tuned for next week's blog, where we'll delve deeper into the patterns emerging from time-series data.\"\n    }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download\",\n        children: \"Download Orange\"\n      }), \", and load and explore your own data sets!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In addition to a variety of learning materials posted online in the form of\\n\", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/\",\n        children: \"blog posts\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"tutorial videos\"\n      }), \", we've created a Discord server. \", _jsx(_components.a, {\n        href: \"https://discord.gg/FWrfeXV\",\n        children: \"Join the community, tell us what you think!\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":1354,"height":891,"src":"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-choropleth.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-covid-19-epidemics-part-2"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<a href="https://orange.biolab.si/blog/">blog posts</a>, <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">tutorial videos</a>, we&#x27;ve created a Discord server. <a href="https://discord.gg/FWrfeXV">Join the community, tell us what you think!</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Robert Cvitkovič","date":"2020-04-13","draft":false,"title":"Data Mining COVID-19 Epidemics: Part 2","thumbImage":"2020-04-09-choropleth.png","frontPageImage":"2020-04-09-choropleth.png","blog":["covid-19","visualization","addons","geo","time"],"shortExcerpt":"Visualizing COVID-19 data using maps.","longExcerpt":"Visualizing COVID-19 data using area and point maps and interactive timeseries.","oldUrl":"/blog/2020/2020-04-09-covid-19-part-2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\",\n    strong: \"strong\",\n    h3: \"h3\",\n    hr: \"hr\"\n  }, _provideComponents(), props.components), {Figure, WorkflowScreenshot, WindowScreenshot, Video} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!Video) _missingMdxReference(\"Video\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Previously on \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/\",\n        children: \"Data Mining COVID-19 Epidemics: Part 1\"\n      }), \" we fired up a COVID-19 epidemics data set and looked at some basic visualizations. If you haven't got your hands dirty with it yet, check that out first.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this post, we'll try putting the data on a map. We'll also expand on the \\\"data mining is interactive\\\" mantra by creating some animations showing the epidemics spread throughout the world.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Prerequisite: installing add-ons\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange already comes with a great selection of widgets that work with all sorts of data. But sometimes we're faced with a particular data set, such as textual data, time-series, or geographical data, which requires widgets with specific functionality – add-ons to the rescue! Add-ons are packages of widgets specially designed to handle specific data types.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We'll be working with the Geo and Timeseries add-ons. To install them, open \", _jsx(_components.em, {\n        children: \"Options\"\n      }), \" in the menu bar and select \", _jsx(_components.em, {\n        children: \"Add-ons\"\n      }), \". A new window will open, showing a list of officially supported add-ons. Select Orange3-Geo and Orange3-TimeSeries and click \", _jsx(_components.em, {\n        children: \"OK\"\n      }), \". After installation,  restart Orange. Check out the new sections in the widget toolbox; you should see some cool new widgets.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-install-addon.png\",\n      width: \"652\",\n      height: \"612\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-install-addon.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Note:\"\n      }), \" the two add-ons have been revamped very recently. If you already have them, use the Add-ons dialog to upgrade them to the latest version.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"From Scatter Plot to Geo Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You should already be familiar with the Scatter Plot widget. In the previous blog post, we used it as an impromptu map by putting latitude and longitude on the axes.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-scatter-plot.png\",\n      width: \"1435\",\n      height: \"811\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-scatter-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A better solution is to use Geo Map from the Orange3-Geo add-on. It provides a variety of maps as backgrounds. With them, it's much easier to associate points with locations. To start, first load the \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\",\n        children: \"COVID-19 data set\"\n      }), \" using the File widget, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/\",\n        children: \"as shown in the previous post\"\n      }), \". Then, add and connect a Geo Map widget.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-01.png\",\n      width: \"237\",\n      height: \"125\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-01.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Latitude and longitude are automatically selected. Below, you can define similar point properties, as in the scatter plot. The size of a point can intuitively represent the number of cases, so let's select the latest date in the \", _jsx(_components.em, {\n        children: \"Size\"\n      }), \" dropdown. If you're feeling adventurous, try changing the map type to satellite or topological.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-full-geomap.png\",\n      width: \"1368\",\n      height: \"905\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-full-geomap.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Just like in Scatter Plot, you can scroll to zoom in and out, and click/drag to select data points. As opposed to using Scatter Plot, showing the points over a map of Europe makes it easier to select the countries we want to inspect.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-eu-geomap.png\",\n      width: \"1362\",\n      height: \"897\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-eu-geomap.png\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"The chloro... chorophl... chloropet... map with areas colored in\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The map is nice and informative, and it's easy to distinguish larger epidemics from smaller ones. Still, those similarly-sized points on the map represent entire countries from big Russia to small Slovenia. We could, alternatively, represent countries by coloring their surface area. This is a job for Choropleth Map. Let's add it and connect it.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-02.png\",\n      width: \"243\",\n      height: \"130\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-02.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget is similar to Geo Map, but instead of displaying data as points, it aggregates them into regions (e.g., Country, State, Province), and displays those instead. Under \", _jsx(_components.em, {\n        children: \"Attribute\"\n      }), \", select the latest date and under \", _jsx(_components.em, {\n        children: \"Agg.\"\n      }), \", select \", _jsx(_components.em, {\n        children: \"sum\"\n      }), \". The colored regions now show the sum of confirmed COVID-19 cases for each country.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Try choosing different dates. Notice how China initially experiences rapid growth, followed by a period of stagnation, and how in the past week, the USA overtook all other countries' confirmed COVID-19 case count.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-choropleth.png\",\n      width: \"1354\",\n      height: \"891\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-choropleth.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A warning that \", _jsx(_components.em, {\n        children: \"7 points are not in any region\"\n      }), \" refers to some small islands and cruise ships included in the data.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"If a picture paints a thousand words, an animation paints a million\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When working with time-series data, the coolest insights arise from observing trends – how data changes depending on time. Let's animate our maps!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We'll be using a special widget from Orange3-Timeseries: Time Slice, which lets you select data in a time window. It also features a playback function that slowly moves the time window, outputting corresponding data as it goes.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Transpose: part I\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Time Slice requires time instances in the form of rows, while our data specifies them in columns. Let's start again with the File widget that loads the data. Move \", _jsx(_components.em, {\n        children: \"Lat\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Long\"\n      }), \" to meta attributes by double-clicking  \", _jsx(_components.em, {\n        children: \"feature\"\n      }), \" in the \", _jsx(_components.em, {\n        children: \"Role\"\n      }), \" column and selecting \", _jsx(_components.em, {\n        children: \"meta\"\n      }), \". This will tell Orange to exclude them from data manipulations.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-file.gif\",\n      width: \"600\",\n      height: \"590\",\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-file.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To flip rows and columns, we use the Transpose widget. Connect it to the File widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-03.png\",\n      width: \"233\",\n      height: \"116\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-03.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Set \", _jsx(_components.em, {\n        children: \"From variable\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"Country/Region\"\n      }), \". Ignore the warning that indices were added to variables with the same name; it won't hurt us.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-transpose-1.png\",\n      width: \"226\",\n      height: \"296\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-transpose-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To better understand what just happened, connect a Data Table to Transpose and see the result: rows and columns were swapped. Columns are named after countries (because we chose \", _jsx(_components.em, {\n        children: \"Country/Region\"\n      }), \" in the Transpose widget), while a new column, \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \", contains the original names of the columns.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-table-1.png\",\n      width: \"1179\",\n      height: \"671\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-table-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Right now, Orange is treating values in \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \" as strings. We need to tell Orange that the feature contains timestamps. Let's connect an Edit Domain widget.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-04.png\",\n      width: \"378\",\n      height: \"116\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-04.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Scroll to the bottom of \", _jsx(_components.em, {\n        children: \"Variables\"\n      }), \" and select \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \". In \", _jsx(_components.em, {\n        children: \"Edit\"\n      }), \", change its type to \", _jsx(_components.em, {\n        children: \"Time\"\n      }), \". While you're at it, change its name to something meaningful, like \", _jsx(_components.em, {\n        children: \"date\"\n      }), \". Finally, click \", _jsx(_components.em, {\n        children: \"Apply\"\n      })]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-edit-domain.gif\",\n      width: \"660\",\n      height: \"660\",\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-edit-domain.gif\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The data is now ready to be processed by Time Slice.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-05.png\",\n      width: \"505\",\n      height: \"120\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-05.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-time-slice.png\",\n      width: \"514\",\n      height: \"504\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-time-slice.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The top indicates the currently selected slice of data. You can change the interval by interacting with the histogram, or by adjusting the start and end dates directly below. Let's select a day's slice between the 25th and 26th January 2020.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With a click of the play button, the slice moves, and the data output updates accordingly. To verify that it works, connect a Data Table, and see it in action.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Transpose: part II\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Make sure to stop the Time Slice playback before proceeding. We're almost at the finish line; we just need to transform the data back into its original form. To do so, connect another Transpose widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-06.png\",\n      width: \"660\",\n      height: \"131\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-06.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Under \", _jsx(_components.em, {\n        children: \"Future names\"\n      }), \" select \", _jsx(_components.em, {\n        children: \"Generic\"\n      }), \", which will name the new column \", _jsx(_components.em, {\n        children: \"Feature 1\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-transpose-2.png\",\n      width: \"236\",\n      height: \"303\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-transpose-2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's connect a Data Table for a final check of our data before putting it on a map.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-table-2.png\",\n      width: \"997\",\n      height: \"654\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-table-2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Looking good!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The final step entails connecting the Choropleth Map, selecting \", _jsx(_components.em, {\n        children: \"Feature 1\"\n      }), \" as \", _jsx(_components.em, {\n        children: \"Attribute\"\n      }), \", and \", _jsx(_components.em, {\n        children: \"Sum\"\n      }), \" as the \", _jsx(_components.em, {\n        children: \"Agg\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-07.png\",\n      width: \"787\",\n      height: \"121\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-07.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To observe the animation, open up both Choropleth and Time Slice at the same time.\", _jsx(\"br\", {}), \"\\nProtip: pressing Ctrl/CMD+Up reveals the workflow, Ctrl/CMD+Down reveals widget windows.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"All that's left is to hit play.\"\n    }), \"\\n\", _jsx(Video, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-animation-choropleth.mp4\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we're familiar with Time Slice, let's use it with Geo Map too. For bonus points, let's also remove countries with zero confirmed cases. That way, the number of points on the map will increase as the virus spreads throughout the world. For this, we'll use a Select Rows widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-08.png\",\n      width: \"903\",\n      height: \"140\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-08.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Under Conditions, select \", _jsx(_components.em, {\n        children: \"Feature 1\"\n      }), \", select \", _jsx(_components.em, {\n        children: \"is greater than\"\n      }), \", and type in \", _jsx(_components.em, {\n        children: \"0\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-select-rows.gif\",\n      width: \"600\",\n      height: \"436\",\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-select-rows.gif\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Add and connect a Geo Map widget. Zoom out, so you see the whole world, and on the left side, check \", _jsx(_components.em, {\n        children: \"Freeze map\"\n      }), \". This will prevented the map from jumping around as new points are added.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-workflow-09.png\",\n      width: \"924\",\n      height: \"144\",\n      src: \"/blog/2020-04-covid-19-part-2/__optimized-images__/2020-04-09-workflow-09.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Press play once again – let's see what we've built!\"\n    }), \"\\n\", _jsx(Video, {\n      src: \"/blog/2020-04-covid-19-part-2/2020-04-09-animation-geomap.mp4\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Where from here?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the blog, we explained how to turn Orange into an interactive data exploration tool for COVID-19 data! With a few widgets we were able to determine the change in time for the confirmed cases and plot the results on a map.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Time Slice isn't the only widget in the Orange3-Timeseries add-on. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>covid-19, visualization, addons, trends, time</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining COVID-19 Epidemics: Part 3</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Andreja Kovačič</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 20, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>So far, we&#x27;ve seen how to make <a href="https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/">basic visualizations</a> related to the corona virus and how to look at the <a href="https://orange.biolab.si/blog/2020/2020-04-09-covid-19-part-2/">disease progression on the map</a>. Be sure to check them out first, before delving into this one.</p>
 <p>We are now heading towards somewhat more advanced visualizations that let us observe trends in the data. Just as a heads up: your results may be different, depending on the day you downloaded the data. We are working with confirmed cases up to April 13, on the previously mentioned data from the John Hopkins University.</p>
 <h2>Prerequisite: Timeseries add-on</h2>
@@ -215,4 +215,4 @@ <h3>Moving transform and smoothing</h3>
 <hr/>
 <p>Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. <a href="https://orange.biolab.si/download">Download Orange</a>, and load and explore your own data sets!</p>
 <p>In addition to a variety of learning materials posted online in the form of
-<a href="https://orange.biolab.si/blog/">blog posts</a>, <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">tutorial videos</a>, we&#x27;ve created a Discord server. <a href="https://discord.gg/FWrfeXV">Join the community, tell us what you think!</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Andreja Kovačič","date":"2020-04-20","draft":false,"title":"Data Mining COVID-19 Epidemics: Part 3","thumbImage":"2020-04-15-thumb-log-growth.png","frontPageImage":"2020-04-15-thumb-log-growth.png","blog":["covid-19","visualization","addons","trends","time"],"shortExcerpt":"Inspection of Covid-19 time trends.","longExcerpt":"Inspecting and comparing Covid-19 time trends, absolute and relative growth.","x2images":false,"aliases":["/blog/2020/2020-4-015-covid-19-part-3/"],"oldUrl":"/blog/2020/2020-04-15-covid-19-part-3/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\",\n    h3: \"h3\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    hr: \"hr\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot, WindowScreenshot, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"So far, we've seen how to make \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/\",\n        children: \"basic visualizations\"\n      }), \" related to the corona virus and how to look at the \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-09-covid-19-part-2/\",\n        children: \"disease progression on the map\"\n      }), \". Be sure to check them out first, before delving into this one.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are now heading towards somewhat more advanced visualizations that let us observe trends in the data. Just as a heads up: your results may be different, depending on the day you downloaded the data. We are working with confirmed cases up to April 13, on the previously mentioned data from the John Hopkins University.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Prerequisite: Timeseries add-on\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The spread of the virus is influenced by many factors, time being one of them. Timeseries add-on specializes in manipulation of time-related data. If you followed our blogs, you should already have it installed. If you don't, just take a peek in the \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-09-covid-19-part-2/\",\n        children: \"second one\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Formatting the data As Timeseries\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We'll load our data as usual with the File widget. The data contains latitude and longitude columns, which are just getting in the way in our further analysis. We will use Select Columns to put them into meta columns and thus exclude them from any calculations. Then we'll select some countries we are interested in with the help of the Data Table. You could also do it straight after inspecting the data in Geo Map.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-wf1.png\",\n      width: \"314\",\n      height: \"224\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-wf1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we just need to tell Orange, which variable contains time stamps. Our dates are represented as columns instead of as rows, so we'll use Transpose widget to make each row represent a datum and then connect it to As Timeseries Widget. In Transpose, we set the variable whose values will be used to name the rows when they are turned into columns. We'll name them by the column Country, as shown in the image, since the Province field is mostly empty. In As Timeseries, we set \", _jsx(_components.em, {\n        children: \"sequence as implied by instance order\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-as_timeseries.png\",\n      width: \"344\",\n      height: \"270\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-as_timeseries.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here is our chain of widgets. We should now be ready to start making some plots. Phew.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-first-snake.png\",\n      width: \"763\",\n      height: \"190\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-first-snake.png\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Line Chart and log plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Make sure you've selected countries you are interested in the Data Table. I've chosen Italy, US, Iran, France and Chinese province Hubei. Then, connect As Timeseries widget and Line Chart and let's get plotting. Once in Line Chart, we can select multiple countries by clicking Ctrl/Cmd and draw them on the same axis or we could compare multiple plots by clicking on Add plot.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-line-chart1.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-line-chart1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here, we see how the virus started its path in China, where the number of cases quickly rose but has since then stayed level. Other countries were hit later on. The slopes of the curves tell us how fast the numbers are growing, but more on that later. One thing to notice though: due to the fast growth of the virus in some countries, some smaller slopes are almost invisible. We can remedy that by checking the Logarithmic axis box just like this:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-log-linechart.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-log-linechart.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can now see the first steps as well as the current situation.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Python Script\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see from these plots, that different countries came into contact with the virus at different times. It seems it would be much easier to compare the curves if all of them started their climb at the same time. We can do that with just a few lines of code. Example snippet here aligns the curves with the moment where the country recorded 100 cases. You can set n to any other (positive) number if you will, though.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" copy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" deepcopy\\n\\nn = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"100\"\n        }), \"\\nout_data = deepcopy(in_data)\\nout_data.X[out_data.X \u003c n] = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"\\nar = np.argwhere(out_data.X)\\n\\ncols, shifts = np.unique(ar[:, \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"], return_counts=\", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"True\"\n        }), \")\\nout_data.X = np.array([np.roll(out_data.X[:, col], shift) \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" col, shift \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"zip\"\n        }), \"(cols, shifts)]).T\\nout_data.X[out_data.X==\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]= np.nan\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Copy these lines into Python Script widget, and plug it into our initial snake of widgets like so:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-snake-pyscript.png\",\n      width: \"897\",\n      height: \"193\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-snake-pyscript.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Look at the aligned curves now, they are much easier to compare.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-script-log-lc.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-script-log-lc.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Absolute growth with derivative\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's take a look at how fast the confirmed cases are spreading. Connect As Timeseries with Difference widget, choose Differencing and select countries of interest. Differencing order of 1 means we'll be looking at derivative of first order, which just means daily change in our case. We'll leave shift as is, on 1.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-diff-settings.png\",\n      width: \"346\",\n      height: \"570\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-diff-settings.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Again, we'll view the transformed curves in Line Chart. Notice how the difference (and every other) transform adds a new column? That means we can compare our curves in different states, so be sure to always check what is being shown. For starters, try looking at, for example, China and its transformed version. See how easy it is to spot the daily spikes that are perhaps out of the ordinary and need to be checked? The most prominent spike here is probably due to the change in counting.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-dif.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-dif.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we again compare multiple countries, we notice the US's speedy climb and Iran seemingly being more successful in curbing the growth.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-diff-compare.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-diff-compare.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The difference could be only due to the difference in population and what is an overwhelmingly large number of patients for one country might be almost business as usual for another one.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Scale free growth with quotient\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Difference widget also has the option to output the quotients. It will allow us to observe the relative growth and compare countries directly.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-quot-settings.png\",\n      width: \"346\",\n      height: \"570\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-quot-settings.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's look at, for example, China (Hubei province) vs France.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-fr-quot.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-fr-quot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It seems China is doing worse in the beginning and better later on, but it is impossible to tell at which point the trends start to shift. There are just too many jumps, due to noise in testing and reporting. So for our final step, let's smooth things out.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Moving transform and smoothing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Smoothing is usually a part of preprocessing, so insert the Moving Transform widget between As Timeseries and Difference, and add some moving average transforms, like this:\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-wf3.png\",\n      width: \"463\",\n      height: \"284\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-wf3.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-mt-settings.png\",\n      width: \"419\",\n      height: \"439\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-mt-settings.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Selecting now smoothened China and the France data in Difference widget, yields the following plot in Line Chart:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-fr-quot-sm.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-fr-quot-sm.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The trends now seem clearer. The countries have somewhat similar trends up to 15th day, when China's growth falls decisively below the French.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Quotient plot obviously benefited from smoothing, as did the difference plot. For completeness, we added another Difference widget after Moving Transform, and plotted now smoothed difference between the two countries. Notice how the turning point is now seemingly further down the line. Orange prides itself on interactivity, so just by sliding the mouse over the plot, we get the exact information regarding the days and counts. The turning point seems to be on the 25th day.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-fr-diff-sm.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-fr-diff-sm.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This blog concludes our Corona virus series for now. Until our next blog, stay safe.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-final-workflow.png\",\n      width: \"851\",\n      height: \"221\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-final-workflow.png\"\n    }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download\",\n        children: \"Download Orange\"\n      }), \", and load and explore your own data sets!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In addition to a variety of learning materials posted online in the form of\\n\", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/\",\n        children: \"blog posts\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"tutorial videos\"\n      }), \", we've created a Discord server. \", _jsx(_components.a, {\n        href: \"https://discord.gg/FWrfeXV\",\n        children: \"Join the community, tell us what you think!\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-thumb-log-growth.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-covid-19-epidemics-part-3"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<a href="https://orange.biolab.si/blog/">blog posts</a>, <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy">tutorial videos</a>, we&#x27;ve created a Discord server. <a href="https://discord.gg/FWrfeXV">Join the community, tell us what you think!</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Andreja Kovačič","date":"2020-04-20","draft":false,"title":"Data Mining COVID-19 Epidemics: Part 3","thumbImage":"2020-04-15-thumb-log-growth.png","frontPageImage":"2020-04-15-thumb-log-growth.png","blog":["covid-19","visualization","addons","trends","time"],"shortExcerpt":"Inspection of Covid-19 time trends.","longExcerpt":"Inspecting and comparing Covid-19 time trends, absolute and relative growth.","x2images":false,"aliases":["/blog/2020/2020-4-015-covid-19-part-3/"],"oldUrl":"/blog/2020/2020-04-15-covid-19-part-3/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\",\n    h3: \"h3\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    hr: \"hr\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot, WindowScreenshot, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"So far, we've seen how to make \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-02-covid-19-basic/\",\n        children: \"basic visualizations\"\n      }), \" related to the corona virus and how to look at the \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-09-covid-19-part-2/\",\n        children: \"disease progression on the map\"\n      }), \". Be sure to check them out first, before delving into this one.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are now heading towards somewhat more advanced visualizations that let us observe trends in the data. Just as a heads up: your results may be different, depending on the day you downloaded the data. We are working with confirmed cases up to April 13, on the previously mentioned data from the John Hopkins University.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Prerequisite: Timeseries add-on\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The spread of the virus is influenced by many factors, time being one of them. Timeseries add-on specializes in manipulation of time-related data. If you followed our blogs, you should already have it installed. If you don't, just take a peek in the \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2020/2020-04-09-covid-19-part-2/\",\n        children: \"second one\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Formatting the data As Timeseries\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We'll load our data as usual with the File widget. The data contains latitude and longitude columns, which are just getting in the way in our further analysis. We will use Select Columns to put them into meta columns and thus exclude them from any calculations. Then we'll select some countries we are interested in with the help of the Data Table. You could also do it straight after inspecting the data in Geo Map.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-wf1.png\",\n      width: \"314\",\n      height: \"224\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-wf1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we just need to tell Orange, which variable contains time stamps. Our dates are represented as columns instead of as rows, so we'll use Transpose widget to make each row represent a datum and then connect it to As Timeseries Widget. In Transpose, we set the variable whose values will be used to name the rows when they are turned into columns. We'll name them by the column Country, as shown in the image, since the Province field is mostly empty. In As Timeseries, we set \", _jsx(_components.em, {\n        children: \"sequence as implied by instance order\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-as_timeseries.png\",\n      width: \"344\",\n      height: \"270\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-as_timeseries.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here is our chain of widgets. We should now be ready to start making some plots. Phew.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-first-snake.png\",\n      width: \"763\",\n      height: \"190\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-first-snake.png\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Line Chart and log plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Make sure you've selected countries you are interested in the Data Table. I've chosen Italy, US, Iran, France and Chinese province Hubei. Then, connect As Timeseries widget and Line Chart and let's get plotting. Once in Line Chart, we can select multiple countries by clicking Ctrl/Cmd and draw them on the same axis or we could compare multiple plots by clicking on Add plot.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-line-chart1.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-line-chart1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here, we see how the virus started its path in China, where the number of cases quickly rose but has since then stayed level. Other countries were hit later on. The slopes of the curves tell us how fast the numbers are growing, but more on that later. One thing to notice though: due to the fast growth of the virus in some countries, some smaller slopes are almost invisible. We can remedy that by checking the Logarithmic axis box just like this:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-log-linechart.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-log-linechart.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can now see the first steps as well as the current situation.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Python Script\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see from these plots, that different countries came into contact with the virus at different times. It seems it would be much easier to compare the curves if all of them started their climb at the same time. We can do that with just a few lines of code. Example snippet here aligns the curves with the moment where the country recorded 100 cases. You can set n to any other (positive) number if you will, though.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" copy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" deepcopy\\n\\nn = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"100\"\n        }), \"\\nout_data = deepcopy(in_data)\\nout_data.X[out_data.X \u003c n] = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"\\nar = np.argwhere(out_data.X)\\n\\ncols, shifts = np.unique(ar[:, \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"], return_counts=\", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"True\"\n        }), \")\\nout_data.X = np.array([np.roll(out_data.X[:, col], shift) \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" col, shift \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"zip\"\n        }), \"(cols, shifts)]).T\\nout_data.X[out_data.X==\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]= np.nan\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Copy these lines into Python Script widget, and plug it into our initial snake of widgets like so:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-snake-pyscript.png\",\n      width: \"897\",\n      height: \"193\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-snake-pyscript.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Look at the aligned curves now, they are much easier to compare.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-script-log-lc.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-script-log-lc.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Absolute growth with derivative\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's take a look at how fast the confirmed cases are spreading. Connect As Timeseries with Difference widget, choose Differencing and select countries of interest. Differencing order of 1 means we'll be looking at derivative of first order, which just means daily change in our case. We'll leave shift as is, on 1.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-diff-settings.png\",\n      width: \"346\",\n      height: \"570\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-diff-settings.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Again, we'll view the transformed curves in Line Chart. Notice how the difference (and every other) transform adds a new column? That means we can compare our curves in different states, so be sure to always check what is being shown. For starters, try looking at, for example, China and its transformed version. See how easy it is to spot the daily spikes that are perhaps out of the ordinary and need to be checked? The most prominent spike here is probably due to the change in counting.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-dif.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-dif.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we again compare multiple countries, we notice the US's speedy climb and Iran seemingly being more successful in curbing the growth.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-diff-compare.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-diff-compare.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The difference could be only due to the difference in population and what is an overwhelmingly large number of patients for one country might be almost business as usual for another one.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Scale free growth with quotient\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Difference widget also has the option to output the quotients. It will allow us to observe the relative growth and compare countries directly.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-quot-settings.png\",\n      width: \"346\",\n      height: \"570\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-quot-settings.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's look at, for example, China (Hubei province) vs France.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-fr-quot.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-fr-quot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It seems China is doing worse in the beginning and better later on, but it is impossible to tell at which point the trends start to shift. There are just too many jumps, due to noise in testing and reporting. So for our final step, let's smooth things out.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Moving transform and smoothing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Smoothing is usually a part of preprocessing, so insert the Moving Transform widget between As Timeseries and Difference, and add some moving average transforms, like this:\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-wf3.png\",\n      width: \"463\",\n      height: \"284\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-wf3.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-mt-settings.png\",\n      width: \"419\",\n      height: \"439\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-mt-settings.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Selecting now smoothened China and the France data in Difference widget, yields the following plot in Line Chart:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-fr-quot-sm.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-fr-quot-sm.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The trends now seem clearer. The countries have somewhat similar trends up to 15th day, when China's growth falls decisively below the French.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Quotient plot obviously benefited from smoothing, as did the difference plot. For completeness, we added another Difference widget after Moving Transform, and plotted now smoothed difference between the two countries. Notice how the turning point is now seemingly further down the line. Orange prides itself on interactivity, so just by sliding the mouse over the plot, we get the exact information regarding the days and counts. The turning point seems to be on the 25th day.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-china-fr-diff-sm.png\",\n      width: \"1179\",\n      height: \"557\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-china-fr-diff-sm.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This blog concludes our Corona virus series for now. Until our next blog, stay safe.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-04-covid-19-part-3/2020-04-15-final-workflow.png\",\n      width: \"851\",\n      height: \"221\",\n      src: \"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-final-workflow.png\"\n    }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange is a multi-platform open-source machine learning and data visualization tool for beginners and experts alike. \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download\",\n        children: \"Download Orange\"\n      }), \", and load and explore your own data sets!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In addition to a variety of learning materials posted online in the form of\\n\", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/\",\n        children: \"blog posts\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n        children: \"tutorial videos\"\n      }), \", we've created a Discord server. \", _jsx(_components.a, {\n        href: \"https://discord.gg/FWrfeXV\",\n        children: \"Join the community, tell us what you think!\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2020-04-covid-19-part-3/__optimized-images__/2020-04-15-thumb-log-growth.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-covid-19-epidemics-part-3"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Data Mining for Anthropologists?</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Data Mining for Anthropologists?"/><meta property="og:description" content="This weekend we were in Lisbon, Portugal, at the [Why the World Needs Anthropologists](https://www.applied-anthropology.com/) conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-11-06 11:45:09+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="education"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, text mining, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining for Anthropologists?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 06, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This weekend we were in Lisbon, Portugal, at the <a href="https://www.applied-anthropology.com/">Why the World Needs Anthropologists</a> conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry. I was there to hold a workshop on Data Ethnography, an approach that tries to combine methods from data science and anthropology into a fruitful interdisciplinary mix!</p>
 <p><a href="/blog/2018-11-data-mining-for-anthropologists/__optimized-images__/blog-header.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="759" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-11-data-mining-for-anthropologists/__optimized-images__/blog-header.jpg"/></a>
 Data Ethnography workshop at this year&#x27;s Why the World Needs Anthropologists conference.</p>
@@ -157,4 +157,4 @@
 <p>At the workshop, I presented a couple of approaches I use in my own research, namely text mining, clustering, visualization of patterns, image analytics, and predictive modeling. Data ethnography can be used, not only in its native field of computational anthropology, but also in museology, digital anthropology, medical anthropology, and folkloristics (the list is probably not exhaustive). There are so many options just waiting for the researchers to dig in!</p>
 <p><strong>Related:</strong> <a href="/blog/2017/08/08/text-analysis-workshop-at-digital-humanities-2017/">Text Analysis Workshop at Digital Humanities 2017</a></p>
 <p>However, having data- and tech-savvy anthropologists does not only benefit the research, but opens a platform for discussing the ethics of data science, human relationships with technology, and overcoming model bias. Hopefully, the workshop inspired some of the participants to join me on a journey through the amazing expanses of data science.</p>
-<p>To get you inspired, here are two contributions that present some option for computational anthropological research: <a href="http://www.sdjt.si/wp/wp-content/uploads/2018/09/JTDH-2018_Pretnar-et-al_Data-Mining-Workspace-Sensors-A-New-Approach-to-Anthropology.pdf">Data Mining Workspace Sensors: A New Approach to Anthropology</a> and <a href="http://www.aiucd2018.uniba.it/content/posters/kozolec-pretnar-final.pdf">Power of Algorithms for Cultural Heritage Classification: The Case of Slovenian Hayracks</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-11-06 11:45:09+00:00","draft":false,"title":"Data Mining for Anthropologists?","blog":["education","text mining","workshop"],"shortExcerpt":"This weekend we were in Lisbon, Portugal, at the [Why the World Needs Anthropologists](https://www.applied-anthropology.com/) conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry.","oldUrl":"/blog/2018/11/06/data-mining-for-anthropologists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This weekend we were in Lisbon, Portugal, at the \", _jsx(_components.a, {\n        href: \"https://www.applied-anthropology.com/\",\n        children: \"Why the World Needs Anthropologists\"\n      }), \" conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry. I was there to hold a workshop on Data Ethnography, an approach that tries to combine methods from data science and anthropology into a fruitful interdisciplinary mix!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-11-data-mining-for-anthropologists/__optimized-images__/blog-header.jpg\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"759\"\n      }), \"\\nData Ethnography workshop at this year's Why the World Needs Anthropologists conference.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Data ethnography is a novel methodological approach that tries to view social phenomena from two different points of view - qualitative and quantitative. The quantitative approach is using data mining and machine learning methods on anthropological data (say from sensors, wearables, social media, online fora, field notes and so on) trying to find interesting patterns and novel information. 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+<p>To get you inspired, here are two contributions that present some option for computational anthropological research: <a href="http://www.sdjt.si/wp/wp-content/uploads/2018/09/JTDH-2018_Pretnar-et-al_Data-Mining-Workspace-Sensors-A-New-Approach-to-Anthropology.pdf">Data Mining Workspace Sensors: A New Approach to Anthropology</a> and <a href="http://www.aiucd2018.uniba.it/content/posters/kozolec-pretnar-final.pdf">Power of Algorithms for Cultural Heritage Classification: The Case of Slovenian Hayracks</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-11-06 11:45:09+00:00","draft":false,"title":"Data Mining for Anthropologists?","blog":["education","text mining","workshop"],"shortExcerpt":"This weekend we were in Lisbon, Portugal, at the [Why the World Needs Anthropologists](https://www.applied-anthropology.com/) conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry.","oldUrl":"/blog/2018/11/06/data-mining-for-anthropologists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This weekend we were in Lisbon, Portugal, at the \", _jsx(_components.a, {\n        href: \"https://www.applied-anthropology.com/\",\n        children: \"Why the World Needs Anthropologists\"\n      }), \" conference, an event that focuses on applied anthropology, design, and how soft skills can greatly benefit the industry. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>archaeology, workshop, image analytics, amphorae</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining for Archaeologists, part I</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 23, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Recently, we held a workshop for a group of archaeologists. While archaeologists are quite well-versed in quantitative analysis, data science was still quite new for most of the participants. Our aim was to introduce basic data science concepts through archaeological use cases. One such case that came to our mind was predicting a type of the artefact from the image.</p>
 <p>Related: <a href="blog/2018/11/06/data-mining-for-anthropologists/" target="_blank" rel="noreferrer">Data Mining for Anthropologists</a></p>
 <p>We took three best-documented amphora types (types with the highest number of images) from the <a href="https://archaeologydataservice.ac.uk/archives/view/amphora_ahrb_2005/index.cfm">Archaeology Data Service portal</a>. We also added some metadata describing each amphora subtype.</p>
@@ -176,4 +176,4 @@
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 <p>Seems like Dressel and Gauloise were successfully predicted, while Keay was mislabelled as a Gauloise. Not what we would have expected. Could archaeologists among you figure out, why this Keay amphora was mislabelled?</p>
 <a href="/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow-final.png" data-gallery="true"><img alt="" loading="lazy" width="1768" height="1054" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow-final.png"/></a>
-<p>In the second part of Data Mining for Archaeologists, we will have a look at geo-tagged data and how to plot them on a map.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-04-23","draft":false,"title":"Data Mining for Archaeologists, part I","thumbImage":"2021-04-23-archaeology-small.png","frontPageImage":"2021-04-23-archaeology-small.png","blog":["archaeology","workshop","image analytics","amphorae"],"shortExcerpt":"The many things archaeologists can do in Orange.","longExcerpt":"A workshop about different kinds of analyses archaeologists can do in Orange.","x2images":true,"oldUrl":"/blog/2021/2021-04-23-archaeology-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently, we held a workshop for a group of archaeologists. While archaeologists are quite well-versed in quantitative analysis, data science was still quite new for most of the participants. Our aim was to introduce basic data science concepts through archaeological use cases. One such case that came to our mind was predicting a type of the artefact from the image.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"blog/2018/11/06/data-mining-for-anthropologists/\",\n        name: \"Data Mining for Anthropologists\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We took three best-documented amphora types (types with the highest number of images) from the \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/archives/view/amphora_ahrb_2005/index.cfm\",\n        children: \"Archaeology Data Service portal\"\n      }), \". We also added some metadata describing each amphora subtype.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is how our data looks like.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-data-table.png\",\n      width: \"2652\",\n      height: \"968\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Each row represents one amphora, with type, image URL, subtype, and metadata included. Let us observe the data in an \", _jsx(_components.strong, {\n        children: \"Image Viewer\"\n      }), \" from the \", _jsx(_components.strong, {\n        children: \"Image Analytics\"\n      }), \" add-on.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-image-viewer.png\",\n      width: \"1754\",\n      height: \"1278\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-image-viewer.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow1.png\",\n      width: \"454\",\n      height: \"360\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Images now have to be converted to numbers, so that predictive models will know how to infer patterns from them. The procedure of describing an image with a vector is called embedding and in Orange, it can be found in \", _jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \" widget. We will use a simple, pre-trained Inception v3 model, but it is possible to train custom models specifically for archaeology.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The result of embedding is a long line of numbers.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-embedding.png\",\n      width: \"1654\",\n      height: \"736\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-embedding.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the predictive model to consider only image vectors, we need to move metadata to ... well, meta attributes. We will do this with \", _jsx(_components.strong, {\n        children: \"Select Columns\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-select-columns.png\",\n      width: \"1138\",\n      height: \"1154\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-select-columns.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow2.png\",\n      width: \"736\",\n      height: \"422\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, we can build our prediction model. Or a couple of them. We will use \", _jsx(_components.strong, {\n        children: \"Logistic Regression\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"kNN\"\n      }), \", and \", _jsx(_components.strong, {\n        children: \"SVM\"\n      }), \", as these are quite successful for working with images. We connect the data and the learners to \", _jsx(_components.strong, {\n        children: \"Test and Score\"\n      }), \". Seems like all of our models are quite accurate, with logistic regression having the highest AUC score.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-test-and-score.png\",\n      width: \"1554\",\n      height: \"920\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-test-and-score.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Looking at the \", _jsx(_components.strong, {\n        children: \"Confusion Matrix\"\n      }), \", logistic regression also best distinguishes between Dressels and Gauloises. It makes 13 mistakes, fairly equally confusing Dressels with Gauloises and vice versa. The other two classifiers more frequently confuse Gauloises for Dressels, so they are slightly biased in this sense.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-confusion-matrix.png\",\n      width: \"1422\",\n      height: \"722\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-confusion-matrix.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow3.png\",\n      width: \"1470\",\n      height: \"906\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow3.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It always makes sense to check the distribution of misclassifications to determine the quality of the model. If the model just predicts the most frequent class, it is useless. Having more data would surely make this model distinguish between amphora type better.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can use the model for predicting the type of amphora for unlabelled images. Go to the internet and find some Dressels, Gauloises, and Keays. I have three images here. I put them in a single folder and I will load them with \", _jsx(_components.strong, {\n        children: \"Import Images\"\n      }), \" widget. We have to pass the data through another \", _jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \" widget, because this data too needs numbers. Finally, we pass the data and one of the models (say, logistic regression) to \", _jsx(_components.strong, {\n        children: \"Predictions\"\n      }), \". Don't forget, logistic regression needs the data input to word with Predictions (you need to pass the model, not the learner).\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-predictions.png\",\n      width: \"1530\",\n      height: \"484\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-predictions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Seems like Dressel and Gauloise were successfully predicted, while Keay was mislabelled as a Gauloise. Not what we would have expected. 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+<p>In the second part of Data Mining for Archaeologists, we will have a look at geo-tagged data and how to plot them on a map.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-04-23","draft":false,"title":"Data Mining for Archaeologists, part I","thumbImage":"2021-04-23-archaeology-small.png","frontPageImage":"2021-04-23-archaeology-small.png","blog":["archaeology","workshop","image analytics","amphorae"],"shortExcerpt":"The many things archaeologists can do in Orange.","longExcerpt":"A workshop about different kinds of analyses archaeologists can do in Orange.","x2images":true,"oldUrl":"/blog/2021/2021-04-23-archaeology-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently, we held a workshop for a group of archaeologists. While archaeologists are quite well-versed in quantitative analysis, data science was still quite new for most of the participants. Our aim was to introduce basic data science concepts through archaeological use cases. One such case that came to our mind was predicting a type of the artefact from the image.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"blog/2018/11/06/data-mining-for-anthropologists/\",\n        name: \"Data Mining for Anthropologists\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We took three best-documented amphora types (types with the highest number of images) from the \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/archives/view/amphora_ahrb_2005/index.cfm\",\n        children: \"Archaeology Data Service portal\"\n      }), \". We also added some metadata describing each amphora subtype.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is how our data looks like.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-data-table.png\",\n      width: \"2652\",\n      height: \"968\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Each row represents one amphora, with type, image URL, subtype, and metadata included. Let us observe the data in an \", _jsx(_components.strong, {\n        children: \"Image Viewer\"\n      }), \" from the \", _jsx(_components.strong, {\n        children: \"Image Analytics\"\n      }), \" add-on.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-image-viewer.png\",\n      width: \"1754\",\n      height: \"1278\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-image-viewer.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow1.png\",\n      width: \"454\",\n      height: \"360\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Images now have to be converted to numbers, so that predictive models will know how to infer patterns from them. The procedure of describing an image with a vector is called embedding and in Orange, it can be found in \", _jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \" widget. We will use a simple, pre-trained Inception v3 model, but it is possible to train custom models specifically for archaeology.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The result of embedding is a long line of numbers.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-embedding.png\",\n      width: \"1654\",\n      height: \"736\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-embedding.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the predictive model to consider only image vectors, we need to move metadata to ... well, meta attributes. We will do this with \", _jsx(_components.strong, {\n        children: \"Select Columns\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-select-columns.png\",\n      width: \"1138\",\n      height: \"1154\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-select-columns.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow2.png\",\n      width: \"736\",\n      height: \"422\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, we can build our prediction model. Or a couple of them. We will use \", _jsx(_components.strong, {\n        children: \"Logistic Regression\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"kNN\"\n      }), \", and \", _jsx(_components.strong, {\n        children: \"SVM\"\n      }), \", as these are quite successful for working with images. We connect the data and the learners to \", _jsx(_components.strong, {\n        children: \"Test and Score\"\n      }), \". Seems like all of our models are quite accurate, with logistic regression having the highest AUC score.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-test-and-score.png\",\n      width: \"1554\",\n      height: \"920\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-test-and-score.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Looking at the \", _jsx(_components.strong, {\n        children: \"Confusion Matrix\"\n      }), \", logistic regression also best distinguishes between Dressels and Gauloises. It makes 13 mistakes, fairly equally confusing Dressels with Gauloises and vice versa. The other two classifiers more frequently confuse Gauloises for Dressels, so they are slightly biased in this sense.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-confusion-matrix.png\",\n      width: \"1422\",\n      height: \"722\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-confusion-matrix.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow3.png\",\n      width: \"1470\",\n      height: \"906\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow3.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It always makes sense to check the distribution of misclassifications to determine the quality of the model. If the model just predicts the most frequent class, it is useless. Having more data would surely make this model distinguish between amphora type better.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can use the model for predicting the type of amphora for unlabelled images. Go to the internet and find some Dressels, Gauloises, and Keays. I have three images here. I put them in a single folder and I will load them with \", _jsx(_components.strong, {\n        children: \"Import Images\"\n      }), \" widget. We have to pass the data through another \", _jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \" widget, because this data too needs numbers. Finally, we pass the data and one of the models (say, logistic regression) to \", _jsx(_components.strong, {\n        children: \"Predictions\"\n      }), \". Don't forget, logistic regression needs the data input to word with Predictions (you need to pass the model, not the learner).\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-predictions.png\",\n      width: \"1530\",\n      height: \"484\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-predictions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Seems like Dressel and Gauloise were successfully predicted, while Keay was mislabelled as a Gauloise. Not what we would have expected. Could archaeologists among you figure out, why this Keay amphora was mislabelled?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-04-archaeology-workshop/2021-04-23-workflow-final.png\",\n      width: \"1768\",\n      height: \"1054\",\n      src: \"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-workflow-final.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the second part of Data Mining for Archaeologists, we will have a look at geo-tagged data and how to plot them on a map.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":1200,"height":800,"src":"/blog/2021-04-archaeology-workshop/__optimized-images__/2021-04-23-archaeology-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-for-archaeologists-part-i"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>archaeology, workshop, preprocess, geolocation, maps</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining for Archaeologists, part II</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">May 30, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This is the second part of a blog on archaeological data analysis in Orange. In the first part, we wrote about image analytics and how to predict amphora types. This blog will show a simpler analysis, where we will plot excavation sites onto maps and interpret the results.</p>
 <p>Related: <a href="blog/2021/2021-04-23-archaeology-workshop/" target="_blank" rel="noreferrer">Data Mining for Archaeologists, part I</a></p>
 <p>This time we will be working with the pottery data from the <a href="https://archaeologydataservice.ac.uk/archives/view/antikythera_ahrc_2012/index.cfm">Antikythera Survey Project</a>. You can <a href="https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv&amp;hs=true">download the data</a> (pottery.csv) or simply copy-paste the below link in the <strong>File</strong> widget&#x27;s URL line.</p>
@@ -193,4 +193,4 @@
 <p>Connect <strong>Geo Map</strong> widget to <strong>Python Script</strong> (or directly to the <strong>File</strong> widget if you are using transformed data). Geo Map will automatically try to find latitude and longitude features. As with most visualizations in Orange, you can use other features to enhance the information gained from the plot. Let us use the <em>VesselPart</em> feature for coloring the points on the map.</p>
 <a href="/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-map.png" data-gallery="true"><img alt="" loading="lazy" width="1830" height="1346" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-map.png"/></a>
 <a href="/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-workflow2.png" data-gallery="true"><img alt="" loading="lazy" width="954" height="374" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-workflow2.png"/></a>
-<p>Now for some homework: in which part of the island are located shards from the Hellenic period? (Hint: Use Select Rows and set the <em>Hell</em> value to be higher than 70, then plot the subset on the map.)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-05-30","draft":false,"title":"Data Mining for Archaeologists, part II","thumbImage":"2021-05-30-archaeology-small.png","frontPageImage":"2021-05-30-archaeology-small.png","blog":["archaeology","workshop","preprocess","geolocation","maps"],"shortExcerpt":"Mapping excavation sites in Orange.","longExcerpt":"How to preprocess and map archaeological data sets.","x2images":true,"oldUrl":"/blog/2021/2021-05-30-archaeology-workshop2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"This is the second part of a blog on archaeological data analysis in Orange. In the first part, we wrote about image analytics and how to predict amphora types. This blog will show a simpler analysis, where we will plot excavation sites onto maps and interpret the results.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"blog/2021/2021-04-23-archaeology-workshop/\",\n        name: \"Data Mining for Archaeologists, part I\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This time we will be working with the pottery data from the \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/archives/view/antikythera_ahrc_2012/index.cfm\",\n        children: \"Antikythera Survey Project\"\n      }), \". You can \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\",\n        children: \"download the data\"\n      }), \" (pottery.csv) or simply copy-paste the below link in the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget's URL line.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\",\n        children: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\"\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-file.png\",\n      width: \"1170\",\n      height: \"1224\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-file.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"All is well, out data is here. Let us check it in the Box Plot, to see what are the distributions of our features.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-box-plot.png\",\n      width: \"1800\",\n      height: \"1056\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-box-plot.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-workflow1.png\",\n      width: \"418\",\n      height: \"192\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-workflow1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Hmmm, it seems like the \", _jsx(_components.em, {\n        children: \"VesselPart\"\n      }), \" feature contains a lot of bodies (B), bases (Ba), handles (H) and rims (R), while other shard parts are significantly less represented. Machine learning doesn't handle discrete features with many values very well, so we will preprocess the data to make it easier to interpret and handle downstream.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-edit-domain1.png\",\n      width: \"1226\",\n      height: \"1178\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-edit-domain1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For data preprocessing, we will use \", _jsx(_components.strong, {\n        children: \"Edit Domain\"\n      }), \". Let us select \", _jsx(_components.em, {\n        children: \"VesselPart\"\n      }), \". Feature values will be shown on the right. One option would be to manually merge values together, say \", _jsx(_components.em, {\n        children: \"Ba?\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Ba\"\n      }), \". We do this by selecting the two values and clicking M in the lower right corner. \", _jsx(_components.em, {\n        children: \"Group selected values\"\n      }), \" will group them together. Don't forget to give them a name, say \", _jsx(_components.em, {\n        children: \"Ba_all\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-edit-domain2.png\",\n      width: \"1226\",\n      height: \"1178\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-edit-domain2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To avoid doing this for a large number of values, Edit Domain offers a nice way to merge infrequent values together. The final option is called \", _jsx(_components.em, {\n        children: \"Group all except N most frequent values\"\n      }), \", where we can set the N ourselves. We know from Box Plot there are 4 fairly frequent values, while the others are less frequent. So let us select 4.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-edit-domain1.png\",\n      width: \"1226\",\n      height: \"1178\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-edit-domain1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the widget, you will see that all infrequent values are now merged and will appear under the label \", _jsx(_components.em, {\n        children: \"other\"\n      }), \". You can do the same for other features. Don't forget to press \", _jsx(_components.em, {\n        children: \"Apply\"\n      }), \" once you are done with preprocessing.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Check the results in a Box Plot. Much better!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-box-plot2.png\",\n      width: \"1604\",\n      height: \"958\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-box-plot2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, let us plot the data on a map. You might have noticed the pottery shards have a location, where they were excavated (XSugg and YSugg). Unfortunately, these coordinates are encoded in an \", _jsx(_components.em, {\n        children: \"EPSG:32634\"\n      }), \" system, so we will have to transform them to the more common \", _jsx(_components.em, {\n        children: \"EPSG:4326\"\n      }), \" projection.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this, we will need \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \" package, which you can install by going to Options - Add-ons. In the upper right corner, you will see \", _jsx(_components.em, {\n        children: \"Add more...\"\n      }), \" option. Select it. Now enter \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \" in the field. It will appear in the list of possible add-ons. Select it and run the installation.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-pyproj.png\",\n      width: \"1226\",\n      height: \"1120\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-pyproj.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \" is installed, you can use \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" widget to transform any projection to another one. Copy-paste the below script in the widget and run it.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" pyproj \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Transformer\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" ContinuousVariable\\n\\nnew_lon = []\\nnew_lat = []\\ntransformer = Transformer.from_crs(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"epsg:32634\\\"\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"epsg:4326\\\"\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"range\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"len\"\n        }), \"(in_data)):\\n    lat, lon = transformer.transform(in_data[i][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], in_data[i][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n    new_lat.append(lat)\\n    new_lon.append(lon)\\n\\nout = in_data.add_column(ContinuousVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lat\\\"\"\n        }), \"), new_lat)\\nout_data = out.add_column(ContinuousVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lon\\\"\"\n        }), \"), new_lon)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To avoid installing \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \", \", _jsx(_components.a, {\n        href: \"/blog/2021-05-archaeology-workshop2/file.biolab.si/datasets/pottery.tab\",\n        children: \"here\"\n      }), \" is the transformed data set. Load it with the File widget and continue along.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At last, it is time to plot the data on a map. For this, you will need \", _jsx(_components.strong, {\n        children: \"Geo\"\n      }), \" add-on, which you can also install in Options - Add-ons.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect \", _jsx(_components.strong, {\n        children: \"Geo Map\"\n      }), \" widget to \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" (or directly to the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget if you are using transformed data). Geo Map will automatically try to find latitude and longitude features. As with most visualizations in Orange, you can use other features to enhance the information gained from the plot. Let us use the \", _jsx(_components.em, {\n        children: \"VesselPart\"\n      }), \" feature for coloring the points on the map.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-map.png\",\n      width: \"1830\",\n      height: \"1346\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-map.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-workflow2.png\",\n      width: \"954\",\n      height: \"374\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-workflow2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now for some homework: in which part of the island are located shards from the Hellenic period? (Hint: Use Select Rows and set the \", _jsx(_components.em, {\n        children: \"Hell\"\n      }), \" value to be higher than 70, then plot the subset on the map.)\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-archaeology-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-for-archaeologists-part-ii"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Now for some homework: in which part of the island are located shards from the Hellenic period? (Hint: Use Select Rows and set the <em>Hell</em> value to be higher than 70, then plot the subset on the map.)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-05-30","draft":false,"title":"Data Mining for Archaeologists, part II","thumbImage":"2021-05-30-archaeology-small.png","frontPageImage":"2021-05-30-archaeology-small.png","blog":["archaeology","workshop","preprocess","geolocation","maps"],"shortExcerpt":"Mapping excavation sites in Orange.","longExcerpt":"How to preprocess and map archaeological data sets.","x2images":true,"oldUrl":"/blog/2021/2021-05-30-archaeology-workshop2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"This is the second part of a blog on archaeological data analysis in Orange. In the first part, we wrote about image analytics and how to predict amphora types. This blog will show a simpler analysis, where we will plot excavation sites onto maps and interpret the results.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"blog/2021/2021-04-23-archaeology-workshop/\",\n        name: \"Data Mining for Archaeologists, part I\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This time we will be working with the pottery data from the \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/archives/view/antikythera_ahrc_2012/index.cfm\",\n        children: \"Antikythera Survey Project\"\n      }), \". You can \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\",\n        children: \"download the data\"\n      }), \" (pottery.csv) or simply copy-paste the below link in the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget's URL line.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\",\n        children: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\"\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-file.png\",\n      width: \"1170\",\n      height: \"1224\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-file.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"All is well, out data is here. Let us check it in the Box Plot, to see what are the distributions of our features.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-box-plot.png\",\n      width: \"1800\",\n      height: \"1056\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-box-plot.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-workflow1.png\",\n      width: \"418\",\n      height: \"192\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-workflow1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Hmmm, it seems like the \", _jsx(_components.em, {\n        children: \"VesselPart\"\n      }), \" feature contains a lot of bodies (B), bases (Ba), handles (H) and rims (R), while other shard parts are significantly less represented. Machine learning doesn't handle discrete features with many values very well, so we will preprocess the data to make it easier to interpret and handle downstream.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-edit-domain1.png\",\n      width: \"1226\",\n      height: \"1178\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-edit-domain1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For data preprocessing, we will use \", _jsx(_components.strong, {\n        children: \"Edit Domain\"\n      }), \". Let us select \", _jsx(_components.em, {\n        children: \"VesselPart\"\n      }), \". Feature values will be shown on the right. One option would be to manually merge values together, say \", _jsx(_components.em, {\n        children: \"Ba?\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Ba\"\n      }), \". We do this by selecting the two values and clicking M in the lower right corner. \", _jsx(_components.em, {\n        children: \"Group selected values\"\n      }), \" will group them together. Don't forget to give them a name, say \", _jsx(_components.em, {\n        children: \"Ba_all\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-edit-domain2.png\",\n      width: \"1226\",\n      height: \"1178\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-edit-domain2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To avoid doing this for a large number of values, Edit Domain offers a nice way to merge infrequent values together. The final option is called \", _jsx(_components.em, {\n        children: \"Group all except N most frequent values\"\n      }), \", where we can set the N ourselves. We know from Box Plot there are 4 fairly frequent values, while the others are less frequent. So let us select 4.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-edit-domain1.png\",\n      width: \"1226\",\n      height: \"1178\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-edit-domain1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the widget, you will see that all infrequent values are now merged and will appear under the label \", _jsx(_components.em, {\n        children: \"other\"\n      }), \". You can do the same for other features. Don't forget to press \", _jsx(_components.em, {\n        children: \"Apply\"\n      }), \" once you are done with preprocessing.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Check the results in a Box Plot. Much better!\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-box-plot2.png\",\n      width: \"1604\",\n      height: \"958\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-box-plot2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, let us plot the data on a map. You might have noticed the pottery shards have a location, where they were excavated (XSugg and YSugg). Unfortunately, these coordinates are encoded in an \", _jsx(_components.em, {\n        children: \"EPSG:32634\"\n      }), \" system, so we will have to transform them to the more common \", _jsx(_components.em, {\n        children: \"EPSG:4326\"\n      }), \" projection.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this, we will need \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \" package, which you can install by going to Options - Add-ons. In the upper right corner, you will see \", _jsx(_components.em, {\n        children: \"Add more...\"\n      }), \" option. Select it. Now enter \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \" in the field. It will appear in the list of possible add-ons. Select it and run the installation.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-pyproj.png\",\n      width: \"1226\",\n      height: \"1120\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-pyproj.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \" is installed, you can use \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" widget to transform any projection to another one. Copy-paste the below script in the widget and run it.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" pyproj \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Transformer\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" ContinuousVariable\\n\\nnew_lon = []\\nnew_lat = []\\ntransformer = Transformer.from_crs(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"epsg:32634\\\"\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"epsg:4326\\\"\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"range\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"len\"\n        }), \"(in_data)):\\n    lat, lon = transformer.transform(in_data[i][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], in_data[i][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n    new_lat.append(lat)\\n    new_lon.append(lon)\\n\\nout = in_data.add_column(ContinuousVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lat\\\"\"\n        }), \"), new_lat)\\nout_data = out.add_column(ContinuousVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lon\\\"\"\n        }), \"), new_lon)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To avoid installing \", _jsx(_components.em, {\n        children: \"pyproj\"\n      }), \", \", _jsx(_components.a, {\n        href: \"/blog/2021-05-archaeology-workshop2/file.biolab.si/datasets/pottery.tab\",\n        children: \"here\"\n      }), \" is the transformed data set. Load it with the File widget and continue along.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At last, it is time to plot the data on a map. For this, you will need \", _jsx(_components.strong, {\n        children: \"Geo\"\n      }), \" add-on, which you can also install in Options - Add-ons.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect \", _jsx(_components.strong, {\n        children: \"Geo Map\"\n      }), \" widget to \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" (or directly to the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget if you are using transformed data). Geo Map will automatically try to find latitude and longitude features. As with most visualizations in Orange, you can use other features to enhance the information gained from the plot. Let us use the \", _jsx(_components.em, {\n        children: \"VesselPart\"\n      }), \" feature for coloring the points on the map.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-map.png\",\n      width: \"1830\",\n      height: \"1346\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-map.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-05-archaeology-workshop2/2021-05-30-workflow2.png\",\n      width: \"954\",\n      height: \"374\",\n      src: \"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-workflow2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now for some homework: in which part of the island are located shards from the Hellenic period? (Hint: Use Select Rows and set the \", _jsx(_components.em, {\n        children: \"Hell\"\n      }), \" value to be higher than 70, then plot the subset on the map.)\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-05-archaeology-workshop2/__optimized-images__/2021-05-30-archaeology-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-mining-for-archaeologists-part-ii"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>business intelligence, clustering, examples, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining for Business and Public Administration</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 17, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We&#x27;ve been having a blast with recent Orange workshops. While Blaž was getting tanned in India, Anže and I went to the charming Liverpool to hold a session for business school professors on how to teach business with Orange.</p>
 <p><strong>Related:</strong> <a href="/blog/2017/11/08/orange-in-kolkata-india/">Orange in Kolkata, India</a></p>
 <p>Obviously, when we say teach business, we mean how to do data mining for business, say predict churn or employee attrition, segment customers, find which items to recommend in an online store and track brand sentiment with text analysis.</p>
@@ -165,4 +165,4 @@
 <p><strong>Related:</strong> <a href="/blog/2017/10/26/analyzing-surveys/">Analyzing Surveys</a></p>
 <p>This group, similar to the one from India, was a pack of curious individuals who asked many interesting questions and were not shy to challenge us. How does a Tree know which attribute to split by? Is Tree better than Naive Bayes? Or is perhaps Logistic Regression better? How do we know which model works best? And finally, what is the mean of sauerkraut and beans? It has to be <a href="https://en.wikipedia.org/wiki/Istrian_stew">jota</a>!</p>
 <a href="/blog/2017-11-data-mining-business-public-administration/__optimized-images__/PANO_20171116_101558.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="568" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-11-data-mining-business-public-administration/__optimized-images__/PANO_20171116_101558.jpg"/></a>
-<p>Workshops are always fun, when you have a curious set of individuals who demand answers! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-11-17 12:35:40+00:00","draft":false,"title":"Data Mining for Business and Public Administration","blog":["business intelligence","clustering","examples","workshop"],"oldUrl":"/blog/2017/11/17/data-mining-business-public-administration/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We've been having a blast with recent Orange workshops. 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+<p>Workshops are always fun, when you have a curious set of individuals who demand answers! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-11-17 12:35:40+00:00","draft":false,"title":"Data Mining for Business and Public Administration","blog":["business intelligence","clustering","examples","workshop"],"oldUrl":"/blog/2017/11/17/data-mining-business-public-administration/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We've been having a blast with recent Orange workshops. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Data Mining for Political Scientists</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Data Mining for Political Scientists"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-11-30 08:24:53+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="education"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="prediction"/><meta property="article:tag" content="predictive analytics"/><meta property="article:tag" content="preprocessing"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="tutorial"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="27"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, classification, education, examples, orange3, prediction, predictive analytics, preprocessing, text mining, tutorial, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Mining for Political Scientists</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 30, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Being a political scientist, I did not even hear about data mining before I&#x27;ve joined <a href="https://www.facebook.com/biolab.si">Biolab</a>. And naturally, as with all good things, data mining started to grow on me. Give me some data, connect a bunch of widgets and see the magic happen!</p>
 <p>But hold on! There are still many social scientists out there who haven&#x27;t yet heard about the wonderful world of data mining, text mining and machine learning. So I&#x27;ve made it my mission to spread the word. And that was the spirit that led me back to my former university - <a href="http://www.politicalsciences.unibo.it/en/index.htm">School of Political Sciences, University of Bologna</a>.</p>
 <p>University of Bologna is the oldest university in the world and has one of the best departments for political sciences in Europe. I held a lecture <em>Digital Research - Data Mining for Political Scientists</em> for <a href="http://corsi.unibo.it/2Cycle/mirees/Pages/default.aspx">MIREES</a> students, who are specializing in research and studies in Central and Eastern Europe.</p>
@@ -166,4 +166,4 @@
 <a href="/blog/2016-11-data-mining-for-political-scientists/__optimized-images__/Bologna-topicmodelling.png" data-gallery="true"><img alt="" loading="lazy" width="917" height="356" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-11-data-mining-for-political-scientists/__optimized-images__/Bologna-topicmodelling.png"/></a>
 <p>Finding potential topics with LDA.</p>
 <a href="/blog/2016-11-data-mining-for-political-scientists/__optimized-images__/Bologna-emotions.png" data-gallery="true"><img alt="" loading="lazy" width="898" height="540" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-11-data-mining-for-political-scientists/__optimized-images__/Bologna-emotions.png"/></a>
-<p>Finally, we offered a sneak peek of our recent Tweet Profiler widget. Tweet Profiler is intended for sentiment analysis of tweets and can output classes. probabilities and embeddings. The widget is not yet officially available, but will be included in the upcoming release.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-11-30 08:24:53+00:00","draft":false,"title":"Data Mining for Political Scientists","blog":["analysis","classification","education","examples","orange3","prediction","predictive analytics","preprocessing","text mining","tutorial","widget"],"oldUrl":"/blog/2016/11/30/data-mining-for-political-scientists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Being a political scientist, I did not even hear about data mining before I've joined \", _jsx(_components.a, {\n        href: \"https://www.facebook.com/biolab.si\",\n        children: \"Biolab\"\n      }), \". 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+<p>Finally, we offered a sneak peek of our recent Tweet Profiler widget. Tweet Profiler is intended for sentiment analysis of tweets and can output classes. probabilities and embeddings. The widget is not yet officially available, but will be included in the upcoming release.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-11-30 08:24:53+00:00","draft":false,"title":"Data Mining for Political Scientists","blog":["analysis","classification","education","examples","orange3","prediction","predictive analytics","preprocessing","text mining","tutorial","widget"],"oldUrl":"/blog/2016/11/30/data-mining-for-political-scientists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Being a political scientist, I did not even hear about data mining before I've joined \", _jsx(_components.a, {\n        href: \"https://www.facebook.com/biolab.si\",\n        children: \"Biolab\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, business intelligence, data, feature engineering, preprocessing</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Preparation for Machine Learning</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 13, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We&#x27;ve said it numerous times and we&#x27;re going to say it again. Data preparation is crucial for any data analysis. If your data is messy, there&#x27;s no way you can make sense of it, let alone a computer. Computers are great at handling large, even enormous data sets, speedy computing and recognizing patterns. But they fail miserably if you give them the wrong input. Also some classification methods work better with binary values, other with continuous, so it is important to know how to treat your data properly.</p>
 <p>Orange is well equipped for such tasks.</p>
 <p><strong>Widget no. 1: Preprocess</strong></p>
@@ -188,4 +188,4 @@
 Original data.</p>
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 Empty columns and columns with the same (constant) value were removed.</p>
-<p>Of course, don&#x27;t forget to include all these procedures into your report with the &#x27;Report&#x27; button! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-01-13 09:25:58+00:00","draft":false,"title":"Data Preparation for Machine Learning","blog":["analysis","business intelligence","data","feature engineering","preprocessing"],"oldUrl":"/blog/2017/01/13/data-preparation-for-machine-learning/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    table: \"table\",\n    thead: \"thead\",\n    tr: \"tr\",\n    th: \"th\",\n    tbody: \"tbody\",\n    td: \"td\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We've said it numerous times and we're going to say it again. 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Also some classification methods work better with binary values, other with continuous, so it is important to know how to treat your data properly.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is well equipped for such tasks.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Widget no. 1: Preprocess\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/preprocess.png\",\n      alt: \"\",\n      width: \"205\",\n      height: \"95\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/data/preprocess/\",\n        children: \"Preprocess\"\n      }), \" is there to handle a big share of your preprocessing tasks.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/original-data.png\",\n      alt: \"\",\n      width: \"684\",\n      height: \"250\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"It can normalize numerical data variables. Say we have a fictional data set of people employed in your company. We want to know which employees are more likely to go on holiday, based on the yearly income, years employed in your company and total years of experience in the industry. If you plot this in heat map, you would see a bold yellow line at 'yearly income'. This obviously happens because yearly income has much higher values than years of experience or years employed by your company. You would naturally like the wage not to overweight the rest of the feature set, so normalization is the way to go. Normalization will transform your values to relative terms, that is, say (depending on the type of normalization) on a scale from 0 to 1. Now \", _jsx(_components.a, {\n            href: \"/widget-catalog/visualize/heatmap/\",\n            children: \"Heat Map\"\n          }), \" neatly shows that people who've been employed longer and have a higher wage more often go on holidays. (Yes, this is a totally fictional data set, but you see the point.)\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            align: \"center\",\n            children: _jsx(_components.img, {\n              src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/heatmap1.png\",\n              alt: \"\",\n              width: \"174\",\n              height: \"389\"\n            })\n          }), _jsx(_components.th, {\n            align: \"center\",\n            children: _jsx(_components.img, {\n              src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/heatmap2.png\",\n              alt: \"\",\n              width: \"178\",\n              height: \"396\"\n            })\n          })]\n        })\n      }), _jsx(_components.tbody, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"center\",\n            children: \"no normalization\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"normalized data\"\n          })]\n        })\n      })]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"It can impute missing values. Average or most frequent missing value imputation might seem as overly simple, but it actually works most of the time. Also, all the learners that require imputation do it implicitly, so the user doesn't have to configure yet another widget for that.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"If you want to compare your results against a randomly mixed data set, select 'Randomize' or if you want to select relevant features, this is the widget for it.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Preprocessing needs to be used with caution and understanding of your data to avoid losing important information or, worse, overfitting the model. A good example is a case of paramedics, who usually don't record pulse if it is normal. Missing values here thus cannot be imputed by an average value or random number, but as a distinct value (normal pulse). Domain knowledge is always crucial for data preparation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Widget no. 2: Discretize\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/discretize.png\",\n      alt: \"\",\n      width: \"324\",\n      height: \"98\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For certain tasks you might want to resort to binning, which is what \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/discretize/\",\n        children: \"Discretize\"\n      }), \" does. It effectively distributes your continuous values into a selected number of bins, thus making the variable discrete-like. You can either discretize all your data variables at once, using selected discretization type, or select a particular discretization method for each attribute. The cool thing is the transformation is already displayed in the widget, so you instantly know what you're getting in the end. A good example of discretization would be having a data set of your customers with their age recorded. It would make little sense to segment customers by each particular age, so binning them into 4 age groups (young, young-adult, middle-aged, senior) would be a great solution. Also some visualizations require feature transformation - \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/sievediagram/\",\n        children: \"Sieve Diagram\"\n      }), \" is currently one such widget. Mosaic Display, however, has the transformation already implemented internally.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/discretize1.png\",\n        alt: \"\",\n        width: \"720\",\n        height: \"310\"\n      }), \"\\noriginal data\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/discretize2.png\",\n        alt: \"\",\n        width: \"780\",\n        height: \"310\"\n      }), \"\\nDiscretized data with 'years employed' lower or higher then/equal to 8 (same for 'yearly income' and 'experience in the industry'.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Widget no. 3: Continuize\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/continuize.png\",\n      alt: \"\",\n      width: \"345\",\n      height: \"98\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget essentially creates new attributes out of your discrete ones. If you have, for example, an attribute with people's eye color, where values can be either blue, brown or green, you would probably want to have three separate attributes 'blue', 'green' and 'brown' with 0 or 1 if a person has that eye color. Some learners perform much better if data is transformed in such a way. You can also only have attributes where you would presume 0 is a normal condition and would only like to have deviations from the normal state recorded ('target or first value as base') or the normal state would be the most common value ('most frequent value as base'). \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/continuize/\",\n        children: \"Continuize\"\n      }), \" widget offers you a lot of room to play. 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That's what \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/purgedomain/\",\n        children: \"Purge Domain\"\n      }), \" does. If all of the values of some attributes are constant, it will remove these attributes. If you have unused (empty) attributes in your data, it will remove them. Effectively, you will get a nice and comprehensive data set in the end.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/purge1.png\",\n        alt: \"\",\n        width: \"893\",\n        height: \"94\"\n      }), \"\\nOriginal data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/purge2.png\",\n        alt: \"\",\n        width: \"401\",\n        height: \"94\"\n      }), \"\\nEmpty columns and columns with the same (constant) value were removed.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course, don't forget to include all these procedures into your report with the 'Report' button! :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"data-preparation-for-machine-learning"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Of course, don&#x27;t forget to include all these procedures into your report with the &#x27;Report&#x27; button! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-01-13 09:25:58+00:00","draft":false,"title":"Data Preparation for Machine Learning","blog":["analysis","business intelligence","data","feature engineering","preprocessing"],"oldUrl":"/blog/2017/01/13/data-preparation-for-machine-learning/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    table: \"table\",\n    thead: \"thead\",\n    tr: \"tr\",\n    th: \"th\",\n    tbody: \"tbody\",\n    td: \"td\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We've said it numerous times and we're going to say it again. Data preparation is crucial for any data analysis. If your data is messy, there's no way you can make sense of it, let alone a computer. Computers are great at handling large, even enormous data sets, speedy computing and recognizing patterns. But they fail miserably if you give them the wrong input. Also some classification methods work better with binary values, other with continuous, so it is important to know how to treat your data properly.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is well equipped for such tasks.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Widget no. 1: Preprocess\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/preprocess.png\",\n      alt: \"\",\n      width: \"205\",\n      height: \"95\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/data/preprocess/\",\n        children: \"Preprocess\"\n      }), \" is there to handle a big share of your preprocessing tasks.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/original-data.png\",\n      alt: \"\",\n      width: \"684\",\n      height: \"250\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"It can normalize numerical data variables. Say we have a fictional data set of people employed in your company. We want to know which employees are more likely to go on holiday, based on the yearly income, years employed in your company and total years of experience in the industry. If you plot this in heat map, you would see a bold yellow line at 'yearly income'. This obviously happens because yearly income has much higher values than years of experience or years employed by your company. You would naturally like the wage not to overweight the rest of the feature set, so normalization is the way to go. Normalization will transform your values to relative terms, that is, say (depending on the type of normalization) on a scale from 0 to 1. Now \", _jsx(_components.a, {\n            href: \"/widget-catalog/visualize/heatmap/\",\n            children: \"Heat Map\"\n          }), \" neatly shows that people who've been employed longer and have a higher wage more often go on holidays. (Yes, this is a totally fictional data set, but you see the point.)\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            align: \"center\",\n            children: _jsx(_components.img, {\n              src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/heatmap1.png\",\n              alt: \"\",\n              width: \"174\",\n              height: \"389\"\n            })\n          }), _jsx(_components.th, {\n            align: \"center\",\n            children: _jsx(_components.img, {\n              src: \"/blog/2017-01-data-preparation-for-machine-learning/__optimized-images__/heatmap2.png\",\n              alt: \"\",\n              width: \"178\",\n              height: \"396\"\n            })\n          })]\n        })\n      }), _jsx(_components.tbody, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"center\",\n            children: \"no normalization\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"normalized data\"\n          })]\n        })\n      })]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"It can impute missing values. Average or most frequent missing value imputation might seem as overly simple, but it actually works most of the time. Also, all the learners that require imputation do it implicitly, so the user doesn't have to configure yet another widget for that.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"If you want to compare your results against a randomly mixed data set, select 'Randomize' or if you want to select relevant features, this is the widget for it.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Preprocessing needs to be used with caution and understanding of your data to avoid losing important information or, worse, overfitting the model. A good example is a case of paramedics, who usually don't record pulse if it is normal. Missing values here thus cannot be imputed by an average value or random number, but as a distinct value (normal pulse). 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The cool thing is the transformation is already displayed in the widget, so you instantly know what you're getting in the end. A good example of discretization would be having a data set of your customers with their age recorded. It would make little sense to segment customers by each particular age, so binning them into 4 age groups (young, young-adult, middle-aged, senior) would be a great solution. Also some visualizations require feature transformation - \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/sievediagram/\",\n        children: \"Sieve Diagram\"\n      }), \" is currently one such widget. 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If you have, for example, an attribute with people's eye color, where values can be either blue, brown or green, you would probably want to have three separate attributes 'blue', 'green' and 'brown' with 0 or 1 if a person has that eye color. Some learners perform much better if data is transformed in such a way. You can also only have attributes where you would presume 0 is a normal condition and would only like to have deviations from the normal state recorded ('target or first value as base') or the normal state would be the most common value ('most frequent value as base'). \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/continuize/\",\n        children: \"Continuize\"\n      }), \" widget offers you a lot of room to play. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, text mining, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Data Science Made Easy: How To Identify Hate Comments with AI</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Dr. Sven Bingert &amp; Steffen Rörtgen</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 02, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The <a href="https://www.ideenexpo.de/">IdeenExpo</a> is a biennial participatory event for children, adolescents and young adults taking place in Hanover, Germany. Companies, research organizations, schools and universities participate to show young people the possibilities of the modern working world and gain their interest in technologies and natural sciences. As a part of one of the biggest research-computing-centers in North Germany the <a href="https://www.gwdg.de/home">GWDG</a> (Gesellschaft für wissenschaftliche Datenverarbeitung mbh Göttingen) took a part in that event to present the possibilities of Data Science and how its methods can be used in different areas.</p>
 <p>Related: <a href="/blog/2018/09/11/text-workshops-in-ljubljana/" target="_blank" rel="noreferrer">Text Workshops in Ljubljana</a></p>
 <p>Our goal was to give the 9th grade students a 60-minute hands-on introduction to some possible real-life use cases. As we were working with Orange3 now for some time, we decided to use it in our workshop, because it has the great benefit of being able to do data analysis without the need to write code, which wouldn&#x27;t have worked in a 60 minute workshop.</p>
@@ -161,4 +161,4 @@
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 <p>\</p>
 <p>With just the tweets we made up in our session we gained a precision value of 0.66 in the first session and after we appended the tweets from the second group, we already gained a value of 0.76. Afterwards the students were asked to made up 4 other tweets the model was not trained on and used the Predictions widget to see how well our model performed. Well, we just got the results we would have thought of, if we would have had to classify them on our own!</p>
-<p>Orange3 made it possible to develop a model for detecting hate comments in just 60 minutes with students, who had no programming skills. Thanks to the Orange Team!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Dr. Sven Bingert \u0026 Steffen Rörtgen","date":"2019-07-02","draft":false,"title":"Data Science Made Easy: How To Identify Hate Comments with AI","thumbImage":"twitter-police-workshop-picture.jpeg","frontPageImage":"twitter-police-workshop-picture.jpeg","blog":["education","text mining","workshop"],"shortExcerpt":"How to teach text mining and data science to the 9th grade students in 60 minutes?","longExcerpt":"How to teach text mining and data science to the 9th grade students in 60 minutes? 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+<p>Orange3 made it possible to develop a model for detecting hate comments in just 60 minutes with students, who had no programming skills. Thanks to the Orange Team!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Dr. Sven Bingert \u0026 Steffen Rörtgen","date":"2019-07-02","draft":false,"title":"Data Science Made Easy: How To Identify Hate Comments with AI","thumbImage":"twitter-police-workshop-picture.jpeg","frontPageImage":"twitter-police-workshop-picture.jpeg","blog":["education","text mining","workshop"],"shortExcerpt":"How to teach text mining and data science to the 9th grade students in 60 minutes?","longExcerpt":"How to teach text mining and data science to the 9th grade students in 60 minutes? 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, bioinformatics, bioorange, data, dataloading</strong></p><h1 class="sc-c67a4900-0 loxVcf">Datasets in Orange Bioinformatics Add-On</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 31, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>As you might know, Orange comes with several basic widget sets pre-installed. These allow you to upload and explore the data, visualize them, learn from them and make predictions. However, there are also some exciting add-ons available for installation. One of these is a <strong>bioinformatics add-on</strong>, which is our specialty.</p>
 <a href="/blog/2015-07-datasets-in-orange-bioinformatics-add-on/__optimized-images__/bioinformatics-blog.jpg" data-gallery="true"><img alt="" loading="lazy" width="571" height="141" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-datasets-in-orange-bioinformatics-add-on/__optimized-images__/bioinformatics-blog.jpg"/></a>
 <p>Bioinformatics widget set allows you to pursue complex analysis of gene expression by providing access to several external libraries. There are four widgets intended specifically for this - <strong>dictyExpress</strong>, <strong>GEO Data Sets</strong>, <strong>PIPAx</strong> and <strong>GenExpress</strong>. <a href="http://www.ncbi.nlm.nih.gov/gds">GEO Data Sets</a> are sourced from NCBI, <a href="http://pipa.biolab.si/hp/">PIPAx</a> and <a href="http://dictyexpress.biolab.si/">dictyExpress</a> from two Biolab projects, and finally <a href="https://www.genialis.com/genexpress/">GenExpress</a> from Genialis. A lot of the data is freely accessible, while you will need a user account for the rest.</p>
 <p>Once you open the widget, select the experiments you wish to use for your analysis and view it in the <strong>Data Table</strong> widget. You can compare these experiments in <strong>Data Profiles</strong>, visualize them in <strong>Volcano Plot</strong>, select the most relevant genes in <strong>Differential Expression</strong> widget and much more.</p>
 <a href="/blog/2015-07-datasets-in-orange-bioinformatics-add-on/__optimized-images__/BioinfoDatasets.png" data-gallery="true"><img alt="" loading="lazy" width="1000" height="800" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-datasets-in-orange-bioinformatics-add-on/__optimized-images__/BioinfoDatasets.png"/></a>
 <p>Three widgets with experiment data libraries.</p>
-<p>These databases enable you to start your research just by installing the bioinformatics add-on (Orange → Options → Add-ons…). The great thing is you can easily combine bioinformatics widgets with the basic pre-installed ones. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>debian, distribution, download, packaging</strong></p><h1 class="sc-c67a4900-0 loxVcf">Debian packages for Squeeze</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 30, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We have updated our daily Debian packages to Squeeze (current Debian stable). You just have to reconfigure our package repository in your <strong>/etc/apt/sources.list</strong> to:</p>
 <pre><code class="hljs language-bash">deb http://orange.biolab.si/debian squeeze main
 deb-src http://orange.biolab.si/debian squeeze main
 </code></pre>
 <p>Those packages are compiled for Python 2.6.</p>
-<p>You can read more about Debian packages in our old <a href="/blog/2010/03/04/debian-repository-lives/">blog post</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-06-30 00:44:00+00:00","draft":false,"title":"Debian packages for Squeeze","blog":["debian","distribution","download","packaging"],"oldUrl":"/blog/2011/06/30/debian-packages-for-squeeze/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have updated our daily Debian packages to Squeeze (current Debian stable). You just have to reconfigure our package repository in your \", _jsx(_components.strong, {\n        children: \"/etc/apt/sources.list\"\n      }), \" to:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"deb http://orange.biolab.si/debian squeeze main\\ndeb-src http://orange.biolab.si/debian squeeze main\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Those packages are compiled for Python 2.6.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can read more about Debian packages in our old \", _jsx(_components.a, {\n        href: \"/blog/2010/03/04/debian-repository-lives/\",\n        children: \"blog post\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"debian-packages-for-squeeze"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>You can read more about Debian packages in our old <a href="/blog/2010/03/04/debian-repository-lives/">blog post</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-06-30 00:44:00+00:00","draft":false,"title":"Debian packages for Squeeze","blog":["debian","distribution","download","packaging"],"oldUrl":"/blog/2011/06/30/debian-packages-for-squeeze/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have updated our daily Debian packages to Squeeze (current Debian stable). You just have to reconfigure our package repository in your \", _jsx(_components.strong, {\n        children: \"/etc/apt/sources.list\"\n      }), \" to:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        className: \"hljs language-bash\",\n        children: \"deb http://orange.biolab.si/debian squeeze main\\ndeb-src http://orange.biolab.si/debian squeeze main\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Those packages are compiled for Python 2.6.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can read more about Debian packages in our old \", _jsx(_components.a, {\n        href: \"/blog/2010/03/04/debian-repository-lives/\",\n        children: \"blog post\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"debian-packages-for-squeeze"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/debian-packages-support-multiple-python-versions-now/index.html b/blog/debian-packages-support-multiple-python-versions-now/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>debian, packaging, python</strong></p><h1 class="sc-c67a4900-0 loxVcf">Debian packages support multiple Python versions now</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 13, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We have created Debian packages for multiple Python versions. This means that they work now with both Python 2.6 and 2.7 out of the box, or if you compile them manually, with any (supported) version you have installed on your (Debian-based) system.</p>
-<p>Practically, this means that now you can install them without manual compiling on current Debian and Ubuntu systems. Give it a try, <a href="/download/">add our Debian package repository</a>, apt-get install <strong>python-orange</strong> for Orange library/modules and/or <strong>orange-canvas</strong> for GUI. If you install the later package, type <strong>orange</strong> in the terminal and Orange canvas will pop-up.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-13 00:32:00+00:00","draft":false,"title":"Debian packages support multiple Python versions now","blog":["debian","packaging","python"],"oldUrl":"/blog/2011/09/13/debian-packages-support-multiple-python-versions-now/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We have created Debian packages for multiple Python versions. This means that they work now with both Python 2.6 and 2.7 out of the box, or if you compile them manually, with any (supported) version you have installed on your (Debian-based) system.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Practically, this means that now you can install them without manual compiling on current Debian and Ubuntu systems. Give it a try, \", _jsx(_components.a, {\n        href: \"/download/\",\n        children: \"add our Debian package repository\"\n      }), \", apt-get install \", _jsx(_components.strong, {\n        children: \"python-orange\"\n      }), \" for Orange library/modules and/or \", _jsx(_components.strong, {\n        children: \"orange-canvas\"\n      }), \" for GUI. If you install the later package, type \", _jsx(_components.strong, {\n        children: \"orange\"\n      }), \" in the terminal and Orange canvas will pop-up.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"debian-packages-support-multiple-python-versions-now"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Practically, this means that now you can install them without manual compiling on current Debian and Ubuntu systems. Give it a try, <a href="/download/">add our Debian package repository</a>, apt-get install <strong>python-orange</strong> for Orange library/modules and/or <strong>orange-canvas</strong> for GUI. If you install the later package, type <strong>orange</strong> in the terminal and Orange canvas will pop-up.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-13 00:32:00+00:00","draft":false,"title":"Debian packages support multiple Python versions now","blog":["debian","packaging","python"],"oldUrl":"/blog/2011/09/13/debian-packages-support-multiple-python-versions-now/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We have created Debian packages for multiple Python versions. This means that they work now with both Python 2.6 and 2.7 out of the box, or if you compile them manually, with any (supported) version you have installed on your (Debian-based) system.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Practically, this means that now you can install them without manual compiling on current Debian and Ubuntu systems. Give it a try, \", _jsx(_components.a, {\n        href: \"/download/\",\n        children: \"add our Debian package repository\"\n      }), \", apt-get install \", _jsx(_components.strong, {\n        children: \"python-orange\"\n      }), \" for Orange library/modules and/or \", _jsx(_components.strong, {\n        children: \"orange-canvas\"\n      }), \" for GUI. If you install the later package, type \", _jsx(_components.strong, {\n        children: \"orange\"\n      }), \" in the terminal and Orange canvas will pop-up.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"debian-packages-support-multiple-python-versions-now"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/debian-repository-lives/index.html b/blog/debian-repository-lives/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Debian repository lives!</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Debian repository lives!"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2010-03-04 01:56:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="debian"/><meta property="article:tag" content="distribution"/><meta property="article:tag" content="download"/><meta property="article:tag" content="packaging"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>debian, distribution, download, packaging</strong></p><h1 class="sc-c67a4900-0 loxVcf">Debian repository lives!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 04, 2010</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We have made still-experimental-but-probably-working Debian repository with daily built Orange packages. Currently without add-ons.</p>
 <p>To get access to those packages just add those two lines to your /etc/apt/sources.list (this file contains a list of repositories with packages):</p>
 <pre><code class="hljs language-bash">deb http://orange.biolab.si/debian lenny main
@@ -167,4 +167,4 @@
 <p>And then build package by yourself in extracted source directory with:</p>
 <pre><code class="hljs language-bash">dpkg-buildpackage
 </code></pre>
-<p>For example this will be useful on amd64 platform for which we currently do not yet provide binary packages. (Edit: now we provide binary packages also for amd64 platform.) But we will once we see how well this system works.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2010-03-04 01:56:00+00:00","draft":false,"title":"Debian repository lives!","blog":["debian","distribution","download","packaging"],"oldUrl":"/blog/2010/03/04/debian-repository-lives/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We have made still-experimental-but-probably-working Debian repository with daily built Orange packages. 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+<p>For example this will be useful on amd64 platform for which we currently do not yet provide binary packages. (Edit: now we provide binary packages also for amd64 platform.) But we will once we see how well this system works.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2010-03-04 01:56:00+00:00","draft":false,"title":"Debian repository lives!","blog":["debian","distribution","download","packaging"],"oldUrl":"/blog/2010/03/04/debian-repository-lives/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We have made still-experimental-but-probably-working Debian repository with daily built Orange packages. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>text mining, sentiment analysis, corpus, story arc, heat map, line chart</strong></p><h1 class="sc-c67a4900-0 loxVcf">Detecting Story Arcs with Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 27, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Reading is fun because it takes you on a journey. Mostly, it is a journey of emotions as you live and breathe with the protagonist and her adventures. Today, we will have a look at how to detect sentiment in a story, plot story arcs and analyze the content of the key segments in a corpus.</p>
 <p>Related: <a href="/blog/2018/09/11/text-workshops-in-ljubljana/" target="_blank" rel="noreferrer">Text Workshops in Ljubljana</a></p>
 <p>We will be using a corpus of Anderson&#x27;s tales, which is available in the <strong>Corpus</strong> widget (data set <em>anderson.tab</em>). Load it in the widget. Next, we will select a single tale which we will analyze, say, Little Match Seller. Connect Corpus to <strong>Data Table</strong> and select the tale. We all know the story of a little girl selling matches on a New Year&#x27;s Eve and freezing to death. It is one of the saddest stories ever told. One could almost forget there are positive parts, such as the girl&#x27;s visions in the moments before her death, which show a glimmer of hope, the only consolation the girl had in her life. Let us verify this in Orange.</p>
@@ -201,4 +201,4 @@
 <p>To finish, let us explore the positive sentences, too. Select the positive section in the Heat Map and observe it in a Corpus Viewer. Now rethink the story, reread her visions in the last moments of her life and how happy she was when she died. Couldn&#x27;t we say that ... the story has a happy ending?</p>
 <a href="/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-positive-sentiment.png" data-gallery="true"><img alt="" loading="lazy" width="733" height="635" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-positive-sentiment.png"/></a>
 <p>While the workflow is quite long, it is conceptually very simple. This is a quick and easy way to explore the story arcs and sentiment in a text. We imagine this to be a very useful tool for the teachers who wish to experiment a bit in their language classes and offer a fun and fruitful way of exploring literature.</p>
-<a href="/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-final-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="2306" height="440" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-final-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-07-27","draft":false,"title":"Detecting Story Arcs with Orange","thumbImage":"2020-07-27-story-arcs-small.png","frontPageImage":"2020-07-27-story-arcs-small.png","blog":["text mining","sentiment analysis","corpus","story arc","heat map","line chart"],"shortExcerpt":"How to detect and analyze story arcs in a corpus.","longExcerpt":"How to detect sentiment, plot story arcs and analyze the key segments in a corpus.","x2images":false,"aliases":["/blog/2021/2021-04-16-story-arcs/"],"oldUrl":"/blog/2020/2020-07-27-story-arcs/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Reading is fun because it takes you on a journey. Mostly, it is a journey of emotions as you live and breathe with the protagonist and her adventures. Today, we will have a look at how to detect sentiment in a story, plot story arcs and analyze the content of the key segments in a corpus.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/09/11/text-workshops-in-ljubljana/\",\n        name: \"Text Workshops in Ljubljana\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will be using a corpus of Anderson's tales, which is available in the \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget (data set \", _jsx(_components.em, {\n        children: \"anderson.tab\"\n      }), \"). Load it in the widget. Next, we will select a single tale which we will analyze, say, Little Match Seller. Connect Corpus to \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \" and select the tale. We all know the story of a little girl selling matches on a New Year's Eve and freezing to death. It is one of the saddest stories ever told. One could almost forget there are positive parts, such as the girl's visions in the moments before her death, which show a glimmer of hope, the only consolation the girl had in her life. Let us verify this in Orange.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-corpus.png\",\n      width: \"454\",\n      height: \"336\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-corpus.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-data-table.png\",\n      width: \"513\",\n      height: \"192\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-data-table.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf1.png\",\n      width: \"246\",\n      height: \"98\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"With our story selected, we have to split it into sentences. At the moment, our story is a single row in the data, but we wish to have each sentence in its own row. We will use \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" and select Sentence tokenization. I have removed the redundant preprocessors and kept the only one we need.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-preprocess.png\",\n      width: \"569\",\n      height: \"526\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-preprocess.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Some Python magic will help us create a new corpus from the existing tokens (sentences). Copy and paste the script below into the \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" widget. Do not forget to press \", _jsx(_components.strong, {\n        children: \"Run\"\n      }), \" once you have pasted the script into the widget.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Domain, StringVariable\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" orangecontrib.text.corpus \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Corpus\\n\\ntokens = in_data.tokens\\nnew_domain = Domain(attributes=[], metas=[StringVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Sentences'\"\n        }), \"), StringVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Title'\"\n        }), \")])\\n\\ntitles = []\\ncontent = []\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i, doc \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"enumerate\"\n        }), \"(tokens):\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" t \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" doc:\\n        titles.append(in_data[i][\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Title'\"\n        }), \"].value)\\n        content.append(t)\\n\\nmetas = np.column_stack((content, titles))\\nout_data = Corpus.from_numpy(domain=new_domain, X=np.empty((\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"len\"\n        }), \"(content), \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \")),\\n                            metas=metas)\\nout_data.set_text_features([StringVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Sentences'\"\n        }), \")])\\n\"]\n      })\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf2.png\",\n      width: \"556\",\n      height: \"109\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Perfect, our data is now ready for the final step. Add another Preprocess Text and keep the default preprocessors. They will lowercase our sentences, split by words and remove English stopwords.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-preprocess2.png\",\n      width: \"735\",\n      height: \"571\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-preprocess2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, add the \", _jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \" widget. By default, the widget uses the Vader algorithm, which works quite well for English. Please note that it won't work for other languages, as all of Orange's sentiment models are language specific. You can use Multilingual sentiment for non-English texts.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-sentiment-analysis.png\",\n      width: \"402\",\n      height: \"249\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-sentiment-analysis.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf3.png\",\n      width: \"853\",\n      height: \"110\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf3.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At last, it is time to analyze the data. We will use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-timeseries\",\n        children: \"Timeseries add-on\"\n      }), \" to plot sequential data. First, we will pass the data to the \", _jsx(_components.strong, {\n        children: \"As Timeseries\"\n      }), \" widget and set the \", _jsx(_components.em, {\n        children: \"Sequence implied by the instance order\"\n      }), \" option. This tells Orange that our data is already ordered by time - in our case, by the order in which each sentence appears in the story.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-as-timeseries.png\",\n      width: \"297\",\n      height: \"225\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-as-timeseries.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect \", _jsx(_components.strong, {\n        children: \"Line Chart\"\n      }), \" to As Timeseries. In the widget, select \", _jsx(_components.em, {\n        children: \"Compound\"\n      }), \" variable, which shows the total sentiment of each sentence. The peaks represent the parts of the story with positive emotions and the drops the parts with the negative ones.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-line-chart.png\",\n      width: \"891\",\n      height: \"327\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-line-chart.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf4.png\",\n      width: \"574\",\n      height: \"107\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf4.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To explore the data in depth, connect \", _jsx(_components.strong, {\n        children: \"Heat Map\"\n      }), \" to Sentiment Analysis. Heat Map will show all 4 sentiment attributes, namely positive (pos), negative (neg), neutral (neu) and compound sentiment. But our data is all over the place. Let us order it. Select \", _jsx(_components.em, {\n        children: \"Clustering (opt. ordering)\"\n      }), \" under the Clustering - Rows option. This will cluster the sentences by their similarity, specifically by how similar their emotion is.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-heat-map.png\",\n      width: \"569\",\n      height: \"757\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-heat-map.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Great! Now this looks like something useful. The blue sections represent the negative sentences, while the yellow and white sections represent the positive ones. Let us select the cluster with negative sentences and observe it in a \", _jsx(_components.strong, {\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-negative-sentiment.png\",\n      width: \"733\",\n      height: \"635\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-negative-sentiment.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf5.png\",\n      width: \"423\",\n      height: \"205\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf5.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I have set Sentences as the single Display variable and selected all the sentences to show them in a list. Unsurprisingly, here is the last sentence of the story, in which a girl if found dead in the street on a New Year's Day.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To finish, let us explore the positive sentences, too. Select the positive section in the Heat Map and observe it in a Corpus Viewer. Now rethink the story, reread her visions in the last moments of her life and how happy she was when she died. Couldn't we say that ... the story has a happy ending?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-positive-sentiment.png\",\n      width: \"733\",\n      height: \"635\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-positive-sentiment.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While the workflow is quite long, it is conceptually very simple. This is a quick and easy way to explore the story arcs and sentiment in a text. We imagine this to be a very useful tool for the teachers who wish to experiment a bit in their language classes and offer a fun and fruitful way of exploring literature.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-final-workflow.png\",\n      width: \"2306\",\n      height: \"440\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-final-workflow.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":599,"height":400,"src":"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-story-arcs-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"detecting-story-arcs-with-orange"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<a href="/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-final-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="2306" height="440" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-final-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-07-27","draft":false,"title":"Detecting Story Arcs with Orange","thumbImage":"2020-07-27-story-arcs-small.png","frontPageImage":"2020-07-27-story-arcs-small.png","blog":["text mining","sentiment analysis","corpus","story arc","heat map","line chart"],"shortExcerpt":"How to detect and analyze story arcs in a corpus.","longExcerpt":"How to detect sentiment, plot story arcs and analyze the key segments in a corpus.","x2images":false,"aliases":["/blog/2021/2021-04-16-story-arcs/"],"oldUrl":"/blog/2020/2020-07-27-story-arcs/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Reading is fun because it takes you on a journey. Mostly, it is a journey of emotions as you live and breathe with the protagonist and her adventures. Today, we will have a look at how to detect sentiment in a story, plot story arcs and analyze the content of the key segments in a corpus.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/09/11/text-workshops-in-ljubljana/\",\n        name: \"Text Workshops in Ljubljana\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will be using a corpus of Anderson's tales, which is available in the \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget (data set \", _jsx(_components.em, {\n        children: \"anderson.tab\"\n      }), \"). Load it in the widget. Next, we will select a single tale which we will analyze, say, Little Match Seller. Connect Corpus to \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \" and select the tale. We all know the story of a little girl selling matches on a New Year's Eve and freezing to death. It is one of the saddest stories ever told. One could almost forget there are positive parts, such as the girl's visions in the moments before her death, which show a glimmer of hope, the only consolation the girl had in her life. Let us verify this in Orange.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-corpus.png\",\n      width: \"454\",\n      height: \"336\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-corpus.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-data-table.png\",\n      width: \"513\",\n      height: \"192\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-data-table.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf1.png\",\n      width: \"246\",\n      height: \"98\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"With our story selected, we have to split it into sentences. At the moment, our story is a single row in the data, but we wish to have each sentence in its own row. We will use \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" and select Sentence tokenization. I have removed the redundant preprocessors and kept the only one we need.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-preprocess.png\",\n      width: \"569\",\n      height: \"526\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-preprocess.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Some Python magic will help us create a new corpus from the existing tokens (sentences). Copy and paste the script below into the \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" widget. Do not forget to press \", _jsx(_components.strong, {\n        children: \"Run\"\n      }), \" once you have pasted the script into the widget.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" np\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" Orange.data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Domain, StringVariable\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" orangecontrib.text.corpus \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Corpus\\n\\ntokens = in_data.tokens\\nnew_domain = Domain(attributes=[], metas=[StringVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Sentences'\"\n        }), \"), StringVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Title'\"\n        }), \")])\\n\\ntitles = []\\ncontent = []\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i, doc \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"enumerate\"\n        }), \"(tokens):\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" t \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" doc:\\n        titles.append(in_data[i][\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Title'\"\n        }), \"].value)\\n        content.append(t)\\n\\nmetas = np.column_stack((content, titles))\\nout_data = Corpus.from_numpy(domain=new_domain, X=np.empty((\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"len\"\n        }), \"(content), \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \")),\\n                            metas=metas)\\nout_data.set_text_features([StringVariable(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Sentences'\"\n        }), \")])\\n\"]\n      })\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf2.png\",\n      width: \"556\",\n      height: \"109\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Perfect, our data is now ready for the final step. Add another Preprocess Text and keep the default preprocessors. They will lowercase our sentences, split by words and remove English stopwords.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-preprocess2.png\",\n      width: \"735\",\n      height: \"571\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-preprocess2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, add the \", _jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \" widget. By default, the widget uses the Vader algorithm, which works quite well for English. Please note that it won't work for other languages, as all of Orange's sentiment models are language specific. You can use Multilingual sentiment for non-English texts.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-sentiment-analysis.png\",\n      width: \"402\",\n      height: \"249\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-sentiment-analysis.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf3.png\",\n      width: \"853\",\n      height: \"110\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf3.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At last, it is time to analyze the data. We will use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-timeseries\",\n        children: \"Timeseries add-on\"\n      }), \" to plot sequential data. First, we will pass the data to the \", _jsx(_components.strong, {\n        children: \"As Timeseries\"\n      }), \" widget and set the \", _jsx(_components.em, {\n        children: \"Sequence implied by the instance order\"\n      }), \" option. This tells Orange that our data is already ordered by time - in our case, by the order in which each sentence appears in the story.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-as-timeseries.png\",\n      width: \"297\",\n      height: \"225\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-as-timeseries.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect \", _jsx(_components.strong, {\n        children: \"Line Chart\"\n      }), \" to As Timeseries. In the widget, select \", _jsx(_components.em, {\n        children: \"Compound\"\n      }), \" variable, which shows the total sentiment of each sentence. The peaks represent the parts of the story with positive emotions and the drops the parts with the negative ones.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-line-chart.png\",\n      width: \"891\",\n      height: \"327\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-line-chart.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf4.png\",\n      width: \"574\",\n      height: \"107\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf4.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To explore the data in depth, connect \", _jsx(_components.strong, {\n        children: \"Heat Map\"\n      }), \" to Sentiment Analysis. Heat Map will show all 4 sentiment attributes, namely positive (pos), negative (neg), neutral (neu) and compound sentiment. But our data is all over the place. Let us order it. Select \", _jsx(_components.em, {\n        children: \"Clustering (opt. ordering)\"\n      }), \" under the Clustering - Rows option. This will cluster the sentences by their similarity, specifically by how similar their emotion is.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-heat-map.png\",\n      width: \"569\",\n      height: \"757\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-heat-map.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Great! Now this looks like something useful. The blue sections represent the negative sentences, while the yellow and white sections represent the positive ones. Let us select the cluster with negative sentences and observe it in a \", _jsx(_components.strong, {\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-negative-sentiment.png\",\n      width: \"733\",\n      height: \"635\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-negative-sentiment.png\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-wf5.png\",\n      width: \"423\",\n      height: \"205\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-wf5.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I have set Sentences as the single Display variable and selected all the sentences to show them in a list. Unsurprisingly, here is the last sentence of the story, in which a girl if found dead in the street on a New Year's Day.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To finish, let us explore the positive sentences, too. Select the positive section in the Heat Map and observe it in a Corpus Viewer. Now rethink the story, reread her visions in the last moments of her life and how happy she was when she died. Couldn't we say that ... the story has a happy ending?\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-positive-sentiment.png\",\n      width: \"733\",\n      height: \"635\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-positive-sentiment.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While the workflow is quite long, it is conceptually very simple. This is a quick and easy way to explore the story arcs and sentiment in a text. We imagine this to be a very useful tool for the teachers who wish to experiment a bit in their language classes and offer a fun and fruitful way of exploring literature.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-07-story-arcs/2020-07-27-final-workflow.png\",\n      width: \"2306\",\n      height: \"440\",\n      src: \"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-final-workflow.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":599,"height":400,"src":"/blog/2020-07-story-arcs/__optimized-images__/2020-07-27-story-arcs-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"detecting-story-arcs-with-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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@@ -165,4 +165,4 @@
 <p>Ok, how about MDS? This is beyond our expectations!</p>
 <a href="/blog/2016-12-dimensionality-reduction-by-manifold-learning/__optimized-images__/SP-mds.png" data-gallery="true"><img alt="" loading="lazy" width="767" height="676" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-12-dimensionality-reduction-by-manifold-learning/__optimized-images__/SP-mds.png"/></a>
 <p>There&#x27;s a plethora of options with embeddings. You can play around with ImageNet embeddings and plot them in 2D or use any of your own high-dimensional data and discover interesting visualizations! Although t-SNE is nowadays probably the most popular dimensionality reduction technique used in combination with scatterplot visualization, do not underestimate the value of other manifold learning techniques. For one, we often find that MDS works fine as well.</p>
-<p>Go, experiment!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-12-12 07:57:27+00:00","draft":false,"title":"Dimensionality Reduction by Manifold Learning","blog":["analysis","embedding","examples","interactive data visualization","orange3","unsupervised","visualization","widget"],"oldUrl":"/blog/2016/12/12/dimensionality-reduction-by-manifold-learning/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The new Orange release (v. 3.3.9) welcomed a few wonderful additions to its widget family, including Manifold Learning widget. 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+<p>Go, experiment!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-12-12 07:57:27+00:00","draft":false,"title":"Dimensionality Reduction by Manifold Learning","blog":["analysis","embedding","examples","interactive data visualization","orange3","unsupervised","visualization","widget"],"oldUrl":"/blog/2016/12/12/dimensionality-reduction-by-manifold-learning/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The new Orange release (v. 3.3.9) welcomed a few wonderful additions to its widget family, including Manifold Learning widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, interactive data visualization, orange3, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Diving Into Car Registration Data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>ASTARIC</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 13, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Last week, we presented Orange at the <a href="https://podatki.gov.si/content/festival-odprtih-podatkov-2017">Festival of Open Data</a>, a mini-conference organized by the Slovenian government, dedicated to the promotion of transparent access to government data. In a 10 minute presentation, we showed how Orange can be used to visualize and explore what kinds of vehicles were registered for the first time in Slovenia in 2017.</p>
 <a href="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/IMG_3530.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="1200" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/IMG_3530.jpg"/></a>
 <p>The original dataset is available at the<a href="https://podatki.gov.si/dataset/prvic-registrirana-vozila-po-mesecih"> OPSI portal</a> and it consists of 73 files, one for each month since January 2012. For the presentation, we focused on the 2017 data. If you want to follow along, you can download the <a href="https://www.dropbox.com/s/rb4gx2856f34i47/OsebnaVozila.tab?dl=0">merged dataset (first 9 months of 2017 as a single file)</a>. The <a href="https://www.dropbox.com/s/i0sckc7e8kll4g8/Priprava%20podatkov.ows?dl=0">workflow I used to prepare the data</a> is also available.</p>
@@ -161,4 +161,4 @@
 <p>In Box Plot, select D.1 Znamka as both the variable and Subgroup and you get an overview of the distribution of cars by manufacturers in the selected region. But that is just the first step. We can also take a look at the distribution of Fiat cars by adding another boxplot. Now you can select the manufacturer and get a detailed distribution of specific car models sold. If you take some care in positioning the windows, you can create an interactive explorer, where you click on regions and instantly see the detailed distributions in the connected boxplots.</p>
 <a href="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/Screen-Shot-2017-10-10-at-13.03.26.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="643" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/Screen-Shot-2017-10-10-at-13.03.26.png"/></a>
 <p>The <a href="https://www.dropbox.com/s/p00wpnbmygajgoc/cars.ows?dl=0">final workflow</a> should look like this:</p>
-<a href="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/Screen-Shot-2017-10-10-at-13.04.47.png" data-gallery="true"><img alt="" loading="lazy" width="583" height="256" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/Screen-Shot-2017-10-10-at-13.04.47.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"ASTARIC","date":"2017-10-13 12:04:32+00:00","draft":false,"title":"Diving Into Car Registration Data","blog":["addons","analysis","interactive data visualization","orange3","visualization"],"oldUrl":"/blog/2017/10/13/diving-into-car-registration-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week, we presented Orange at the \", _jsx(_components.a, {\n        href: \"https://podatki.gov.si/content/festival-odprtih-podatkov-2017\",\n        children: \"Festival of Open Data\"\n      }), \", a mini-conference organized by the Slovenian government, dedicated to the promotion of transparent access to government data. 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+<a href="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/Screen-Shot-2017-10-10-at-13.04.47.png" data-gallery="true"><img alt="" loading="lazy" width="583" height="256" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-10-diving-into-car-registration-data/__optimized-images__/Screen-Shot-2017-10-10-at-13.04.47.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"ASTARIC","date":"2017-10-13 12:04:32+00:00","draft":false,"title":"Diving Into Car Registration Data","blog":["addons","analysis","interactive data visualization","orange3","visualization"],"oldUrl":"/blog/2017/10/13/diving-into-car-registration-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week, we presented Orange at the \", _jsx(_components.a, {\n        href: \"https://podatki.gov.si/content/festival-odprtih-podatkov-2017\",\n        children: \"Festival of Open Data\"\n      }), \", a mini-conference organized by the Slovenian government, dedicated to the promotion of transparent access to government data. In a 10 minute presentation, we showed how Orange can be used to visualize and explore what kinds of vehicles were registered for the first time in Slovenia in 2017.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-10-diving-into-car-registration-data/__optimized-images__/IMG_3530.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1200\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The original dataset is available at the\", _jsx(_components.a, {\n        href: \"https://podatki.gov.si/dataset/prvic-registrirana-vozila-po-mesecih\",\n        children: \" OPSI portal\"\n      }), \" and it consists of 73 files, one for each month since January 2012. For the presentation, we focused on the 2017 data. If you want to follow along, you can download the \", _jsx(_components.a, {\n        href: \"https://www.dropbox.com/s/rb4gx2856f34i47/OsebnaVozila.tab?dl=0\",\n        children: \"merged dataset (first 9 months of 2017 as a single file)\"\n      }), \". The \", _jsx(_components.a, {\n        href: \"https://www.dropbox.com/s/i0sckc7e8kll4g8/Priprava%20podatkov.ows?dl=0\",\n        children: \"workflow I used to prepare the data\"\n      }), \" is also available.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When exploring the data, the first thing we do is take a look at distributions. If we observe the distribution of new and used cars bought by the gender of the buyer, we can see that men prefer used cars while women more often opt for a new car. Or we can observe the distribution by age to see that older people tend to buy newer cars.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But the true power of Orange can be seen if we visualize the data on a map. In order to do this, we need to first use Geocoding to map municipality names to regions which can be shown on a map by choosing the column that contains municipality name (C1.3-Obcina uporabnika) and clicking apply. Since municipalities in Slovenia are created all the time, not all of them can be matched. The right part of the widget allows us to map these small municipalities to the nearest region. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>workshop, education, data science, summer school</strong></p><h1 class="sc-c67a4900-0 loxVcf">Doctoral Summer School</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 26, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>For the second year in a row, the Orange team was a part of the <a href="https://summerprogrammes.ef.uni-lj.si/programmes/doctoral_summer_school/">Ljubljana Doctoral Summer School</a>, which is organized by the <a href="http://www.ef.uni-lj.si/en">School of Economics and Business, University of Ljubljana</a>. Our course, called Pratical Introduction to Machine Learning and Data Analytics, was aimed at presenting the nuts and bolts of data science methods and concepts with the help of visual programming. In Orange, of course.</p>
 <a href="/blog/2019-07-doctoral_summer_school/__optimized-images__/2019-07-26_ekonomska.jpg" data-gallery="true"><img alt="" loading="lazy" width="1114" height="505" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-07-doctoral_summer_school/__optimized-images__/2019-07-26_ekonomska.jpg"/></a>
 <p><br/>
@@ -171,4 +171,4 @@
 <p><br/>
 <!-- -->\</p>
 <p>We won&#x27;t answer this directly, but this is one of the homeworks we give the students in order to introduce cross-validation. This is such an important concept that we make sure everyone in the class really understand why we need separate training and testing data.</p>
-<p>Feel free to use this exercise in any of your future classes!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-07-26","draft":false,"title":"Doctoral Summer School","thumbImage":"2019-07-26_ekonomska_small.jpg","frontPageImage":"2019-07-26_ekonomska_small.jpg","blog":["workshop","education","data science","summer school"],"shortExcerpt":"This year's Data Science course at the Doctoral Summer School.","longExcerpt":"For the second year in a row we took part in the Ljubljana Doctoral Summer School, organized by the School of Economics and Business.","oldUrl":"/blog/2019/2019-07-26-doctoral_summer_school/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    br: \"br\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"For the second year in a row, the Orange team was a part of the \", _jsx(_components.a, {\n        href: \"https://summerprogrammes.ef.uni-lj.si/programmes/doctoral_summer_school/\",\n        children: \"Ljubljana Doctoral Summer School\"\n      }), \", which is organized by the \", _jsx(_components.a, {\n        href: \"http://www.ef.uni-lj.si/en\",\n        children: \"School of Economics and Business, University of Ljubljana\"\n      }), \". 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+<p>Feel free to use this exercise in any of your future classes!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-07-26","draft":false,"title":"Doctoral Summer School","thumbImage":"2019-07-26_ekonomska_small.jpg","frontPageImage":"2019-07-26_ekonomska_small.jpg","blog":["workshop","education","data science","summer school"],"shortExcerpt":"This year's Data Science course at the Doctoral Summer School.","longExcerpt":"For the second year in a row we took part in the Ljubljana Doctoral Summer School, organized by the School of Economics and Business.","oldUrl":"/blog/2019/2019-07-26-doctoral_summer_school/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    br: \"br\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"For the second year in a row, the Orange team was a part of the \", _jsx(_components.a, {\n        href: \"https://summerprogrammes.ef.uni-lj.si/programmes/doctoral_summer_school/\",\n        children: \"Ljubljana Doctoral Summer School\"\n      }), \", which is organized by the \", _jsx(_components.a, {\n        href: \"http://www.ef.uni-lj.si/en\",\n        children: \"School of Economics and Business, University of Ljubljana\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>regression</strong></p><h1 class="sc-c67a4900-0 loxVcf">Earth - Multivariate adaptive regression splines</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 20, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>There have recently been some additions to the lineup of Orange learners. One of these is Orange.regression.earth.EarthLearner. It is an Orange interface to the <a href="http://www.milbo.users.sonic.net/earth/">Earth</a> library written by Stephen Milborrow implementing <a href="http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines">Multivariate adaptive regression splines</a>.</p>
 <p>So lets take it out for a spin on a simple toy dataset (<a href="http://blog.biolab.si/wp-content/uploads/2011/12/13/data.tab">data.tab</a> - created using the Paint Data widget in the Orange Canvas):</p>
 <pre><code>    import Orange
@@ -185,4 +185,4 @@
        +1.591 * max(0, X - 0.907)
 </code></pre>
 <p>See <a href="https://orange-multitarget.readthedocs.io/en/0.9.2/Orange.regression.earth.html">Orange.regression.earth reference</a> for full documentation.</p>
-<p>(Edit: Added link to the dataset file)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-12-20 12:22:00+00:00","draft":false,"title":"Earth - Multivariate adaptive regression splines","blog":["regression"],"oldUrl":"/blog/2011/12/20/earth-multivariate-adaptive-regression-splines/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"There have recently been some additions to the lineup of Orange learners. One of these is Orange.regression.earth.EarthLearner. It is an Orange interface to the \", _jsx(_components.a, {\n        href: \"http://www.milbo.users.sonic.net/earth/\",\n        children: \"Earth\"\n      }), \" library written by Stephen Milborrow implementing \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines\",\n        children: \"Multivariate adaptive regression splines\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"So lets take it out for a spin on a simple toy dataset (\", _jsx(_components.a, {\n        href: \"http://blog.biolab.si/wp-content/uploads/2011/12/13/data.tab\",\n        children: \"data.tab\"\n      }), \" - created using the Paint Data widget in the Orange Canvas):\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    from Orange.regression import earth\\n    import numpy\\n    from matplotlib import pylab as pl\\n\\n    data = Orange.data.Table(\\\"data.tab\\\")\\n    earth_predictor = earth.EarthLearner(data)\\n\\n    X, Y = data.to_numpy(\\\"A/C\\\")\\n\\n    pl.plot(X, Y, \\\".r\\\")\\n\\n    linspace = numpy.linspace(min(X), max(X), 20)\\n    predictions = [earth_predictor([s, \\\"?\\\"]) for s in linspace]\\n\\n    pl.plot(linspace, predictions, \\\"-b\\\")\\n    pl.show()\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"which produces the following plot:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-12-earth-multivariate-adaptive-regression-splines/__optimized-images__/earth_demo_2.png\",\n      alt: \"\",\n      width: \"600\",\n      height: \"470\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can also print the model representation using\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print earth_predictor\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"which outputs:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Y =\\n       1.013\\n       +1.198 * max(0, X - 0.485)\\n       -1.803 * max(0, 0.485 - X)\\n       -1.321 * max(0, X - 0.283)\\n       -1.609 * max(0, X - 0.640)\\n       +1.591 * max(0, X - 0.907)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"See \", _jsx(_components.a, {\n        href: \"https://orange-multitarget.readthedocs.io/en/0.9.2/Orange.regression.earth.html\",\n        children: \"Orange.regression.earth reference\"\n      }), \" for full documentation.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"(Edit: Added link to the dataset file)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"earth-multivariate-adaptive-regression-splines"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>(Edit: Added link to the dataset file)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-12-20 12:22:00+00:00","draft":false,"title":"Earth - Multivariate adaptive regression splines","blog":["regression"],"oldUrl":"/blog/2011/12/20/earth-multivariate-adaptive-regression-splines/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"There have recently been some additions to the lineup of Orange learners. One of these is Orange.regression.earth.EarthLearner. It is an Orange interface to the \", _jsx(_components.a, {\n        href: \"http://www.milbo.users.sonic.net/earth/\",\n        children: \"Earth\"\n      }), \" library written by Stephen Milborrow implementing \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines\",\n        children: \"Multivariate adaptive regression splines\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"So lets take it out for a spin on a simple toy dataset (\", _jsx(_components.a, {\n        href: \"http://blog.biolab.si/wp-content/uploads/2011/12/13/data.tab\",\n        children: \"data.tab\"\n      }), \" - created using the Paint Data widget in the Orange Canvas):\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    from Orange.regression import earth\\n    import numpy\\n    from matplotlib import pylab as pl\\n\\n    data = Orange.data.Table(\\\"data.tab\\\")\\n    earth_predictor = earth.EarthLearner(data)\\n\\n    X, Y = data.to_numpy(\\\"A/C\\\")\\n\\n    pl.plot(X, Y, \\\".r\\\")\\n\\n    linspace = numpy.linspace(min(X), max(X), 20)\\n    predictions = [earth_predictor([s, \\\"?\\\"]) for s in linspace]\\n\\n    pl.plot(linspace, predictions, \\\"-b\\\")\\n    pl.show()\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"which produces the following plot:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-12-earth-multivariate-adaptive-regression-splines/__optimized-images__/earth_demo_2.png\",\n      alt: \"\",\n      width: \"600\",\n      height: \"470\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can also print the model representation using\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print earth_predictor\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"which outputs:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Y =\\n       1.013\\n       +1.198 * max(0, X - 0.485)\\n       -1.803 * max(0, 0.485 - X)\\n       -1.321 * max(0, X - 0.283)\\n       -1.609 * max(0, X - 0.640)\\n       +1.591 * max(0, X - 0.907)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"See \", _jsx(_components.a, {\n        href: \"https://orange-multitarget.readthedocs.io/en/0.9.2/Orange.regression.earth.html\",\n        children: \"Orange.regression.earth reference\"\n      }), \" for full documentation.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"(Edit: Added link to the dataset file)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"earth-multivariate-adaptive-regression-splines"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
diff --git a/blog/editing-the-photographs-collection-with-the-help-of-machine-learning/index.html b/blog/editing-the-photographs-collection-with-the-help-of-machine-learning/index.html
index c87b69291..65c2062b6 100644
--- a/blog/editing-the-photographs-collection-with-the-help-of-machine-learning/index.html
+++ b/blog/editing-the-photographs-collection-with-the-help-of-machine-learning/index.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Editing the photographs collection with the help of machine learning</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Editing the photographs collection with the help of machine learning"/><meta property="og:description" content="How to use Orange&#x27;s image embedding to identify similar images, classify them in categories and make an order in your photographs collection?"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2022-02-11"/><meta property="article:author" content="Primož Godec"/><meta property="article:tag" content="orange"/><meta property="article:tag" content="image analytics"/><meta property="article:tag" content="images"/><meta property="article:tag" content="machine learning"/><meta property="og:image" content="/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-grid-title.jpg"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange, image analytics, images, machine learning</strong></p><h1 class="sc-c67a4900-0 loxVcf">Editing the photographs collection with the help of machine learning</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Primož Godec</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 11, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The core element of Orange&#x27;s image analysis is embedding images in the vector space. Last year, we upgraded the embedding server infrastructure, enabling around ten times faster image embedding.</p>
 <p>We switched from an old cluster with a fixed number of workers for each embedding to a new infrastructure where workers turn on when needed. It means that when a user sends images to the embedder, it will turn on as many instances as required until processors&#x27; cores are available. Inception-v3, VGG16, and VGG19 embedders also perform computation on GPU, making them even faster.</p>
 <p>We also made some modifications in the implementation on Orange&#x27;s part, making embedding more reliable.</p>
@@ -190,4 +190,4 @@ <h3>Training the model to sort images</h3>
 <p>We also connected <a href="/widget-catalog/model/savemodel">the Save Model widget</a> to the Logistic Regression. This way, the model is saved for later and can be reused.</p>
 <h3>Want to learn more?</h3>
 <p>We showed two ways of analysing images with Orange&#x27;s Image Analytics module. The Image Grid is a great tool to identify similar images and see them in the grid. The second one needs an extra effort to prepare a training dataset, but this leads to an automatic pipeline to identify categories.</p>
-<p>If you want to learn more about Image Analysis in Orange <a href="https://youtube.com/playlist?list=PLmNPvQr9Tf-aRstY69vGAPO_c5RaceBhN"> watch the tutorial wideos</a> or read related blogs: <a href="/blog/2017/04/03/image-analytics-clustering/%5D">Image Analytics: Clustering</a>, <a href="/blog/2018/05/09/clustering-of-monet-and-manet/">Clustering of Monet and Manet</a>, and <a href="/blog/2017/04/25/outliers-in-traffic-signs/">Outliers in Traffic Signs</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Primož Godec","date":"2022-02-11","draft":false,"title":"Editing the photographs collection with the help of machine learning","thumbImage":"2022-02-11-grid-title.jpg","frontPageImage":"2022-02-11-grid-title.jpg","blog":["orange","image analytics","images","machine learning"],"shortExcerpt":"How to use Orange's image embedding to identify similar images, classify them in categories and make an order in your photographs collection?","longExcerpt":"The core element of Orange's image analysis is embedding images in the vector space, which just became a feaster with our infrastructure upgrades. We use this opportunity to show possible ways of analyzing images through observing similar images and classifying them.","x2images":false,"oldUrl":"/blog/2022/2022-02-11-edit-image-collection/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot, LinkNew} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The core element of Orange's image analysis is embedding images in the vector space. Last year, we upgraded the embedding server infrastructure, enabling around ten times faster image embedding.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We switched from an old cluster with a fixed number of workers for each embedding to a new infrastructure where workers turn on when needed. It means that when a user sends images to the embedder, it will turn on as many instances as required until processors' cores are available. Inception-v3, VGG16, and VGG19 embedders also perform computation on GPU, making them even faster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We also made some modifications in the implementation on Orange's part, making embedding more reliable.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"How does embedding work?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imageembedding/\",\n        children: \"The Image Embedding widget\"\n      }), \" gets the table with images' metadata such as an image name, location and size, but they are not ready for analysis. Images are sent to the server to be embedded in a format understandable to machine learning algorithms. The server pushes images through a pre-trained deep neural network and returns the number vectors to the widget.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-embedding-widget.jpg\",\n      width: \"674\",\n      height: \"552\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-embedding-widget.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Deep neural networks are trained on various specific tasks. \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1512.00567\",\n        children: \"Inception v3\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1409.1556\",\n        children: \"VGG16, VGG19\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1602.07360\",\n        children: \"SqueezeNet\"\n      }), \" are trained to classify images into 1000 classes according to the object at the image. \", _jsx(_components.a, {\n        href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n        children: \"Painters\"\n      }), \" are trained to predict paintings' authors, \", _jsx(_components.a, {\n        href: \"https://pubmed.ncbi.nlm.nih.gov/29036616/\",\n        children: \"DeepLoc\"\n      }), \" to predict yeast's cells structures and \", _jsx(_components.a, {\n        href: \"https://ieeexplore.ieee.org/document/8373812\",\n        children: \"OpenFace\"\n      }), \" for face recognition. We disregard the classification layer of the network and consider the penultimate layer instead and use that for the image's vector-based representation.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Editing photo collection\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I have taken many photographs in the last few years, but I have not had the time to edit the collection. I want to sort them into subfolders based on the categories. Can machine learning algorithms and image embeddings help me with that?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This blog post will show two approaches for identifying categories and sorting images. The first one is unsupervised, and the second one is supervised.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, we load photos. We load a folder of images with a collection that we would like to sort in the subfolders (\", _jsx(_components.a, {\n        href: \"https://github.com/PrimozGodec/datasets/blob/master/image-analytics-collection/test-photos.zip\",\n        children: \"download dataset\"\n      }), \").\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-inspect-images.jpg\",\n      width: \"700\",\n      height: \"646\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-inspect-images.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Both approaches will include embedding images. We connect the \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imageembedding/\",\n        children: \"Image Embedding\"\n      }), \" widget to the Import Images widget to embed pictures and select \", _jsx(_components.strong, {\n        children: \"Inception v3 embedder\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"Our server deletes all images immediately after embedding. If you would not like to share your photos with our server anyway, you can select the SqueezeNet embedder that embeds images at your computer.\"\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-embedding.jpg\",\n      width: \"700\",\n      height: \"537\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-embedding.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Image Embedding widget added 2048 columns with numbers to each image in the data table. Those numbers describe properties that neural networks inferred from pictures. 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More similar images will be closer in the plot.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We passed embedded images from the Image Embedding to the Image Grid widget.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-grid.jpg\",\n      width: \"700\",\n      height: \"882\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-grid.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image Grid has placed similar images closer in the grid. For example, in the top right corner are images of food, in the top left corner are photos of cities, on the right side are pictures of mountains, and at the bottom left are images of sunsets. The Image Grid had more difficulties with snowy photos. Some are placed at the bottom (they seem to include some mountain image elements), and three central ones seem to have elements of cities, mountains and snow.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Since images that belong to the same group are close together, it is easy to mark groups of images manually (colours around images). Marked groups are in the output table as class attributes. We can save them on the computer such that each group is a separate folder with \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/saveimages\",\n        children: \"the Save Images widget\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(LinkNew, {\n        url: \"/blog/2017/04/03/image-analytics-clustering/\",\n        name: \"Image Analytics: Clustering\"\n      })]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Training the model to sort images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Image Grid is excellent to discover similar images. Still, it is not practical to sort photos into category groups. If we would like to do that more often, we would always need to assign images to groups manually. Can we have a pipeline that would allow us to make a model that we can reuse later when we take new photos s?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related\"\n      }), \": \", _jsx(LinkNew, {\n        url: \"https://www.youtube.com/watch?v=lvgx62a8XQk\u0026list=PLmNPvQr9Tf-aRstY69vGAPO_c5RaceBhN\u0026index=1\u0026ab_channel=OrangeDataMining\",\n        name: \"Video on image classification\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can train a classification model that will mark images with categories.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We prepared a separate training dataset with pictures in 5 subfolders. Folders are named: city, food, mountains, sunsets and winter. Names of the folders define categories; once the model is trained, it will always classify images in those categories.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will first test how accurate the model is.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-test-and-score.jpg\",\n      width: \"700\",\n      height: \"342\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-test-and-score.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/importimages/\",\n        children: \"The Import Images\"\n      }), \" widget loads images from the training dataset (\", _jsx(_components.a, {\n        href: \"https://github.com/PrimozGodec/datasets/raw/master/image-analytics-collection/train-photos.zip\",\n        children: \"download the training data\"\n      }), \"). Since images are sorted in multiple folders, the widget assigns the folder name to images as a category, which we can see in the Data Table. We embed images with Inception v3 embedder in \", _jsx(_components.a, {\n        href: \"/blog/2022-02-edit-image-collection/(/widget-catalog/image-analytics/imageembedding/)\",\n        children: \"the Image Embedding widget\"\n      }), \" and performs cross-validation in \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/testandscore\",\n        children: \"the Test and Score widget\"\n      }), \". The model we use is logistic regression. We are happy with the model with AUC 0.992 and classification accuracy of 0.943.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we can train the model that will be used to label an unlabeled set of photos.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-classification.jpg\",\n      width: \"700\",\n      height: \"492\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-classification.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We train \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/logisticregression\",\n        children: \"the logistic regression model\"\n      }), \" on the training images in the top branch on the labelled training dataset. Data that we want to label (sort in subfolders) are loaded and embedded in the bottom branch. We use \", _jsx(_components.a, {\n        href: \"https://github.com/PrimozGodec/datasets/blob/master/image-analytics-collection/test-photos.zip\",\n        children: \"the same images\"\n      }), \" as in the first part of the blog. \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/predictions\",\n        children: \"The Predictions widget\"\n      }), \" assigns labels to the images based on the logistic regression model.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We use \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imageviewer\",\n        children: \"the Image Viewer\"\n      }), \" to inspect assigned labels to images. Labels seem to be meaningful and correct. Now that we have photos labelled, we save them to the folder structure with \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/saveimages\",\n        children: \"the Save Images widget\"\n      }), \" such that each folder is one category. Since the Prediction widget places predictions (column named Logistic Regression) in the meta part of the table, we need to first set the attribute as a target with the Select Columns widget --- the Save Images widget uses the target column as a subfolder name.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We also connected \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/savemodel\",\n        children: \"the Save Model widget\"\n      }), \" to the Logistic Regression. This way, the model is saved for later and can be reused.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Want to learn more?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We showed two ways of analysing images with Orange's Image Analytics module. The Image Grid is a great tool to identify similar images and see them in the grid. The second one needs an extra effort to prepare a training dataset, but this leads to an automatic pipeline to identify categories.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If you want to learn more about Image Analysis in Orange \", _jsx(_components.a, {\n        href: \"https://youtube.com/playlist?list=PLmNPvQr9Tf-aRstY69vGAPO_c5RaceBhN\",\n        children: \" watch the tutorial wideos\"\n      }), \" or read related blogs: \", _jsx(_components.a, {\n        href: \"/blog/2017/04/03/image-analytics-clustering/%5D\",\n        children: \"Image Analytics: Clustering\"\n      }), \", \", _jsx(_components.a, {\n        href: \"/blog/2018/05/09/clustering-of-monet-and-manet/\",\n        children: \"Clustering of Monet and Manet\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"/blog/2017/04/25/outliers-in-traffic-signs/\",\n        children: \"Outliers in Traffic Signs\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-grid-title.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"editing-the-photographs-collection-with-the-help-of-machine-learning"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>If you want to learn more about Image Analysis in Orange <a href="https://youtube.com/playlist?list=PLmNPvQr9Tf-aRstY69vGAPO_c5RaceBhN"> watch the tutorial wideos</a> or read related blogs: <a href="/blog/2017/04/03/image-analytics-clustering/%5D">Image Analytics: Clustering</a>, <a href="/blog/2018/05/09/clustering-of-monet-and-manet/">Clustering of Monet and Manet</a>, and <a href="/blog/2017/04/25/outliers-in-traffic-signs/">Outliers in Traffic Signs</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Primož Godec","date":"2022-02-11","draft":false,"title":"Editing the photographs collection with the help of machine learning","thumbImage":"2022-02-11-grid-title.jpg","frontPageImage":"2022-02-11-grid-title.jpg","blog":["orange","image analytics","images","machine learning"],"shortExcerpt":"How to use Orange's image embedding to identify similar images, classify them in categories and make an order in your photographs collection?","longExcerpt":"The core element of Orange's image analysis is embedding images in the vector space, which just became a feaster with our infrastructure upgrades. We use this opportunity to show possible ways of analyzing images through observing similar images and classifying them.","x2images":false,"oldUrl":"/blog/2022/2022-02-11-edit-image-collection/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot, LinkNew} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The core element of Orange's image analysis is embedding images in the vector space. Last year, we upgraded the embedding server infrastructure, enabling around ten times faster image embedding.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We switched from an old cluster with a fixed number of workers for each embedding to a new infrastructure where workers turn on when needed. It means that when a user sends images to the embedder, it will turn on as many instances as required until processors' cores are available. Inception-v3, VGG16, and VGG19 embedders also perform computation on GPU, making them even faster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We also made some modifications in the implementation on Orange's part, making embedding more reliable.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"How does embedding work?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imageembedding/\",\n        children: \"The Image Embedding widget\"\n      }), \" gets the table with images' metadata such as an image name, location and size, but they are not ready for analysis. Images are sent to the server to be embedded in a format understandable to machine learning algorithms. The server pushes images through a pre-trained deep neural network and returns the number vectors to the widget.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-embedding-widget.jpg\",\n      width: \"674\",\n      height: \"552\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-embedding-widget.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Deep neural networks are trained on various specific tasks. \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1512.00567\",\n        children: \"Inception v3\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1409.1556\",\n        children: \"VGG16, VGG19\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1602.07360\",\n        children: \"SqueezeNet\"\n      }), \" are trained to classify images into 1000 classes according to the object at the image. \", _jsx(_components.a, {\n        href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n        children: \"Painters\"\n      }), \" are trained to predict paintings' authors, \", _jsx(_components.a, {\n        href: \"https://pubmed.ncbi.nlm.nih.gov/29036616/\",\n        children: \"DeepLoc\"\n      }), \" to predict yeast's cells structures and \", _jsx(_components.a, {\n        href: \"https://ieeexplore.ieee.org/document/8373812\",\n        children: \"OpenFace\"\n      }), \" for face recognition. We disregard the classification layer of the network and consider the penultimate layer instead and use that for the image's vector-based representation.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Editing photo collection\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I have taken many photographs in the last few years, but I have not had the time to edit the collection. I want to sort them into subfolders based on the categories. Can machine learning algorithms and image embeddings help me with that?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This blog post will show two approaches for identifying categories and sorting images. The first one is unsupervised, and the second one is supervised.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, we load photos. We load a folder of images with a collection that we would like to sort in the subfolders (\", _jsx(_components.a, {\n        href: \"https://github.com/PrimozGodec/datasets/blob/master/image-analytics-collection/test-photos.zip\",\n        children: \"download dataset\"\n      }), \").\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-inspect-images.jpg\",\n      width: \"700\",\n      height: \"646\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-inspect-images.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Both approaches will include embedding images. We connect the \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imageembedding/\",\n        children: \"Image Embedding\"\n      }), \" widget to the Import Images widget to embed pictures and select \", _jsx(_components.strong, {\n        children: \"Inception v3 embedder\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"Our server deletes all images immediately after embedding. If you would not like to share your photos with our server anyway, you can select the SqueezeNet embedder that embeds images at your computer.\"\n      })\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-embedding.jpg\",\n      width: \"700\",\n      height: \"537\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-embedding.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Image Embedding widget added 2048 columns with numbers to each image in the data table. Those numbers describe properties that neural networks inferred from pictures. Numbers are not understandable to humans but make sense to machine learning algorithms.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Image grid will place similar photographs closer on the grid\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imagegrid/\",\n        children: \"The Image Grid\"\n      }), \" uses \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/tsne/\",\n        children: \"t-sne embedding\"\n      }), \" to project images on 2-dimensional plane based on their image embeddings. More similar images will be closer in the plot.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We passed embedded images from the Image Embedding to the Image Grid widget.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-grid.jpg\",\n      width: \"700\",\n      height: \"882\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-grid.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image Grid has placed similar images closer in the grid. For example, in the top right corner are images of food, in the top left corner are photos of cities, on the right side are pictures of mountains, and at the bottom left are images of sunsets. The Image Grid had more difficulties with snowy photos. Some are placed at the bottom (they seem to include some mountain image elements), and three central ones seem to have elements of cities, mountains and snow.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Since images that belong to the same group are close together, it is easy to mark groups of images manually (colours around images). Marked groups are in the output table as class attributes. We can save them on the computer such that each group is a separate folder with \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/saveimages\",\n        children: \"the Save Images widget\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(LinkNew, {\n        url: \"/blog/2017/04/03/image-analytics-clustering/\",\n        name: \"Image Analytics: Clustering\"\n      })]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Training the model to sort images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Image Grid is excellent to discover similar images. Still, it is not practical to sort photos into category groups. If we would like to do that more often, we would always need to assign images to groups manually. Can we have a pipeline that would allow us to make a model that we can reuse later when we take new photos s?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related\"\n      }), \": \", _jsx(LinkNew, {\n        url: \"https://www.youtube.com/watch?v=lvgx62a8XQk\u0026list=PLmNPvQr9Tf-aRstY69vGAPO_c5RaceBhN\u0026index=1\u0026ab_channel=OrangeDataMining\",\n        name: \"Video on image classification\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can train a classification model that will mark images with categories.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We prepared a separate training dataset with pictures in 5 subfolders. Folders are named: city, food, mountains, sunsets and winter. Names of the folders define categories; once the model is trained, it will always classify images in those categories.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will first test how accurate the model is.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-test-and-score.jpg\",\n      width: \"700\",\n      height: \"342\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-test-and-score.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/importimages/\",\n        children: \"The Import Images\"\n      }), \" widget loads images from the training dataset (\", _jsx(_components.a, {\n        href: \"https://github.com/PrimozGodec/datasets/raw/master/image-analytics-collection/train-photos.zip\",\n        children: \"download the training data\"\n      }), \"). Since images are sorted in multiple folders, the widget assigns the folder name to images as a category, which we can see in the Data Table. We embed images with Inception v3 embedder in \", _jsx(_components.a, {\n        href: \"/blog/2022-02-edit-image-collection/(/widget-catalog/image-analytics/imageembedding/)\",\n        children: \"the Image Embedding widget\"\n      }), \" and performs cross-validation in \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/testandscore\",\n        children: \"the Test and Score widget\"\n      }), \". The model we use is logistic regression. We are happy with the model with AUC 0.992 and classification accuracy of 0.943.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we can train the model that will be used to label an unlabeled set of photos.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-edit-image-collection/2022-02-11-image-classification.jpg\",\n      width: \"700\",\n      height: \"492\",\n      src: \"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-image-classification.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We train \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/logisticregression\",\n        children: \"the logistic regression model\"\n      }), \" on the training images in the top branch on the labelled training dataset. Data that we want to label (sort in subfolders) are loaded and embedded in the bottom branch. We use \", _jsx(_components.a, {\n        href: \"https://github.com/PrimozGodec/datasets/blob/master/image-analytics-collection/test-photos.zip\",\n        children: \"the same images\"\n      }), \" as in the first part of the blog. \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/predictions\",\n        children: \"The Predictions widget\"\n      }), \" assigns labels to the images based on the logistic regression model.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We use \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/imageviewer\",\n        children: \"the Image Viewer\"\n      }), \" to inspect assigned labels to images. Labels seem to be meaningful and correct. Now that we have photos labelled, we save them to the folder structure with \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/saveimages\",\n        children: \"the Save Images widget\"\n      }), \" such that each folder is one category. Since the Prediction widget places predictions (column named Logistic Regression) in the meta part of the table, we need to first set the attribute as a target with the Select Columns widget --- the Save Images widget uses the target column as a subfolder name.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We also connected \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/savemodel\",\n        children: \"the Save Model widget\"\n      }), \" to the Logistic Regression. This way, the model is saved for later and can be reused.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Want to learn more?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We showed two ways of analysing images with Orange's Image Analytics module. The Image Grid is a great tool to identify similar images and see them in the grid. The second one needs an extra effort to prepare a training dataset, but this leads to an automatic pipeline to identify categories.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If you want to learn more about Image Analysis in Orange \", _jsx(_components.a, {\n        href: \"https://youtube.com/playlist?list=PLmNPvQr9Tf-aRstY69vGAPO_c5RaceBhN\",\n        children: \" watch the tutorial wideos\"\n      }), \" or read related blogs: \", _jsx(_components.a, {\n        href: \"/blog/2017/04/03/image-analytics-clustering/%5D\",\n        children: \"Image Analytics: Clustering\"\n      }), \", \", _jsx(_components.a, {\n        href: \"/blog/2018/05/09/clustering-of-monet-and-manet/\",\n        children: \"Clustering of Monet and Manet\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"/blog/2017/04/25/outliers-in-traffic-signs/\",\n        children: \"Outliers in Traffic Signs\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-02-edit-image-collection/__optimized-images__/2022-02-11-grid-title.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"editing-the-photographs-collection-with-the-help-of-machine-learning"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Excel files in Orange 3.0</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Excel files in Orange 3.0"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-05-29 11:58:39+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="data"/><meta property="article:tag" content="dataloading"/><meta property="article:tag" content="orange3"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, dataloading, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Excel files in Orange 3.0</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">May 29, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange 3.0 version comes with an exciting feature that will simplify reading your data. If the old Orange required conversion from Excel into either tab-delimited or comma-separated files, the new version allows you to open plain .xlsx format data sets in the program. Naturally, the .txt and .csv files are still readable in Orange, so feel free to use data sets in any of the above-mentioned formats.</p>
 <p>Since Orange 3.0 is still in the development mode, you will find a smaller selection of widgets available at the moment, but give it a go and see how it works for Excel type data and whether the existing widgets are sufficient for your data analysis. Please find the daily build for OSX <a href="http://orange.biolab.si/orange3/">here</a>.</p>
 <a href="/blog/2015-05-excel-files-in-orange-3-0/__optimized-images__/blog2-compile.png" data-gallery="true"><img alt="" loading="lazy" width="800" height="640" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-05-excel-files-in-orange-3-0/__optimized-images__/blog2-compile.png"/></a>
-<p>Orange 3.0 can read Excel files.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-05-29 11:58:39+00:00","draft":false,"title":"Excel files in Orange 3.0","blog":["data","dataloading","orange3"],"oldUrl":"/blog/2015/05/29/excel-files-in-orange-3-0/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange 3.0 version comes with an exciting feature that will simplify reading your data. If the old Orange required conversion from Excel into either tab-delimited or comma-separated files, the new version allows you to open plain .xlsx format data sets in the program. Naturally, the .txt and .csv files are still readable in Orange, so feel free to use data sets in any of the above-mentioned formats.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Since Orange 3.0 is still in the development mode, you will find a smaller selection of widgets available at the moment, but give it a go and see how it works for Excel type data and whether the existing widgets are sufficient for your data analysis. Please find the daily build for OSX \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/orange3/\",\n        children: \"here\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-05-excel-files-in-orange-3-0/__optimized-images__/blog2-compile.png\",\n      alt: \"\",\n      width: \"800\",\n      height: \"640\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange 3.0 can read Excel files.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"excel-files-in-orange-30"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Orange 3.0 can read Excel files.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-05-29 11:58:39+00:00","draft":false,"title":"Excel files in Orange 3.0","blog":["data","dataloading","orange3"],"oldUrl":"/blog/2015/05/29/excel-files-in-orange-3-0/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange 3.0 version comes with an exciting feature that will simplify reading your data. If the old Orange required conversion from Excel into either tab-delimited or comma-separated files, the new version allows you to open plain .xlsx format data sets in the program. Naturally, the .txt and .csv files are still readable in Orange, so feel free to use data sets in any of the above-mentioned formats.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Since Orange 3.0 is still in the development mode, you will find a smaller selection of widgets available at the moment, but give it a go and see how it works for Excel type data and whether the existing widgets are sufficient for your data analysis. Please find the daily build for OSX \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/orange3/\",\n        children: \"here\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-05-excel-files-in-orange-3-0/__optimized-images__/blog2-compile.png\",\n      alt: \"\",\n      width: \"800\",\n      height: \"640\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange 3.0 can read Excel files.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"excel-files-in-orange-30"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>project, explainable ai, teaching</strong></p><h1 class="sc-c67a4900-0 loxVcf">Explainable AI Project Meeting</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 26, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Recently, we have attended an xAIM project meeting in Hannover, Germany. xAIM is an EU project whose aim is to develop a Master&#x27;s degree tailored to the medical professionals or those wanting to work in the medical field. The focus of the MA is on explainable artificial intelligence, that is on the explanation of machine learning models, ethical aspects of AI, and the translation of models into the medical setting.</p>
 <p>In two days we have finalized the syllabus of the MA, which will consist of three modules - artificial intelligence, healthcare management, and ethics. Orange will be heavily used in (at least) two courses, namely the Introduction to Data Science and Text Mining. The first course is the core subject of the MA, while the second course is an elective.</p>
 <p>After two days of hard work we had to relax a bit, so we visited the Christmas market in the center of Hannover. It smelled of cinnamon, Glühwein, and, of course, of oranges.</p>
-<a href="/blog/2021-10-explainable-ai/2021-11-26-market.jpeg" data-gallery="true"><img alt="" loading="lazy" width="1280" height="960" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-10-explainable-ai/2021-11-26-market.jpeg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-11-26","draft":false,"title":"Explainable AI Project Meeting","thumbImage":"2021-11-26-xaim.jpeg","frontPageImage":"2021-11-26-xaim.jpeg","blog":["project","explainable ai","teaching"],"shortExcerpt":"Orange will be used in two Explainable AI courses.","longExcerpt":"Orange will be used in two courses of the new Explainable AI Master's degree.","x2images":false,"oldUrl":"/blog/2021/2021-10-26-explainable-ai/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently, we have attended an xAIM project meeting in Hannover, Germany. xAIM is an EU project whose aim is to develop a Master's degree tailored to the medical professionals or those wanting to work in the medical field. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>workshop, telco, clustering, nomogram</strong></p><h1 class="sc-c67a4900-0 loxVcf">Explaining Customer Segments for Business</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 15, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Last month we held a workshop for a large Slovenian Telco company. Their two key questions were - what machine learning techniques can we use on our data and how do we explain the models afterwards. So the workshop focused on three use cases - product segmentation, modeling churn, and clustering customers. With any kind of models, especially unsupervised ones, we often get the question - but how can we explain the clusters? What do these clusters tell us about the customers?</p>
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 <p><br/>
@@ -172,4 +172,4 @@
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 <p><br/>
 <!-- -->\</p>
-<p>What does this mean from a business perspective? Well, that you should focus your marketing on cluster C1 and offer discounted internet packages. Marketing to C3 would be essentially useless. This is how Orange can help you identify business opportunities and understand you customer base better.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-11-15","draft":false,"title":"Explaining Customer Segments for Business","thumbImage":"2019-11-15_ws-title.JPG","frontPageImage":"2019-11-15_ws-title.JPG","blog":["workshop","telco","clustering","nomogram"],"shortExcerpt":"Explaining customer base enables businesses to make informed decisions. 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+<p>What does this mean from a business perspective? Well, that you should focus your marketing on cluster C1 and offer discounted internet packages. Marketing to C3 would be essentially useless. This is how Orange can help you identify business opportunities and understand you customer base better.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-11-15","draft":false,"title":"Explaining Customer Segments for Business","thumbImage":"2019-11-15_ws-title.JPG","frontPageImage":"2019-11-15_ws-title.JPG","blog":["workshop","telco","clustering","nomogram"],"shortExcerpt":"Explaining customer base enables businesses to make informed decisions. 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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Explaining Kickstarter Success</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Explaining Kickstarter Success"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-08-27 16:02:36+00:00"/><meta property="article:author" content="ANDREJA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="prediction"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, orange3, prediction, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Explaining Kickstarter Success</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>ANDREJA</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 27, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>On Kickstarter most app ideas don&#x27;t get funded. But why is that? When we are looking for possible explanations, it is easy to ascribe the failure to the type of the idea.</p>
 <p>But what about those rare cases, where an app idea gets funded? Can we figure out why a particular idea succeeded? Our new widget Explain Predictions can do just that - explain why they will succeed. Or at least, explain why the classifier thinks they will.</p>
 <p>First, let us load the Kickstarter data from the Datasets widget and inspect it in a Data Table.</p>
@@ -163,4 +163,4 @@
 <p><a href="/blog/2018-08-explaining-kickstarter-success/__optimized-images__/expl_res.png" data-gallery="true"><img alt="" loading="lazy" width="1392" height="784" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-08-explaining-kickstarter-success/__optimized-images__/expl_res.png"/></a>
 High score means the attribute contributed positively to the the final decision (Funded: yes), while low scores contributed negatively.</p>
 <p>When explaining the decision of the classifier, we look at the values of the attributes for our sample and how they interact. We do that by approximating <a href="https://en.wikipedia.org/wiki/Shapley_value">Shapely value</a>, since calculating it exactly would sometimes take more then a lifetime. That means customized explanations for every individual case, while treating classifier like a black box. You could do the same for any model the Orange offers, including Neural Networks!</p>
-<p>And there you have it, an easy way to know what makes your Kickstarter campaign succeed, cell be classified as healthy, or a bank loan approved.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"ANDREJA","date":"2018-08-27 16:02:36+00:00","draft":false,"title":"Explaining Kickstarter Success","blog":["addons","orange3","prediction","widget"],"oldUrl":"/blog/2018/08/27/explaining-kickstarter-success/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"On Kickstarter most app ideas don't get funded. 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+<p>And there you have it, an easy way to know what makes your Kickstarter campaign succeed, cell be classified as healthy, or a bank loan approved.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"ANDREJA","date":"2018-08-27 16:02:36+00:00","draft":false,"title":"Explaining Kickstarter Success","blog":["addons","orange3","prediction","widget"],"oldUrl":"/blog/2018/08/27/explaining-kickstarter-success/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"On Kickstarter most app ideas don't get funded. 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@@ -172,4 +172,4 @@
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 <p>The key message of this part of the tutorial was that different models work differently and we have to understand what they are telling us and how they were constructed. Luckily, Orange enables us to explore certain models, so we can inspect them and draw conclusions from the best ranked attributes.</p>
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Instead of 10 variables, we kept just 4 - gender (male or female), age (adult or child), status (first, second, third or crew) and the survival class label (yes or no).\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_datasets.png\",\n      width: \"624\",\n      height: \"369\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_datasets.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us build a simple decision tree model with a Tree widget and observe it in a Tree Viewer. I have hidden the bottom branches for simplicity. Let us observe, what gives me the highest probability of survival. Seems like it is best if I travel as female in the first or second class or as a member of the crew. 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But why does Naive Bayes say it is bad to travel as a female crew member, while Tree says it is ok?\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_bayes.png\",\n      width: \"675\",\n      height: \"498\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_bayes.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, Naive Bayes does not say it is bad to travel as a female crew member. Naive Bayes assumes attribute independence, so it does not claim anything about being a female AND a member of the crew. It just says it is better to travel as a female and not so good if you are a member of the crew.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Second, if we observe the parameters of the Tree classifier, we see that we asked for a binary tree. 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+<a href="/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="366" height="208" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-11-20","draft":false,"title":"Explaining Models: Workshop in Belgrade","thumbImage":"2019-11-20_workshop_small.jpg","frontPageImage":"2019-11-20_workshop_small.jpg","blog":["workshop","belgrade","classification","nomogram","naive bayes","decision tree"],"shortExcerpt":"We explained how different models mean different things and how to interpret them at a recent tutorial in Belgrade.","longExcerpt":"We explained how different models mean different things and how to interpret them at a recent tutorial in Belgrade.","oldUrl":"/blog/2019/2019-11-20-belgrade-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    br: \"br\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"On Monday, Blaž and I held a technical tutorial \", _jsx(_components.em, {\n        children: \"Data Mining through Visual Programming and Interactive Analytics in Orange\"\n      }), \" at this year's edition of \", _jsx(_components.a, {\n        href: \"https://datasciconference.com/technical-tutorials/\",\n        children: \"Data Science Conference\"\n      }), \" in Belgrade, Serbia. The tutorial explained how to quickly prototype standard data mining and machine learning workflows in Orange and how to interactively explore clustering and classification models. The final part raised an interesting question that we're going to explore in continuation.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_workshop.jpg\",\n      width: \"1500\",\n      height: \"722\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_workshop.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, let us load the titanic data with the Datasets widget. The data is a simplified version of Kaggle's \", _jsx(_components.a, {\n        href: \"https://www.kaggle.com/c/titanic/data\",\n        children: \"Titanic data set\"\n      }), \". Instead of 10 variables, we kept just 4 - gender (male or female), age (adult or child), status (first, second, third or crew) and the survival class label (yes or no).\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_datasets.png\",\n      width: \"624\",\n      height: \"369\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_datasets.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us build a simple decision tree model with a Tree widget and observe it in a Tree Viewer. I have hidden the bottom branches for simplicity. Let us observe, what gives me the highest probability of survival. Seems like it is best if I travel as female in the first or second class or as a member of the crew. This would give me 92% chance of surviving the trip.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_tree.png\",\n      width: \"859\",\n      height: \"576\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_tree.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, what about another classifier. Let's say Naive Bayes. Connect Naive Bayes to Datasets and then Nomogram to Naive Bayes. It seems like I have the best change of surviving the trip if I travel as female in the first class - the chance of surviving is 90%. But why does Naive Bayes say it is bad to travel as a female crew member, while Tree says it is ok?\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_bayes.png\",\n      width: \"675\",\n      height: \"498\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_bayes.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, Naive Bayes does not say it is bad to travel as a female crew member. Naive Bayes assumes attribute independence, so it does not claim anything about being a female AND a member of the crew. It just says it is better to travel as a female and not so good if you are a member of the crew.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Second, if we observe the parameters of the Tree classifier, we see that we asked for a binary tree. So inevitably the variables will fall into two categories - third class (which has a low chance of survival) and everything else. If we uncheck this box, we see that the probability of surviving for female crew is slightly lower - 87%. That is still not too bad, but note that we only have 23 instances for this branch! There were not many female crew members on Titanic, so a sample is fairly small compared to other status values.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-11-belgrade-workshop/2019-11-20_non-binary-tree.png\",\n      width: \"1388\",\n      height: \"676\",\n      src: \"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_non-binary-tree.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The key message of this part of the tutorial was that different models work differently and we have to understand what they are telling us and how they were constructed. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>model explanation, explainable AI, explain, predictive modelling</strong></p><h1 class="sc-c67a4900-0 loxVcf">Explaining Predictive Models</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 10, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It is easy to build powerful predictive models in Orange. But how does the model &quot;look like&quot;? Which attributes and which values of those attributes are important? And when making predictions, which attributes contributed to the decision? <strong>Orange&#x27;s new Explain add-on</strong> helps you answer all those questions.</p>
 <p>Related: <a href="/blog/2019/2019-11-20-belgrade-workshop/" target="_blank" rel="noreferrer">Explaining Models</a></p>
 <p>Go to <strong>Options --&gt; Add-ons</strong> and install Explain add-on. Restart Orange for the add-on to appear. It only contains two widgets, but boy are they great!</p>
@@ -171,4 +171,4 @@
 <p>Once again, we are interested in target value Yes. Variables in red increase the probability of the target value (conversely, blue decrease it). The size of the arrow corresponds to the <a href="https://shap.readthedocs.io/en/latest/index.html">SHAP value</a> - in other words, the larger the arrow the larger the variable&#x27;s contribution to the target value. The model also predicted that John will leave the job with 77 % probability.</p>
 <a href="/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-prediction.png" data-gallery="true"><img alt="" loading="lazy" width="751" height="770" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-prediction.png"/></a>
 <p>As before, the most important variable for John is overtime. Him working overtime contributes a lot to the final prediction. Also, his job satisfaction is low (1 out of 5), making him likely to quit.</p>
-<p>The results correspond very much to those of the model, but it might not always be the case. Some people might leave because they are very dissatisfied without working overtime. This would show in Explain Predictions. See how the results change for the other two employees, Rachel and Veronica. Or make up your own employee with Excel and see what would the prediction be.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-02-10","draft":false,"title":"Explaining Predictive Models","thumbImage":"2021-02-10-explain-models-small.png","frontPageImage":"2021-02-10-explain-models-small.png","blog":["model explanation","explainable AI","explain","predictive modelling"],"shortExcerpt":"New Orange add-on for explaining predictive models.","longExcerpt":"New Orange Explain add-on for understanding predictions and predictive models.","x2images":false,"oldUrl":"/blog/2021/2021-02-10-explaining-models/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"It is easy to build powerful predictive models in Orange. But how does the model \\\"look like\\\"? Which attributes and which values of those attributes are important? And when making predictions, which attributes contributed to the decision? \", _jsx(_components.strong, {\n        children: \"Orange's new Explain add-on\"\n      }), \" helps you answer all those questions.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2019/2019-11-20-belgrade-workshop/\",\n        name: \"Explaining Models\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Go to \", _jsx(_components.strong, {\n        children: \"Options --\u003e Add-ons\"\n      }), \" and install Explain add-on. Restart Orange for the add-on to appear. It only contains two widgets, but boy are they great!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us start with the attrition data set from the Datasets widget. We will go with \", _jsx(_components.strong, {\n        children: \"Attrition - Train\"\n      }), \", which a data set on which employees resigned from the company and which stayed. The target variable is called Attrition, where \", _jsx(_components.strong, {\n        children: \"No\"\n      }), \" means that the employee stayed and \", _jsx(_components.strong, {\n        children: \"Yes\"\n      }), \" that the employee resigned. The other attributes describe the employee - her position, education, department, years since promotion, and so on.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-data-table.png\",\n      width: \"1052\",\n      height: \"590\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, we will build a simple logistic regression predictive model. If inspecting the model in Test and Score, we learn that the model has an AUC of 0.788 and 86 % CA. But what \", _jsx(_components.em, {\n        children: \"kind\"\n      }), \" of a model is this? How does it makes its decisions?\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us add Explain Model to Logistic Regression and add another connection passing the data. The workflow should look like this:\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-model-workflow.png\",\n      width: \"482\",\n      height: \"209\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-model-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now open Explain Model. The widget lists top ranked variables, which means they contribute the most to the selected target variable. As we are trying to understand why people leave the company, we have set the target variable to Yes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The highest ranked variable is OverTime - this is the variable with the highest impact on the prediction. Having a value Yes in the categorical attribute OverTime (red dots on the right) means the employee is likely to quit. Also, having low job satisfaction contributes to attrition (blue values on the right). The visualization shows the values which have a high impact on the prediction of the selected class on the right and those which vote against the selected class on the left. The color of dot represents the value of the attribute (red for higher values and blue for lower).\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-explain-model.png\",\n      width: \"996\",\n      height: \"763\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-model.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us look at, for example, YearsAtCompany. How would we interpret this? The variable has more red dots on the left, which means high variable values contribute \", _jsx(_components.em, {\n        children: \"against\"\n      }), \" the target value (against attrition, i.e. employees will stay). Red dots refer to the value of the attribute. So if the employee is with the company for a long time (high value == red dot), it means it is more likely she will stay (dots are on the left, while our target value is Yes).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Great, now we understand the model and we are ready to make some predictions. Let us load Attrition - Predict with another Dataset widget. We have three new employees, who are described with all the previous variables, but they are lacking Attrition - we do not know, who is more likely to leave.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-table-predict.png\",\n      width: \"775\",\n      height: \"223\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-table-predict.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now pass the logistic regression model and the train data set to Explain Predictions. Then select John from the table and pass the selection to Explain Predictions. The widget requires three inputs: the model, training data, and the instance we are predicting (John).\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-predict-workflow.png\",\n      width: \"524\",\n      height: \"340\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-predict-workflow.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once again, we are interested in target value Yes. Variables in red increase the probability of the target value (conversely, blue decrease it). The size of the arrow corresponds to the \", _jsx(_components.a, {\n        href: \"https://shap.readthedocs.io/en/latest/index.html\",\n        children: \"SHAP value\"\n      }), \" - in other words, the larger the arrow the larger the variable's contribution to the target value. The model also predicted that John will leave the job with 77 % probability.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-explain-prediction.png\",\n      width: \"751\",\n      height: \"770\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-prediction.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As before, the most important variable for John is overtime. Him working overtime contributes a lot to the final prediction. Also, his job satisfaction is low (1 out of 5), making him likely to quit.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results correspond very much to those of the model, but it might not always be the case. Some people might leave because they are very dissatisfied without working overtime. This would show in Explain Predictions. See how the results change for the other two employees, Rachel and Veronica. Or make up your own employee with Excel and see what would the prediction be.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-models-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"explaining-predictive-models"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>The results correspond very much to those of the model, but it might not always be the case. Some people might leave because they are very dissatisfied without working overtime. This would show in Explain Predictions. See how the results change for the other two employees, Rachel and Veronica. Or make up your own employee with Excel and see what would the prediction be.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-02-10","draft":false,"title":"Explaining Predictive Models","thumbImage":"2021-02-10-explain-models-small.png","frontPageImage":"2021-02-10-explain-models-small.png","blog":["model explanation","explainable AI","explain","predictive modelling"],"shortExcerpt":"New Orange add-on for explaining predictive models.","longExcerpt":"New Orange Explain add-on for understanding predictions and predictive models.","x2images":false,"oldUrl":"/blog/2021/2021-02-10-explaining-models/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, WorkflowScreenshot} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"It is easy to build powerful predictive models in Orange. But how does the model \\\"look like\\\"? Which attributes and which values of those attributes are important? And when making predictions, which attributes contributed to the decision? \", _jsx(_components.strong, {\n        children: \"Orange's new Explain add-on\"\n      }), \" helps you answer all those questions.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2019/2019-11-20-belgrade-workshop/\",\n        name: \"Explaining Models\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Go to \", _jsx(_components.strong, {\n        children: \"Options --\u003e Add-ons\"\n      }), \" and install Explain add-on. Restart Orange for the add-on to appear. It only contains two widgets, but boy are they great!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us start with the attrition data set from the Datasets widget. We will go with \", _jsx(_components.strong, {\n        children: \"Attrition - Train\"\n      }), \", which a data set on which employees resigned from the company and which stayed. The target variable is called Attrition, where \", _jsx(_components.strong, {\n        children: \"No\"\n      }), \" means that the employee stayed and \", _jsx(_components.strong, {\n        children: \"Yes\"\n      }), \" that the employee resigned. The other attributes describe the employee - her position, education, department, years since promotion, and so on.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-data-table.png\",\n      width: \"1052\",\n      height: \"590\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, we will build a simple logistic regression predictive model. If inspecting the model in Test and Score, we learn that the model has an AUC of 0.788 and 86 % CA. But what \", _jsx(_components.em, {\n        children: \"kind\"\n      }), \" of a model is this? How does it makes its decisions?\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us add Explain Model to Logistic Regression and add another connection passing the data. The workflow should look like this:\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-model-workflow.png\",\n      width: \"482\",\n      height: \"209\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-model-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now open Explain Model. The widget lists top ranked variables, which means they contribute the most to the selected target variable. As we are trying to understand why people leave the company, we have set the target variable to Yes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The highest ranked variable is OverTime - this is the variable with the highest impact on the prediction. Having a value Yes in the categorical attribute OverTime (red dots on the right) means the employee is likely to quit. Also, having low job satisfaction contributes to attrition (blue values on the right). The visualization shows the values which have a high impact on the prediction of the selected class on the right and those which vote against the selected class on the left. The color of dot represents the value of the attribute (red for higher values and blue for lower).\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-explain-model.png\",\n      width: \"996\",\n      height: \"763\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-model.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us look at, for example, YearsAtCompany. How would we interpret this? The variable has more red dots on the left, which means high variable values contribute \", _jsx(_components.em, {\n        children: \"against\"\n      }), \" the target value (against attrition, i.e. employees will stay). Red dots refer to the value of the attribute. So if the employee is with the company for a long time (high value == red dot), it means it is more likely she will stay (dots are on the left, while our target value is Yes).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Great, now we understand the model and we are ready to make some predictions. Let us load Attrition - Predict with another Dataset widget. We have three new employees, who are described with all the previous variables, but they are lacking Attrition - we do not know, who is more likely to leave.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-table-predict.png\",\n      width: \"775\",\n      height: \"223\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-table-predict.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now pass the logistic regression model and the train data set to Explain Predictions. Then select John from the table and pass the selection to Explain Predictions. The widget requires three inputs: the model, training data, and the instance we are predicting (John).\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-predict-workflow.png\",\n      width: \"524\",\n      height: \"340\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-predict-workflow.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once again, we are interested in target value Yes. Variables in red increase the probability of the target value (conversely, blue decrease it). The size of the arrow corresponds to the \", _jsx(_components.a, {\n        href: \"https://shap.readthedocs.io/en/latest/index.html\",\n        children: \"SHAP value\"\n      }), \" - in other words, the larger the arrow the larger the variable's contribution to the target value. The model also predicted that John will leave the job with 77 % probability.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-02-explaining-models/2021-02-10-explain-prediction.png\",\n      width: \"751\",\n      height: \"770\",\n      src: \"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-prediction.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As before, the most important variable for John is overtime. Him working overtime contributes a lot to the final prediction. Also, his job satisfaction is low (1 out of 5), making him likely to quit.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results correspond very much to those of the model, but it might not always be the case. Some people might leave because they are very dissatisfied without working overtime. This would show in Explain Predictions. See how the results change for the other two employees, Rachel and Veronica. Or make up your own employee with Excel and see what would the prediction be.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-02-explaining-models/__optimized-images__/2021-02-10-explain-models-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"explaining-predictive-models"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Explorative data analysis with Hierarchical Clustering</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Explorative data analysis with Hierarchical Clustering"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-07-20 07:21:04+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="visualization"/><meta property="article:tag" content="principal component analysis"/><meta property="article:tag" content="visualization"/><meta property="article:tag" content="workflow"/><meta name="next-head-count" content="23"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, clustering, orange3, visualization, principal component analysis, visualization, workflow</strong></p><h1 class="sc-c67a4900-0 loxVcf">Explorative data analysis with Hierarchical Clustering</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 20, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Today we will write about cluster analysis with <strong>Hierarchical Clustering</strong> widget. We use a well-known <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set">Iris data set</a>, which contains 150 Iris flowers, each belonging to one of the three species (setosa, versicolor and virginica). To an untrained eye the three species are very alike, so how could we best tell them apart? The data set contains measurements of sepal and petal dimensions (width and length) and we assume that these gives rise to interesting clustering. But is this so?</p>
 <a href="/blog/2015-07-explorative-data-analysis-with-hierarchical-clustering/__optimized-images__/HierarchicalClustering5.jpg" data-gallery="true"><img alt="" loading="lazy" width="311" height="126" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-explorative-data-analysis-with-hierarchical-clustering/__optimized-images__/HierarchicalClustering5.jpg"/></a>
 <p>Hierarchical Clustering workflow</p>
@@ -158,4 +158,4 @@
 <p>Selected clusters in Hierarchical Clustering widget</p>
 <p>To see these clusters, we select them in <strong>Hierarchical Clustering</strong> widget by clicking on a branch. Selected data will be fed into the output of this widget. Let us inspect the data we have selected by adding <strong>Scatter Plot</strong> and <strong>PCA</strong> widgets. If we draw a <strong>Data Table</strong> directly from <strong>Hierarchical Clustering</strong>, we see the selected instances and the clusters they belong to. But if we first add the <strong>PCA</strong> widget, which decomposes the data into principal components, and then connect it to <strong>Scatter Plot</strong>, we will see the selected instances in the adjusted scatter plot (where principal components are used for x and y-axis).</p>
 <a href="/blog/2015-07-explorative-data-analysis-with-hierarchical-clustering/__optimized-images__/HierarchicalClustering-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1200" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-explorative-data-analysis-with-hierarchical-clustering/__optimized-images__/HierarchicalClustering-Example2.png"/></a>
-<p>Select other clusters in <strong>Hierarchical Clustering</strong> widget to see how the scatter plot visualization changes. This allows for an interesting <strong>explorative data analysis</strong> through a combination of widgets for unsupervised learning and visualizations.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-20 07:21:04+00:00","draft":false,"title":"Explorative data analysis with Hierarchical Clustering","blog":["analysis","clustering","orange3","visualization","principal component analysis","visualization","workflow"],"oldUrl":"/blog/2015/07/20/explorative-data-analysis-with-hierarchical-clustering/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Today we will write about cluster analysis with \", _jsx(_components.strong, {\n        children: \"Hierarchical Clustering\"\n      }), \" widget. 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+<p>Select other clusters in <strong>Hierarchical Clustering</strong> widget to see how the scatter plot visualization changes. This allows for an interesting <strong>explorative data analysis</strong> through a combination of widgets for unsupervised learning and visualizations.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-20 07:21:04+00:00","draft":false,"title":"Explorative data analysis with Hierarchical Clustering","blog":["analysis","clustering","orange3","visualization","principal component analysis","visualization","workflow"],"oldUrl":"/blog/2015/07/20/explorative-data-analysis-with-hierarchical-clustering/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Today we will write about cluster analysis with \", _jsx(_components.strong, {\n        children: \"Hierarchical Clustering\"\n      }), \" widget. 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diff --git a/blog/fall-season-brings-fresh-content-to-the-introduction-to-data-science-series/index.html b/blog/fall-season-brings-fresh-content-to-the-introduction-to-data-science-series/index.html
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@@ -1,4 +1,4 @@
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>update, video series</strong></p><h1 class="sc-c67a4900-0 loxVcf">Fall Season Brings Fresh Content to the Introduction to Data Science Series</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Erika Funa</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 17, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>What better time than the charming fall season to bring to light a long-awaited new batch of videos to our <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-b_SuBdoRsuNhTmaHJ0eKab">Introduction to Data Science Series</a>. The crisp air and the gentle rustle of leaves create the perfect backdrop for staying in with a hot drink and diving into a new chapter of data analysis and processing.</p>
 <p>In the video series so far, we have covered exploratory data analysis, clustering, dimensionality reduction with PCA, t-SNE, and MDS, as well as introduction to classification with trees, forests, and logistic regression. For a taste, or to revisit the last topic, check out the <strong>latest video in the set on logistic regression</strong> below:</p>
 <br/>
@@ -164,4 +164,4 @@
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 <br/>
 <p>We&#x27;d love to hear your impressions, thoughts, or questions in the comments. The next video in the series will be here in no time too, so stick around!</p>
-<p><em>The development of this free, hands-on video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenian Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation. Videos do not require any prior knowledge in math, statistics, or computer science.</em></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Erika Funa","date":"2023-10-17","draft":false,"title":"Fall Season Brings Fresh Content to the Introduction to Data Science Series","thumbImage":"2023-10-17-LogRegress.png","frontPageImage":"2023-10-17-LogRegress.png","blog":["update","video series"],"shortExcerpt":"Updates in the Introduction to Data Science Video Series, new video logistic regression nomogram.","longExcerpt":"Updates in the Introduction to Data Science Video Series, new video logistic regression nomogram.","oldUrl":"/blog/2023/2023-09-01-announcement-nomogram/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"What better time than the charming fall season to bring to light a long-awaited new batch of videos to our \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-b_SuBdoRsuNhTmaHJ0eKab\",\n        children: \"Introduction to Data Science Series\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, regression, tree</strong></p><h1 class="sc-c67a4900-0 loxVcf">Faster classification and regression trees</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 24, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><strong>SimpleTreeLearner</strong> is an implementation of classification and regression trees that sacrifices flexibility for speed. A benchmark on 42 different datasets reveals that <strong>SimpleTreeLearner</strong> is <strong>11 times faster</strong> than the original <strong>TreeLearner</strong>.</p>
 <p>The motivation behind developing a new tree induction algorithm from scratch was to speed up the construction of random forests, but you can also use it as a standalone learner. <strong>SimpleTreeLearner</strong> uses gain ratio for classification and MSE for regression and can handle unknown values.</p>
 <h3>Comparison with TreeLearner</h3>
@@ -180,4 +180,4 @@ <h3>Usage</h3>
 learner = Orange.classification.tree.SimpleTreeLearner(minExamples=<span class="hljs-number">2</span>)
 result = Orange.evaluation.testing.cross_validation([learner], data)
 <span class="hljs-built_in">print</span> <span class="hljs-string">&#x27;CA:&#x27;</span>, Orange.evaluation.scoring.CA(result)[<span class="hljs-number">0</span>]
-</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-08-24 22:26:00+00:00","draft":false,"title":"Faster classification and regression trees","blog":["classification","regression","tree"],"oldUrl":"/blog/2011/08/24/faster-classification-and-regression-trees/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    h3: \"h3\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"SimpleTreeLearner\"\n      }), \" is an implementation of classification and regression trees that sacrifices flexibility for speed. 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Smaller is better.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-08-faster-classification-and-regression-trees/__optimized-images__/simpletree_speed.png__600x641_q95_crop_upscale.png\",\n      alt: \"\",\n      width: \"600\",\n      height: \"641\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The harmonic mean (average speedup) on all the benchmarks is 11.4.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Usage\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The user can set four parameters:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"maxMajority\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Maximal proportion of majority class.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"minExamples\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Minimal number of examples in leaves.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"maxDepth\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Maximal depth of tree.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"skipProb\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At every split an attribute will be skipped with probability skipProb. 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It behaves much like any other Orange learner would.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"iris\\\"\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# build classifier and classify train data\"\n        }), \"\\nclassifier = Orange.classification.tree.SimpleTreeLearner(data, maxMajority=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0.8\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" ex \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" classifier(ex)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# estimate classification accuracy with cross-validation\"\n        }), \"\\nlearner = Orange.classification.tree.SimpleTreeLearner(minExamples=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \")\\nresult = Orange.evaluation.testing.cross_validation([learner], data)\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'CA:'\"\n        }), \", Orange.evaluation.scoring.CA(result)[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n\"]\n      })\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"faster-classification-and-regression-trees"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-08-24 22:26:00+00:00","draft":false,"title":"Faster classification and regression trees","blog":["classification","regression","tree"],"oldUrl":"/blog/2011/08/24/faster-classification-and-regression-trees/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    h3: \"h3\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"SimpleTreeLearner\"\n      }), \" is an implementation of classification and regression trees that sacrifices flexibility for speed. 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Smaller is better.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-08-faster-classification-and-regression-trees/__optimized-images__/simpletree_speed.png__600x641_q95_crop_upscale.png\",\n      alt: \"\",\n      width: \"600\",\n      height: \"641\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The harmonic mean (average speedup) on all the benchmarks is 11.4.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Usage\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The user can set four parameters:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"maxMajority\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Maximal proportion of majority class.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"minExamples\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Minimal number of examples in leaves.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"maxDepth\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Maximal depth of tree.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"skipProb\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At every split an attribute will be skipped with probability skipProb. 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--- a/blog/fink-packages-now-also-64-bit/index.html
+++ b/blog/fink-packages-now-also-64-bit/index.html
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So if you were using 64-bit Fink installation you will be now able also to use Orange (and our binary Fink repository of already compiled packages). Just use this <a href="/svn/orange/trunk/install-scripts/mac/fink-selfupdate-orange.sh">this installation script</a> to configure your local Fink installation to use our binary Fink repository and add information about Orange packages (they are not available among official Fink packages).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-07-03 12:31:00+00:00","draft":false,"title":"Fink packages now also 64-bit","blog":["distribution","download","packaging"],"oldUrl":"/blog/2011/07/03/fink-packages-now-also-64-bit/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [_jsx(_components.a, {\n      href: \"http://www.finkproject.org/\",\n      children: \"Fink packages\"\n    }), \" (we are using for system-wide Orange installations on Mac OS X) were updated to 64-bit. 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So if you were using 64-bit Fink installation you will be now able also to use Orange (and our binary Fink repository of already compiled packages). Just use this <a href="/svn/orange/trunk/install-scripts/mac/fink-selfupdate-orange.sh">this installation script</a> to configure your local Fink installation to use our binary Fink repository and add information about Orange packages (they are not available among official Fink packages).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-07-03 12:31:00+00:00","draft":false,"title":"Fink packages now also 64-bit","blog":["distribution","download","packaging"],"oldUrl":"/blog/2011/07/03/fink-packages-now-also-64-bit/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [_jsx(_components.a, {\n      href: \"http://www.finkproject.org/\",\n      children: \"Fink packages\"\n    }), \" (we are using for system-wide Orange installations on Mac OS X) were updated to 64-bit. 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@@ -1,4 +1,4 @@
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - For When You Want to Transpose a Data Table...</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - For When You Want to Transpose a Data Table..."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-02-03 13:16:37+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="bioinformatics"/><meta property="article:tag" content="feature engineering"/><meta property="article:tag" content="features"/><meta property="article:tag" content="orange3"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, bioinformatics, feature engineering, features, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">For When You Want to Transpose a Data Table...</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 03, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Sometimes, you need something more. Something different. Something, that helps you look at the world from a different perspective. Sometimes, you simply need to transpose your data.</p>
 <p>Since version 3.3.9, Orange has a Transpose widget that flips your data table around. Columns become rows and rows become columns. This is often useful, if you have, say, biological data.</p>
 <p><strong>Related:</strong> <a href="/blog/2015/07/31/datasets-in-orange-bioinformatics-add-on/">Datasets in Orange Bioinformatics</a></p>
@@ -165,4 +165,4 @@
 <a href="/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/scatter-plot-pca.png" data-gallery="true"><img alt="" loading="lazy" width="716" height="672" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/scatter-plot-pca.png"/></a>
 <p>Now, if you are reproducing the result, you probably won&#x27;t see these nice colors for class.</p>
 <a href="/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/Screen-Shot-2017-02-03-at-12.29.20.png" data-gallery="true"><img alt="" loading="lazy" width="1074" height="482" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/Screen-Shot-2017-02-03-at-12.29.20.png"/></a>
-<p>This is because we used the Create Class widget to help us create new class values. Create Class already available in <a href="https://github.com/biolab/orange3-prototypes">Orange3-Prototypes</a> add-on and will be included in a future Orange release. You can learn more about it soon... :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-02-03 13:16:37+00:00","draft":false,"title":"For When You Want to Transpose a Data Table...","blog":["analysis","bioinformatics","feature engineering","features","orange3"],"oldUrl":"/blog/2017/02/03/for-when-you-want-to-transpose-a-data-table/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Sometimes, you need something more. Something different. Something, that helps you look at the world from a different perspective. Sometimes, you simply need to transpose your data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Since version 3.3.9, Orange has a Transpose widget that flips your data table around. Columns become rows and rows become columns. This is often useful, if you have, say, biological data.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2015/07/31/datasets-in-orange-bioinformatics-add-on/\",\n        children: \"Datasets in Orange Bioinformatics\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Today we will play around with \", _jsx(_components.em, {\n        children: \"brown-selected.tab\"\n      }), \", a data set on gene expression levels for 79 experiments. Our data table has genes in rows and experiments in columns, with gene expression levels recorded as values.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/data-table-normal.png\",\n      alt: \"\",\n      width: \"2190\",\n      height: \"1112\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This representation focuses on exploring genes and allows them to be plotted or to construct a model to predict their functions. But what if I want to explore the experimental conditions and see how different external conditions influence the yeast cells? For this, it would be better to have experiments in rows and genes in columns. We can do this with Transpose.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/data-table-transposed.png\",\n      alt: \"\",\n      width: \"2186\",\n      height: \"1110\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Transpose widget took our \", _jsx(_components.em, {\n        children: \"gene\"\n      }), \" meta attribute and used it for the new column names (YGR270W, YIL075C, etc.). It also appended class values to columns (Proteas). Former columns names (alpha 0, alpha 7, etc.) became our new meta attribute called \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, we have a transposed data table. 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A scatter plot after a 2-D transformation looks like this:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/scatter-plot-big.png\",\n      alt: \"\",\n      width: \"2258\",\n      height: \"1664\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.em, {\n        children: \"Spo5 11\"\n      }), \" lies quite far from the rest, so it could be an experiment to look out for. If we zoom in on the big cluster, we see that similar experiments indeed lie closer together.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/scatter-plot-pca.png\",\n      alt: \"\",\n      width: \"716\",\n      height: \"672\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, if you are reproducing the result, you probably won't see these nice colors for class.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/Screen-Shot-2017-02-03-at-12.29.20.png\",\n      alt: \"\",\n      width: \"1074\",\n      height: \"482\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is because we used the Create Class widget to help us create new class values. 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\ No newline at end of file
+<p>This is because we used the Create Class widget to help us create new class values. Create Class already available in <a href="https://github.com/biolab/orange3-prototypes">Orange3-Prototypes</a> add-on and will be included in a future Orange release. You can learn more about it soon... :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-02-03 13:16:37+00:00","draft":false,"title":"For When You Want to Transpose a Data Table...","blog":["analysis","bioinformatics","feature engineering","features","orange3"],"oldUrl":"/blog/2017/02/03/for-when-you-want-to-transpose-a-data-table/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Sometimes, you need something more. Something different. Something, that helps you look at the world from a different perspective. Sometimes, you simply need to transpose your data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Since version 3.3.9, Orange has a Transpose widget that flips your data table around. Columns become rows and rows become columns. This is often useful, if you have, say, biological data.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2015/07/31/datasets-in-orange-bioinformatics-add-on/\",\n        children: \"Datasets in Orange Bioinformatics\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Today we will play around with \", _jsx(_components.em, {\n        children: \"brown-selected.tab\"\n      }), \", a data set on gene expression levels for 79 experiments. Our data table has genes in rows and experiments in columns, with gene expression levels recorded as values.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/data-table-normal.png\",\n      alt: \"\",\n      width: \"2190\",\n      height: \"1112\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This representation focuses on exploring genes and allows them to be plotted or to construct a model to predict their functions. But what if I want to explore the experimental conditions and see how different external conditions influence the yeast cells? For this, it would be better to have experiments in rows and genes in columns. We can do this with Transpose.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/data-table-transposed.png\",\n      alt: \"\",\n      width: \"2186\",\n      height: \"1110\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Transpose widget took our \", _jsx(_components.em, {\n        children: \"gene\"\n      }), \" meta attribute and used it for the new column names (YGR270W, YIL075C, etc.). It also appended class values to columns (Proteas). Former columns names (alpha 0, alpha 7, etc.) became our new meta attribute called \", _jsx(_components.em, {\n        children: \"Feature name\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, we have a transposed data table. 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A scatter plot after a 2-D transformation looks like this:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/scatter-plot-big.png\",\n      alt: \"\",\n      width: \"2258\",\n      height: \"1664\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.em, {\n        children: \"Spo5 11\"\n      }), \" lies quite far from the rest, so it could be an experiment to look out for. If we zoom in on the big cluster, we see that similar experiments indeed lie closer together.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/scatter-plot-pca.png\",\n      alt: \"\",\n      width: \"716\",\n      height: \"672\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, if you are reproducing the result, you probably won't see these nice colors for class.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-for-when-you-want-to-transpose-a-data-table/__optimized-images__/Screen-Shot-2017-02-03-at-12.29.20.png\",\n      alt: \"\",\n      width: \"1074\",\n      height: \"482\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is because we used the Create Class widget to help us create new class values. Create Class already available in \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-prototypes\",\n        children: \"Orange3-Prototypes\"\n      }), \" add-on and will be included in a future Orange release. You can learn more about it soon... :)\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"for-when-you-want-to-transpose-a-data-table"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - From Surveys to Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - From Surveys to Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-06-12 07:54:17+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="data"/><meta property="article:tag" content="dataloading"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, dataloading, orange3, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">From Surveys to Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 12, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Today we have finished a series of workshops for the Ministry of Public Affairs. This was a year-long cooperation and we had many students asking many different questions. There was however one that we talked about a lot. If I have a survey, how do I get it into Orange?</p>
 <p><strong>Related:</strong> <a href="/blog/2017/10/26/analyzing-surveys/">Analyzing Surveys</a></p>
 <a href="/blog/2018-06-from-surveys-to-orange/__optimized-images__/IMG_20180611_120525.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="672" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-06-from-surveys-to-orange/__optimized-images__/IMG_20180611_120525.jpg"/></a>
@@ -162,4 +162,4 @@
 <p>Now, all I have to do is open Orange, place EnKlik Anketa widget from the Prototypes add-on onto the canvas, enter the public link into the &#x27;Public link URL&#x27; fields and press Enter. If your data has loaded successfully, the widget will display available variables and information in the Info pane.</p>
 <a href="/blog/2018-06-from-surveys-to-orange/__optimized-images__/Screen-Shot-2018-06-11-at-15.59.01.png" data-gallery="true"><img alt="" loading="lazy" width="1064" height="1104" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-06-from-surveys-to-orange/__optimized-images__/Screen-Shot-2018-06-11-at-15.59.01.png"/></a>
 <p>From here on you can continue your analysis just like you would with any other data source!</p>
-<a href="/blog/2018-06-from-surveys-to-orange/__optimized-images__/Screen-Shot-2018-06-12-at-09.46.23.png" data-gallery="true"><img alt="" loading="lazy" width="822" height="562" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-06-from-surveys-to-orange/__optimized-images__/Screen-Shot-2018-06-12-at-09.46.23.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-06-12 07:54:17+00:00","draft":false,"title":"From Surveys to Orange","blog":["data","dataloading","orange3","workshop"],"oldUrl":"/blog/2018/06/12/from-surveys-to-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Today we have finished a series of workshops for the Ministry of Public Affairs. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics, gene expression, line plot</strong></p><h1 class="sc-c67a4900-0 loxVcf">Gene Expression Profiles with Line Plot</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 03, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Line Plot is one of our recent additions to the visualization widgets. It shows data profiles, meaning it plots values for all features in the data set. Each data instance in a line plot is a line or a &#x27;profile&#x27;.</p>
 <p>The widget can show four types of information – individual data profiles (lines), data range, mean profile and error bars. It has the same cool features of other Orange visualizations – it is interactive, meaning you can select a subset of data instances from the plot, it allows grouping by a discrete variable, and it highlights an incoming data subset.</p>
 <p>Related: <a href="/blog/2018/12/21/scatter-plots-the-tour/" target="_blank" rel="noreferrer">Scatter Plot: The Tour</a></p>
@@ -181,4 +181,4 @@
 <a href="/blog/2019-06-gene-expression-profiles-with-line-plot/__optimized-images__/gene-expression-5.png" data-gallery="true"><img alt="" loading="lazy" width="688" height="250" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-06-gene-expression-profiles-with-line-plot/__optimized-images__/gene-expression-5.png"/></a>
 <p><br/>
 <!-- -->\</p>
-<p>This is it. Line Plot is really simple to use and can reveal many interesting things not only for biologists, but for any kind of data analyst. Next week we will talk about how to work with timeseries data in combination with the Line Plot.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-06-03","draft":false,"title":"Gene Expression Profiles with Line Plot","thumbImage":"gene-blog.png","frontPageImage":"gene-blog.png","blog":["bioinformatics","gene expression","line plot"],"shortExcerpt":"We show how to explore gene expression profiles with the new Line Plot widget.","longExcerpt":"Line Plot shows profiles of data instances – each instance is a line in the plot and its profile are values across all variables in the data. We show how to explore gene expression profiles.","oldUrl":"/blog/2019/6/gene-expression-profiles-with-line-plot/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    br: \"br\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Line Plot is one of our recent additions to the visualization widgets. It shows data profiles, meaning it plots values for all features in the data set. Each data instance in a line plot is a line or a 'profile'.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget can show four types of information – individual data profiles (lines), data range, mean profile and error bars. It has the same cool features of other Orange visualizations – it is interactive, meaning you can select a subset of data instances from the plot, it allows grouping by a discrete variable, and it highlights an incoming data subset.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/12/21/scatter-plots-the-tour/\",\n        name: \"Scatter Plot: The Tour\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us check a simple example. We will use brown-selected data, which is a data on gene expression of baker's yeast. 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Let us select the most interesting region in the plot by selecting the zoom tool and dragging across the area of interest.\\n\", _jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-06-gene-expression-profiles-with-line-plot/gene-expression-2.png\",\n      src: \"80%\",\n      width: \"1600\",\n      height: \"892\",\n      src: \"/blog/2019-06-gene-expression-profiles-with-line-plot/__optimized-images__/gene-expression-2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We see that spo-mid feature distinguishes really well between protease and two other gene types and that values of protease are normally high for spo-mid.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Another thing we can do is select a subset from the plot. 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\ No newline at end of file
+<p>This is it. Line Plot is really simple to use and can reveal many interesting things not only for biologists, but for any kind of data analyst. Next week we will talk about how to work with timeseries data in combination with the Line Plot.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-06-03","draft":false,"title":"Gene Expression Profiles with Line Plot","thumbImage":"gene-blog.png","frontPageImage":"gene-blog.png","blog":["bioinformatics","gene expression","line plot"],"shortExcerpt":"We show how to explore gene expression profiles with the new Line Plot widget.","longExcerpt":"Line Plot shows profiles of data instances – each instance is a line in the plot and its profile are values across all variables in the data. 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Each data instance in a line plot is a line or a 'profile'.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget can show four types of information – individual data profiles (lines), data range, mean profile and error bars. It has the same cool features of other Orange visualizations – it is interactive, meaning you can select a subset of data instances from the plot, it allows grouping by a discrete variable, and it highlights an incoming data subset.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/12/21/scatter-plots-the-tour/\",\n        name: \"Scatter Plot: The Tour\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us check a simple example. We will use brown-selected data, which is a data on gene expression of baker's yeast. To observe gene expression profiles, we will use Line Plot.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-06-gene-expression-profiles-with-line-plot/gene-expression-1.png\",\n      src: \"80%\",\n      width: \"1600\",\n      height: \"892\",\n      src: \"/blog/2019-06-gene-expression-profiles-with-line-plot/__optimized-images__/gene-expression-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Since the data has class, which represents a function of the gene, Line Plot will automatically group by class variable. It seems like protease, respiratory and ribosome genes have quite distinctive profiles! 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\ No newline at end of file
diff --git a/blog/getting-started-series-part-two/index.html b/blog/getting-started-series-part-two/index.html
index 29eb8f958..2a54a594f 100644
--- a/blog/getting-started-series-part-two/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>tutorial, youtube</strong></p><h1 class="sc-c67a4900-0 loxVcf">Getting Started Series: Part Two</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 26, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We&#x27;ve recently published two more videos in our Getting Started with Orange series. The series is intended to introduce beginners to Orange and teach them how to use its components.</p>
 <p>The first video explains how to do hierarchical clustering and select interesting subsets directly in Orange:</p>
 <div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/dJ5z2SRwzgs" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div>
 <p>while the second video introduces classification trees and predictive modelling:</p>
 <div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/D6zd7m2aYqU" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div>
 <p>The seventh video in the series will address how to score classification and regression models by different evaluation methods. Fruits and vegetables data set can be found <a href="https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/fruits-and-vegetables-train.tab">here</a>.</p>
-<p>If you have an idea what you&#x27;d like to see in the upcoming videos, let us know!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-02-26 10:35:33+00:00","draft":false,"title":"Getting Started Series: Part Two","blog":["tutorial","youtube"],"oldUrl":"/blog/2016/02/26/getting-started-series-pt2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We've recently published two more videos in our Getting Started with Orange series. The series is intended to introduce beginners to Orange and teach them how to use its components.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first video explains how to do hierarchical clustering and select interesting subsets directly in Orange:\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"dJ5z2SRwzgs\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"while the second video introduces classification trees and predictive modelling:\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"D6zd7m2aYqU\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The seventh video in the series will address how to score classification and regression models by different evaluation methods. Fruits and vegetables data set can be found \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/fruits-and-vegetables-train.tab\",\n        children: \"here\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you have an idea what you'd like to see in the upcoming videos, let us know!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"getting-started-series-part-two"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>If you have an idea what you&#x27;d like to see in the upcoming videos, let us know!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-02-26 10:35:33+00:00","draft":false,"title":"Getting Started Series: Part Two","blog":["tutorial","youtube"],"oldUrl":"/blog/2016/02/26/getting-started-series-pt2/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We've recently published two more videos in our Getting Started with Orange series. The series is intended to introduce beginners to Orange and teach them how to use its components.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first video explains how to do hierarchical clustering and select interesting subsets directly in Orange:\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"dJ5z2SRwzgs\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"while the second video introduces classification trees and predictive modelling:\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"D6zd7m2aYqU\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The seventh video in the series will address how to score classification and regression models by different evaluation methods. Fruits and vegetables data set can be found \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/fruits-and-vegetables-train.tab\",\n        children: \"here\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you have an idea what you'd like to see in the upcoming videos, let us know!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"getting-started-series-part-two"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, data, distribution, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Ghostbusters</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 30, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Ok, we’ve just recently stumbled across <a href="http://www.isixsigma.com/tools-templates/normality/dealing-non-normal-data-strategies-and-tools/">an interesting article</a> on how to deal with non normal (non-Gaussian distributed) data.
 <a href="/blog/2015-10-ghostbusters/__optimized-images__/paranormal1.png" data-gallery="true"><img alt="" loading="lazy" width="674" height="539" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-ghostbusters/__optimized-images__/paranormal1.png"/></a></p>
 <p>We have an absolutely paranormal data set of 20 persons with weight, height, paleness, vengefulness, habitation and age attributes (<a href="https://docs.google.com/spreadsheets/d/1KycPyD1KdX8NFCQhS6OnrOZqVds5nppljwFCesba3RA/edit?usp=sharing">download</a>).</p>
@@ -167,4 +167,4 @@
 <p>Secondly, why can’t we use Outliers widget to hunt for those ghosts? Again, our data set is too small. With just 20 instances, the estimation variance is so large that it can easily cover a few ghosts under its sheet. We don’t have enough “normal” data to define what is normal and thus detect the paranormal.</p>
 <p>Haven’t we just written two exactly opposite things? Perhaps.</p>
 <p>Happy Halloween everybody! :)</p>
-<a href="/blog/2015-10-ghostbusters/__optimized-images__/spooky-orange.jpg" data-gallery="true"><img alt="" loading="lazy" width="362" height="377" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-ghostbusters/__optimized-images__/spooky-orange.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-10-30 15:07:34+00:00","draft":false,"title":"Ghostbusters","blog":["analysis","data","distribution","orange3"],"oldUrl":"/blog/2015/10/30/ghostbusters/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Ok, we’ve just recently stumbled across \", _jsx(_components.a, {\n        href: \"http://www.isixsigma.com/tools-templates/normality/dealing-non-normal-data-strategies-and-tools/\",\n        children: \"an interesting article\"\n      }), \" on how to deal with non normal (non-Gaussian distributed) data.\\n\", _jsx(_components.img, {\n        src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal1.png\",\n        alt: \"\",\n        width: \"674\",\n        height: \"539\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We have an absolutely paranormal data set of 20 persons with weight, height, paleness, vengefulness, habitation and age attributes (\", _jsx(_components.a, {\n        href: \"https://docs.google.com/spreadsheets/d/1KycPyD1KdX8NFCQhS6OnrOZqVds5nppljwFCesba3RA/edit?usp=sharing\",\n        children: \"download\"\n      }), \").\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal2.png\",\n      alt: \"\",\n      width: \"987\",\n      height: \"423\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let’s check the distribution in Distributions widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal3.png\",\n      alt: \"\",\n      width: \"831\",\n      height: \"523\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Our first attribute is “\", _jsx(_components.em, {\n        children: \"Weight\"\n      }), \"” and we see a little hump on the left. Otherwise the data would be normally distributed. Ok, so perhaps we have a few children in the data set. Let’s check the age distribution.\\n\", _jsx(_components.img, {\n        src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal4.png\",\n        alt: \"\",\n        width: \"831\",\n        height: \"523\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Whoa, what? Why is the hump now on the right? These distributions look scary. We seem to have a few reaaaaally old people here. What is going on? Perhaps we can figure this out with MDS. This widget projects the data into two dimensions so that the distances between the points correspond to differences between the data instances.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal5.png\",\n      alt: \"\",\n      width: \"877\",\n      height: \"672\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Aha! Now we see that three instances are quite different from all others. Select them and send them to the Data Table for final inspection.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal6.png\",\n      alt: \"\",\n      width: \"866\",\n      height: \"423\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Busted! We have found three ghosts hiding in our data. They are extremely light (the sheet they are wearing must weight around 2kg), quite vengeful and old.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, joke aside, what would this mean for a general non-normally distributed data? One thing is your data set might be too small. Here we only have 20 instances, thus 3 outlying ghosts have a great impact on the distribution. 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Now we see that three instances are quite different from all others. Select them and send them to the Data Table for final inspection.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-10-ghostbusters/__optimized-images__/paranormal6.png\",\n      alt: \"\",\n      width: \"866\",\n      height: \"423\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Busted! We have found three ghosts hiding in our data. They are extremely light (the sheet they are wearing must weight around 2kg), quite vengeful and old.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, joke aside, what would this mean for a general non-normally distributed data? One thing is your data set might be too small. Here we only have 20 instances, thus 3 outlying ghosts have a great impact on the distribution. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Girls Go Data Mining</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Girls Go Data Mining"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-06-21 09:23:09+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="education"/><meta property="article:tag" content="interactive data visualization"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, education, interactive data visualization, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Girls Go Data Mining</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 21, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This week we held our first Girls Go Data Mining workshop. The workshop brought together curious women and intuitively introduced them to essential data mining and machine learning concepts. Of course, we used Orange to explore visualizations, build predictive models, perform clustering and dive into text analysis. The workshop was supported by <a href="https://www.numfocus.org/">NumFocus</a> through their <a href="https://www.numfocus.org/programs/grants-sponsored-events">small development grant</a> initiative and we hope to repeat it next year with even more ladies attending!</p>
 <p><strong>Related:</strong> <a href="/blog/2017/06/09/workshop-text-analysis-for-social-scientists/">Text Analysis for Social Scientists</a></p>
 <a href="/blog/2018-06-girls-go-data-mining/__optimized-images__/punce1.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="593" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-06-girls-go-data-mining/__optimized-images__/punce1.jpg"/></a>
@@ -181,4 +181,4 @@
 <p>It looks like animals from our selected cluster have feathers. Probably, this is a cluster of birds. We can check this with the same procedure as above.</p>
 <p>In summary, most Orange visualizations have two outputs - Selected Data and Data. Selected Data will output a subset of data instances selected in the visualization (or selected clusters in the case of hierarchical clustering), while Data will output the entire data table with a column defining whether a data instance was selected or not. This is very useful if we want to inspect what is typical of an interesting group in our data, inspect clusters or even manually define groups.</p>
 <a href="/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-11.19.55.png" data-gallery="true"><img alt="" loading="lazy" width="1116" height="386" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-11.19.55.png"/></a>
-<p>Overall, this was another interesting workshop and we hope to continue our fruitful partnership with NumFocus and keep offering free educational events for beginners and experts alike!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-06-21 09:23:09+00:00","draft":false,"title":"Girls Go Data Mining","blog":["clustering","education","interactive data visualization","workshop"],"oldUrl":"/blog/2018/06/21/girls-go-data-mining/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This week we held our first Girls Go Data Mining workshop. The workshop brought together curious women and intuitively introduced them to essential data mining and machine learning concepts. Of course, we used Orange to explore visualizations, build predictive models, perform clustering and dive into text analysis. The workshop was supported by \", _jsx(_components.a, {\n        href: \"https://www.numfocus.org/\",\n        children: \"NumFocus\"\n      }), \" through their \", _jsx(_components.a, {\n        href: \"https://www.numfocus.org/programs/grants-sponsored-events\",\n        children: \"small development grant\"\n      }), \" initiative and we hope to repeat it next year with even more ladies attending!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2017/06/09/workshop-text-analysis-for-social-scientists/\",\n        children: \"Text Analysis for Social Scientists\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/punce1.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"593\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/punce2.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"944\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/punce3.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"847\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In two days, we covered many topics. On day one, we got to know Orange and the concept of visual programming, where the user construct analytical workflow by stacking visual components. Then we got to know several useful visualizations, such as box plot, scatter plot, distributions, and mosaic display, which give us an initial overview of the data and the potentially interesting patterns. Finally, we got our hands dirty with predictive modeling. We learnt about decision trees, logistic regression, and naive Bayes classifiers, and observed the models in tree viewer and nomogram. It is great having interpretable models and we had great fun exploring what is in the model!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"On the second day, we tried to uncover groups in our data with clustering. First, we tried hierarchical clustering and explored the discovered clusters with box plot. Then we also tried k-means and learnt, why this method is better than hierarchical clustering. In the final part, we talked about the methods for text mining, how to do preprocessing, construct a bag of words and perform the machine learning on corpora. We used both clustering and classification and tried to find interesting information about Grimm tales.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.37.51.png\",\n      alt: \"\",\n      width: \"1446\",\n      height: \"1024\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One of our workflows, where we explored the data in many different ways, including inspecting misclassifications in a scatter plot!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One thing that always comes up as really useful in our workshops is Orange's ability to output different types of data. For example, in Hierarchical Clustering, we can select the similarity cutoff at the top and output clusters. Our data table will have an additional column Cluster, with cluster labels for each data instance.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.27.00.png\",\n      alt: \"\",\n      width: \"1298\",\n      height: \"550\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hierarchial Clustering outputs data with an additional Cluster column.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.23.14.png\",\n      alt: \"\",\n      width: \"1004\",\n      height: \"232\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can explore clusters by connecting a Box Plot to Hierarchical Clustering, selecting Cluster in Subgroups and using Order by relevance option. This sorts the variables in Box Plot by how well they separate between clusters or, in other words, what is typical of each cluster.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.23.10.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"860\"\n      }), \"\\nWe have selected Cluster in Subgroups section and ticked 'Order by relevance' to sort the variables. Variables at the top are the most interesting ones. Looks like giving milk is an exclusive property of cluster C1.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We used \", _jsx(_components.em, {\n        children: \"zoo.tab\"\n      }), \" and made the cutoff at three clusters. It looks like the first cluster gives milk. Could these be a cluster of mammals?\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.24.09.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"860\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We said giving milk is a property of cluster C1. By selecting type as our variable, we can see that C1 is a cluster of mammals.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Indeed it is!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another option is to select a specific cluster in the dendrogram. Then, we have to rewire the connection between Hierarchical Clustering and Box Plot by setting it to Data. Data option will output the entire data set, with an extra column showing whether the data instance was selected or not. In our case, there would be a Yes if the instance is in the selected cluster and No if it is not.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.59.09.png\",\n        alt: \"\",\n        width: \"1242\",\n        height: \"982\"\n      }), \"\\nTo rewire the connection, double-click on it and drag a line from Data to Data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.55.09.png\",\n        alt: \"\",\n        width: \"1312\",\n        height: \"594\"\n      }), \"\\nWe have selected one cluster in the dendrogram, rewired the connection to transmit Data (instead of Selected Data) and observed the results in a Data Table. We see an additional Selected column, which shows whether a data instance was selected in the visualization or not.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then we can use Box Plot to observe what is particular for our selected cluster.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.55.28.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"865\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this Box Plot we have used Selected in the Subgroups section and kept 'Order by relevance' on. The suggested distinctive feature of our selected cluster is having feathers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It looks like animals from our selected cluster have feathers. Probably, this is a cluster of birds. We can check this with the same procedure as above.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In summary, most Orange visualizations have two outputs - Selected Data and Data. Selected Data will output a subset of data instances selected in the visualization (or selected clusters in the case of hierarchical clustering), while Data will output the entire data table with a column defining whether a data instance was selected or not. This is very useful if we want to inspect what is typical of an interesting group in our data, inspect clusters or even manually define groups.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-11.19.55.png\",\n      alt: \"\",\n      width: \"1116\",\n      height: \"386\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Overall, this was another interesting workshop and we hope to continue our fruitful partnership with NumFocus and keep offering free educational events for beginners and experts alike!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"girls-go-data-mining"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Overall, this was another interesting workshop and we hope to continue our fruitful partnership with NumFocus and keep offering free educational events for beginners and experts alike!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-06-21 09:23:09+00:00","draft":false,"title":"Girls Go Data Mining","blog":["clustering","education","interactive data visualization","workshop"],"oldUrl":"/blog/2018/06/21/girls-go-data-mining/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This week we held our first Girls Go Data Mining workshop. The workshop brought together curious women and intuitively introduced them to essential data mining and machine learning concepts. Of course, we used Orange to explore visualizations, build predictive models, perform clustering and dive into text analysis. The workshop was supported by \", _jsx(_components.a, {\n        href: \"https://www.numfocus.org/\",\n        children: \"NumFocus\"\n      }), \" through their \", _jsx(_components.a, {\n        href: \"https://www.numfocus.org/programs/grants-sponsored-events\",\n        children: \"small development grant\"\n      }), \" initiative and we hope to repeat it next year with even more ladies attending!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2017/06/09/workshop-text-analysis-for-social-scientists/\",\n        children: \"Text Analysis for Social Scientists\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/punce1.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"593\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/punce2.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"944\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/punce3.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"847\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In two days, we covered many topics. On day one, we got to know Orange and the concept of visual programming, where the user construct analytical workflow by stacking visual components. Then we got to know several useful visualizations, such as box plot, scatter plot, distributions, and mosaic display, which give us an initial overview of the data and the potentially interesting patterns. Finally, we got our hands dirty with predictive modeling. We learnt about decision trees, logistic regression, and naive Bayes classifiers, and observed the models in tree viewer and nomogram. It is great having interpretable models and we had great fun exploring what is in the model!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"On the second day, we tried to uncover groups in our data with clustering. First, we tried hierarchical clustering and explored the discovered clusters with box plot. Then we also tried k-means and learnt, why this method is better than hierarchical clustering. In the final part, we talked about the methods for text mining, how to do preprocessing, construct a bag of words and perform the machine learning on corpora. We used both clustering and classification and tried to find interesting information about Grimm tales.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.37.51.png\",\n      alt: \"\",\n      width: \"1446\",\n      height: \"1024\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One of our workflows, where we explored the data in many different ways, including inspecting misclassifications in a scatter plot!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One thing that always comes up as really useful in our workshops is Orange's ability to output different types of data. For example, in Hierarchical Clustering, we can select the similarity cutoff at the top and output clusters. Our data table will have an additional column Cluster, with cluster labels for each data instance.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.27.00.png\",\n      alt: \"\",\n      width: \"1298\",\n      height: \"550\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hierarchial Clustering outputs data with an additional Cluster column.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.23.14.png\",\n      alt: \"\",\n      width: \"1004\",\n      height: \"232\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can explore clusters by connecting a Box Plot to Hierarchical Clustering, selecting Cluster in Subgroups and using Order by relevance option. This sorts the variables in Box Plot by how well they separate between clusters or, in other words, what is typical of each cluster.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.23.10.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"860\"\n      }), \"\\nWe have selected Cluster in Subgroups section and ticked 'Order by relevance' to sort the variables. Variables at the top are the most interesting ones. Looks like giving milk is an exclusive property of cluster C1.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We used \", _jsx(_components.em, {\n        children: \"zoo.tab\"\n      }), \" and made the cutoff at three clusters. It looks like the first cluster gives milk. Could these be a cluster of mammals?\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.24.09.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"860\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We said giving milk is a property of cluster C1. By selecting type as our variable, we can see that C1 is a cluster of mammals.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Indeed it is!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another option is to select a specific cluster in the dendrogram. Then, we have to rewire the connection between Hierarchical Clustering and Box Plot by setting it to Data. Data option will output the entire data set, with an extra column showing whether the data instance was selected or not. In our case, there would be a Yes if the instance is in the selected cluster and No if it is not.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.59.09.png\",\n        alt: \"\",\n        width: \"1242\",\n        height: \"982\"\n      }), \"\\nTo rewire the connection, double-click on it and drag a line from Data to Data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.55.09.png\",\n        alt: \"\",\n        width: \"1312\",\n        height: \"594\"\n      }), \"\\nWe have selected one cluster in the dendrogram, rewired the connection to transmit Data (instead of Selected Data) and observed the results in a Data Table. We see an additional Selected column, which shows whether a data instance was selected in the visualization or not.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then we can use Box Plot to observe what is particular for our selected cluster.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-10.55.28.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"865\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this Box Plot we have used Selected in the Subgroups section and kept 'Order by relevance' on. The suggested distinctive feature of our selected cluster is having feathers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It looks like animals from our selected cluster have feathers. Probably, this is a cluster of birds. We can check this with the same procedure as above.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In summary, most Orange visualizations have two outputs - Selected Data and Data. Selected Data will output a subset of data instances selected in the visualization (or selected clusters in the case of hierarchical clustering), while Data will output the entire data table with a column defining whether a data instance was selected or not. This is very useful if we want to inspect what is typical of an interesting group in our data, inspect clusters or even manually define groups.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-06-girls-go-data-mining/__optimized-images__/Screen-Shot-2018-06-21-at-11.19.55.png\",\n      alt: \"\",\n      width: \"1116\",\n      height: \"386\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Overall, this was another interesting workshop and we hope to continue our fruitful partnership with NumFocus and keep offering free educational events for beginners and experts alike!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"girls-go-data-mining"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
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--- a/blog/golden-sublime-triangles-in-orange/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Golden (sublime) triangles in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MARKO</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 19, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Hand in hand with the development of the <a href="/blog/2011/06/30/orange-gsoc-visualizations-with-qt/">new visualization framework</a> and the financial crisis we are putting some gold into Orange. The arrows at the ends of the axes are, as of today, small <a href="http://en.wikipedia.org/wiki/Golden_triangle_(mathematics)">golden triangles</a>. See the changes in <a href="http://orange.biolab.si/trac/intertrac/source%3Atrunk/orange/OrangeWidgets/plot/owaxis.py">owaxis.py</a>!</p>
 <pre><code>    -        path.moveTo(0, 3)
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+</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-08-19 14:18:00+00:00","draft":false,"title":"Golden (sublime) triangles in Orange","blog":["visualization"],"oldUrl":"/blog/2011/08/19/golden-sublime-triangles-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Hand in hand with the development of the \", _jsx(_components.a, {\n        href: \"/blog/2011/06/30/orange-gsoc-visualizations-with-qt/\",\n        children: \"new visualization framework\"\n      }), \" and the financial crisis we are putting some gold into Orange. The arrows at the ends of the axes are, as of today, small \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Golden_triangle_(mathematics)\",\n        children: \"golden triangles\"\n      }), \". See the changes in \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/trac/intertrac/source%3Atrunk/orange/OrangeWidgets/plot/owaxis.py\",\n        children: \"owaxis.py\"\n      }), \"!\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    -        path.moveTo(0, 3)\\n    -        path.lineTo(0, -3)\\n    -        path.lineTo(5, 0)\\n    +        path.moveTo(0, 3.09)\\n    +        path.lineTo(0, -3.09)\\n    +        path.lineTo(9.51, 0)\\n\"\n      })\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"golden-sublime-triangles-in-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">GSoC Mentor Summit</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 26, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>On 22th and 23th October 2011 there was <a href="https://code.google.com/soc/">Google Summer of Code</a> <a href="http://gsoc-wiki.osuosl.org/index.php/2011">Mentor Summit</a> in <a href="http://en.wikipedia.org/wiki/Mountain_View,_California">Mountain View, California</a>. Google Summer of Code is Google&#x27;s program for encouraging students to work on open-source projects during their summer break. Because this year Orange participated in this program too, we decided to participate also in this summit and get to know other mentors, other open-source projects and organizations, exchange our experiences, learn something new, and improve our connections and collaborations with others.</p>
 <table><thead><tr><th><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1593_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1593_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th><th><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1538_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1538_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th><th><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1548_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1548_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></th></tr></thead><tbody><tr><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1554_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1554_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1559_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1559_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1563_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1563_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1569_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1569_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1571_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1571_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1578_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1578_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1582_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1582_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1592_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1592_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1595_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1595_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1596_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1596_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1551_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1551_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1567_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1567_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1575_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1575_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1591_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1591_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1594_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1594_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td></tr><tr><td><a href="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1597_1.jpg__160x160_q95_crop-True_upscale-False.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-10-gsoc-mentor-summit/__optimized-images__/img_1597_1.jpg__160x160_q95_crop-True_upscale-False.jpg"/></a></td><td></td><td></td></tr></tbody></table>
 <p>We went to the meeting together with another Slovenian open-source project: <em><a href="http://wlan-si.net/">wlan slovenija</a></em>, an open wireless network initiative. Because the summit itself was held at Google&#x27;s premises, where taking photographs was forbidden, photos are mostly from the trip there itself and area around the buildings. There are some <a href="http://gsoc-wiki.osuosl.org/index.php/Photos_2011">photos by others</a> available.</p>
-<p>Summit really satisfied all expectations. We have experienced how it is at Google&#x27;s, meet many new people, sessions were great, presenting a lot of interesting issues within open-source deployment and IT in general, and giving some ideas how to solve them. We meet many other researchers doing open-source science and developing different programs, libraries and having similar problems. 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+<p>Summit really satisfied all expectations. We have experienced how it is at Google&#x27;s, meet many new people, sessions were great, presenting a lot of interesting issues within open-source deployment and IT in general, and giving some ideas how to solve them. We meet many other researchers doing open-source science and developing different programs, libraries and having similar problems. 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Because the summit itself was held at Google's premises, where taking photographs was forbidden, photos are mostly from the trip there itself and area around the buildings. There are some \", _jsx(_components.a, {\n        href: \"http://gsoc-wiki.osuosl.org/index.php/Photos_2011\",\n        children: \"photos by others\"\n      }), \" available.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Summit really satisfied all expectations. We have experienced how it is at Google's, meet many new people, sessions were great, presenting a lot of interesting issues within open-source deployment and IT in general, and giving some ideas how to solve them. We meet many other researchers doing open-source science and developing different programs, libraries and having similar problems. We have discussed ways of solving them: how to maintain libraries we all use, how to make our projects survive, once research is completed or funding ends and we move to some other research, etc. Cooperation is the key here, but there is often not much time to do that, as it requires extra time and energy, often not a part of research projects.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"gsoc-mentor-summit"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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@@ -1,4 +1,4 @@
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - GSoC Review: MF - Matrix Factorization Techniques for Data Mining</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - GSoC Review: MF - Matrix Factorization Techniques for Data Mining"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-09-01 23:48:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="matrixfactorization"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -251,4 +251,4 @@ <h3>Relevant Links</h3>
 <li><a href="http://orange.biolab.si/trac/wiki/MatrixFactorization">Orange wiki MF Project page</a></li>
 <li><a href="https://github.com/marinkaz/mf">Github repository with source code</a></li>
 <li><a href="http://helikoid.si/mf/GSoC_MF.pdf">Short presentation in pdf format of MF library</a></li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-01 23:48:00+00:00","draft":false,"title":"GSoC Review: MF - Matrix Factorization Techniques for Data Mining","blog":["gsoc","matrixfactorization"],"oldUrl":"/blog/2011/09/01/gsoc-review-mf-matrix-factorization-techniques-for-data-mining/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    h4: \"h4\",\n    ul: \"ul\",\n    li: \"li\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"MF - Matrix Factorization Techniques for Data Mining is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface to matrix factorization algorithms and methods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The MF works with numpy dense matrices and scipy sparse matrices (where this is possible to save on space). The library has support for multiple runs of the algorithms which can be used for some quality measures. By setting runtime specific options tracking the residuals error within one (or more) run or tracking fitted factorization model is possible. Extensive documentation with working examples which demonstrate real applications, commonly used benchmark data and visualization methods are provided to help with the interpretation and comprehension of the results.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Content of Current Release\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Factorization Methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"BD - Bayesian nonnegative matrix factorization Gibbs sampler [Schmidt2009]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"BMF - Binary matrix factorization [Zhang2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"ICM - Iterated conditional modes nonnegative matrix factorization [Schmidt2009]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"LFNMF - Fisher nonnegative matrix factorization for learning local features [Wang2004], [Li2001]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"LSNMF - Alternative nonnegative least squares matrix factorization using projected gradient method for subproblems [Lin2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"NMF - Standard nonnegative matrix factorization with Euclidean / Kullback-Leibler update equations and Frobenius / divergence / connectivity cost functions [Lee2001], [Brunet2004]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"NSNMF - Nonsmooth nonnegative matrix factorization [Montano2006]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"PMF - Probabilistic nonnegative matrix factorization [Laurberg2008], [Hansen2008]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"PSMF - Probabilistic sparse matrix factorization [Dueck2005], [Dueck2004], [Srebro2001], [Li2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"SNMF - Sparse nonnegative matrix factorization based on alternating nonnegativity constrained least squares [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"SNMNMF - Sparse network regularized multiple nonnegative matrix factorization [Zhang2011]\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Initialization Methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Random\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Fixed\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"NNDSVD [Boutsidis2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Random C [Albright2006]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Random VCol [Albright2006]\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Quality and Performance Measures\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distance\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residuals\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Connectivity matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Consensus matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Entropy of the fitted NMF model [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Dominant basis components computation\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Explained variance\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Feature score computation representing its specificity to basis vectors [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Computation of most basis specific features for basis vectors [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Purity [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residual sum of squares - can be used for rank estimate [Hutchins2008], [Frigyesi2008]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Sparseness [Hoyer2004]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Cophenetic correlation coefficient of consensus matrix - can be used for rank estimate [Brunet2004]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Dispersion [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Factorization rank estimation\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected matrix factorization method specific\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Plans for Future\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"General plan for future releases of MF library is to alleviate the usage for non-technical users, increase library stability and provide comprehensive visualization methods. 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Construct sparse matrix in CSR format.\"\n        }), \"\\nV = csr_matrix((array([\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"6\"\n        }), \"]),array([\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \"]),array([\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"6\"\n        }), \"])),shape=(\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \"))\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Run Standard NMF rank 4 algorithm\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Returned object is fitted factorization model.\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Through it user can access quality and performance measures.\"\n        }), \"\\nfit = mf.mf(V,method = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"nmf\\\"\"\n        }), \",max_iter = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"30\"\n        }), \",rank = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4\"\n        }), \",update = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'divergence'\"\n        }), \",objective = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'div'\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Basis matrix. It is sparse, as input V was sparse as well.\"\n        }), \"\\nW = fit.basis()\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Basis matrix\\\\n\\\"\"\n        }), \", W.todense()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Mixture matrix. We print this tiny matrix in dense format.\"\n        }), \"\\nH = fit.coef()\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Coef\\\\n\\\"\"\n        }), \", H.todense()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Return the loss function according to Kullback-Leibler divergence.\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Distance Kullback-Leibler\\\"\"\n        }), \", fit.distance(metric = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"kl\\\"\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Compute generic set of measures to evaluate the quality of the factorization\"\n        }), \"\\nsm = fit.summary()\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Print sparseness (Hoyer, 2004) of basis and mixture matrix\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Sparseness W: %5.3f  H: %5.3f\\\"\"\n        }), \" % (sm[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'sparseness'\"\n        }), \"][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], sm[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'sparseness'\"\n        }), \"][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Print actual number of iterations performed\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Iterations\\\"\"\n        }), \", sm[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'n_iter'\"\n        }), \"]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Print estimate of target matrix V\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Estimate\\\\n\\\"\"\n        }), \", dot(W.todense(), H.todense())\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Examples with visualized results in bioinformatics, image processing, text analysis, recommendation systems are provided in \", _jsx(_components.a, {\n        href: \"http://helikoid.si/mf/\",\n        children: \"Examples section of Documentation.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Figure 1: Reordered consensus matrix generated for rank = 2 on Leukemia data set.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-09-gsoc-review-mf-matrix-factorization-techniques-for-data-mining/__optimized-images__/all_aml_consensus2.png__160x160_q95_crop.png\",\n      alt: \"\",\n      width: \"160\",\n      height: \"160\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Figure 2: Interpretation of NMF - Divergence basis vectors on Medlars data set. By considering the highest weighted terms in this vector, we can assign a label or topic to basis vector W1, a user might attach the label liver to basis vector W1.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-09-gsoc-review-mf-matrix-factorization-techniques-for-data-mining/__optimized-images__/documents_basisw1.png__160x160_q95_crop.png\",\n      alt: \"\",\n      width: \"160\",\n      height: \"160\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Figure 3: Basis images of LSNMF obtained after 500 iterations on original face images. The bases trained by LSNMF are additive but not spatially localized for representation of faces.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-09-gsoc-review-mf-matrix-factorization-techniques-for-data-mining/__optimized-images__/orl_faces_500_iters_large_lsnmf.png__160x160_q95_crop.png\",\n      alt: \"\",\n      width: \"160\",\n      height: \"160\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Relevant Links\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://helikoid.si/mf/\",\n          children: \"Extensive published documentation with examples\"\n        })\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://orange.biolab.si/trac/wiki/MatrixFactorization\",\n          children: \"Orange wiki MF Project page\"\n        })\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"https://github.com/marinkaz/mf\",\n          children: \"Github repository with source code\"\n        })\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://helikoid.si/mf/GSoC_MF.pdf\",\n          children: \"Short presentation in pdf format of MF library\"\n        })\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"gsoc-review-mf-matrix-factorization-techniques-for-data-mining"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-01 23:48:00+00:00","draft":false,"title":"GSoC Review: MF - Matrix Factorization Techniques for Data Mining","blog":["gsoc","matrixfactorization"],"oldUrl":"/blog/2011/09/01/gsoc-review-mf-matrix-factorization-techniques-for-data-mining/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    h4: \"h4\",\n    ul: \"ul\",\n    li: \"li\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"MF - Matrix Factorization Techniques for Data Mining is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface to matrix factorization algorithms and methods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The MF works with numpy dense matrices and scipy sparse matrices (where this is possible to save on space). The library has support for multiple runs of the algorithms which can be used for some quality measures. By setting runtime specific options tracking the residuals error within one (or more) run or tracking fitted factorization model is possible. Extensive documentation with working examples which demonstrate real applications, commonly used benchmark data and visualization methods are provided to help with the interpretation and comprehension of the results.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Content of Current Release\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Factorization Methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"BD - Bayesian nonnegative matrix factorization Gibbs sampler [Schmidt2009]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"BMF - Binary matrix factorization [Zhang2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"ICM - Iterated conditional modes nonnegative matrix factorization [Schmidt2009]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"LFNMF - Fisher nonnegative matrix factorization for learning local features [Wang2004], [Li2001]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"LSNMF - Alternative nonnegative least squares matrix factorization using projected gradient method for subproblems [Lin2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"NMF - Standard nonnegative matrix factorization with Euclidean / Kullback-Leibler update equations and Frobenius / divergence / connectivity cost functions [Lee2001], [Brunet2004]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"NSNMF - Nonsmooth nonnegative matrix factorization [Montano2006]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"PMF - Probabilistic nonnegative matrix factorization [Laurberg2008], [Hansen2008]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"PSMF - Probabilistic sparse matrix factorization [Dueck2005], [Dueck2004], [Srebro2001], [Li2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"SNMF - Sparse nonnegative matrix factorization based on alternating nonnegativity constrained least squares [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"SNMNMF - Sparse network regularized multiple nonnegative matrix factorization [Zhang2011]\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Initialization Methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Random\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Fixed\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"NNDSVD [Boutsidis2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Random C [Albright2006]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Random VCol [Albright2006]\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Quality and Performance Measures\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distance\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residuals\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Connectivity matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Consensus matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Entropy of the fitted NMF model [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Dominant basis components computation\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Explained variance\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Feature score computation representing its specificity to basis vectors [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Computation of most basis specific features for basis vectors [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Purity [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residual sum of squares - can be used for rank estimate [Hutchins2008], [Frigyesi2008]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Sparseness [Hoyer2004]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Cophenetic correlation coefficient of consensus matrix - can be used for rank estimate [Brunet2004]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Dispersion [Park2007]\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Factorization rank estimation\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected matrix factorization method specific\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Plans for Future\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"General plan for future releases of MF library is to alleviate the usage for non-technical users, increase library stability and provide comprehensive visualization methods. Specifically, in algorithm sense addition of the following could be provided.\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Extending Bayesian methods with variational BD and linearly constrained BD.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Adaptation of the PMF model to interval-valued matrices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Nonnegative matrix approximation. Multiplicative iterative schema.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Usage\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Import MF library entry point for factorization\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" mf\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" scipy.sparse \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" csr_matrix\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" scipy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" array\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" numpy \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" dot\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# We will try to factorize sparse matrix. Construct sparse matrix in CSR format.\"\n        }), \"\\nV = csr_matrix((array([\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"6\"\n        }), \"]),array([\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \"]),array([\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"6\"\n        }), \"])),shape=(\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \",\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \"))\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Run Standard NMF rank 4 algorithm\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Returned object is fitted factorization model.\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Through it user can access quality and performance measures.\"\n        }), \"\\nfit = mf.mf(V,method = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"nmf\\\"\"\n        }), \",max_iter = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"30\"\n        }), \",rank = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4\"\n        }), \",update = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'divergence'\"\n        }), \",objective = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'div'\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Basis matrix. It is sparse, as input V was sparse as well.\"\n        }), \"\\nW = fit.basis()\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Basis matrix\\\\n\\\"\"\n        }), \", W.todense()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Mixture matrix. We print this tiny matrix in dense format.\"\n        }), \"\\nH = fit.coef()\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Coef\\\\n\\\"\"\n        }), \", H.todense()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Return the loss function according to Kullback-Leibler divergence.\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Distance Kullback-Leibler\\\"\"\n        }), \", fit.distance(metric = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"kl\\\"\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Compute generic set of measures to evaluate the quality of the factorization\"\n        }), \"\\nsm = fit.summary()\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Print sparseness (Hoyer, 2004) of basis and mixture matrix\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Sparseness W: %5.3f  H: %5.3f\\\"\"\n        }), \" % (sm[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'sparseness'\"\n        }), \"][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], sm[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'sparseness'\"\n        }), \"][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Print actual number of iterations performed\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Iterations\\\"\"\n        }), \", sm[\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'n_iter'\"\n        }), \"]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Print estimate of target matrix V\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Estimate\\\\n\\\"\"\n        }), \", dot(W.todense(), H.todense())\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Examples with visualized results in bioinformatics, image processing, text analysis, recommendation systems are provided in \", _jsx(_components.a, {\n        href: \"http://helikoid.si/mf/\",\n        children: \"Examples section of Documentation.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Figure 1: Reordered consensus matrix generated for rank = 2 on Leukemia data set.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-09-gsoc-review-mf-matrix-factorization-techniques-for-data-mining/__optimized-images__/all_aml_consensus2.png__160x160_q95_crop.png\",\n      alt: \"\",\n      width: \"160\",\n      height: \"160\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Figure 2: Interpretation of NMF - Divergence basis vectors on Medlars data set. By considering the highest weighted terms in this vector, we can assign a label or topic to basis vector W1, a user might attach the label liver to basis vector W1.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-09-gsoc-review-mf-matrix-factorization-techniques-for-data-mining/__optimized-images__/documents_basisw1.png__160x160_q95_crop.png\",\n      alt: \"\",\n      width: \"160\",\n      height: \"160\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Figure 3: Basis images of LSNMF obtained after 500 iterations on original face images. The bases trained by LSNMF are additive but not spatially localized for representation of faces.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2011-09-gsoc-review-mf-matrix-factorization-techniques-for-data-mining/__optimized-images__/orl_faces_500_iters_large_lsnmf.png__160x160_q95_crop.png\",\n      alt: \"\",\n      width: \"160\",\n      height: \"160\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Relevant Links\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://helikoid.si/mf/\",\n          children: \"Extensive published documentation with examples\"\n        })\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://orange.biolab.si/trac/wiki/MatrixFactorization\",\n          children: \"Orange wiki MF Project page\"\n        })\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"https://github.com/marinkaz/mf\",\n          children: \"Github repository with source code\"\n        })\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://helikoid.si/mf/GSoC_MF.pdf\",\n          children: \"Short presentation in pdf format of MF library\"\n        })\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"gsoc-review-mf-matrix-factorization-techniques-for-data-mining"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - GSoC Review: Multi-label Classification Implementation</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - GSoC Review: Multi-label Classification Implementation"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-09-02 05:47:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="multilabel"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, gsoc, multilabel</strong></p><h1 class="sc-c67a4900-0 loxVcf">GSoC Review: Multi-label Classification Implementation</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 02, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label <strong>l</strong> from a set of disjoint labels <strong>L</strong>, <strong>|L|</strong> &gt; <strong>1</strong>. If <strong>|L|</strong> = <strong>2</strong>, then the learning problem is called a binary classification problem, while if <strong>|L|</strong> &gt; <strong>2</strong>, then it is called a multi-class classification problem (Tsoumakas &amp; Katakis, 2007).</p>
 <p>Multi-label classification methods are increasingly used by many applications, such as textual data classification, protein function classification, music categorization and semantic scene classification. However, currently, Orange can only handle single-label problems. As a result, the project <em>Multi-label classification Implementation</em> has been proposed to extend Orange to support multi-label.</p>
 <p>We can group the existing methods for multi-label classification into two main categories: a) problem transformation method, and b) algorithm adaptation methods. In the former one, multi-label problems are converted to single-label, and then the traditional binary classification can apply; in the latter case, methods directly classify the multi-label data, instead.</p>
@@ -251,4 +251,4 @@ <h3>References</h3>
 <li>M. Zhang and Z. Zhou. <em>ML-KNN: A lazy learning approach to multi-label learning</em>. Pattern Recognition, 40, 7 (Jul. 2007), 2038-2048.</li>
 <li>S. Godbole and S. Sarawagi. <em>Discriminative Methods for Multi-labeled Classification</em>, Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004.</li>
 <li>R. E. Schapire and Y. Singer. <em>Boostexter: a bossting-based system for text categorization</em>, Machine Learning, vol.39, no.2/3, 2000, pp:135-68.</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-02 05:47:00+00:00","draft":false,"title":"GSoC Review: Multi-label Classification Implementation","blog":["classification","gsoc","multilabel"],"oldUrl":"/blog/2011/09/02/gsoc-review-multi-label-classification-implementation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    a: \"a\",\n    h3: \"h3\",\n    h4: \"h4\",\n    ul: \"ul\",\n    li: \"li\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label \", _jsx(_components.strong, {\n        children: \"l\"\n      }), \" from a set of disjoint labels \", _jsx(_components.strong, {\n        children: \"L\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"|L|\"\n      }), \" \u003e \", _jsx(_components.strong, {\n        children: \"1\"\n      }), \". 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The code is available in \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/trac/intertrac/browser%3Abranches/multilabel\",\n        children: \"SVN branch\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fortunately, benefiting from the Tab file format, the \", _jsx(_components.strong, {\n        children: \"ExampleTable\"\n      }), \" can store multi-label data without any modification. Now, we can add a special value – \", _jsx(_components.strong, {\n        children: \"label\"\n      }), \" into the \", _jsx(_components.strong, {\n        children: \"attributes\"\n      }), \" dictionary of the domain with value 1. 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The only difference between them is the different types of input data.\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Example for Classification\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"emotions.tab\\\"\"\n        }), \")\\n\\nclassifier = Orange.multilabel.BinaryRelevanceLearner(data)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = classifier(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\\npowerset_cliassifer = Orange.multilabel.LabelPowersetLearner(data)\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = powerset_cliassifer(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\\nmlknn_cliassifer = Orange.multilabel.MLkNNLearner(data,k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = mlknn_cliassifer(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\\nbr_cliassifer = Orange.multilabel.BRkNNLearner(data,k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = br_cliassifer(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Example for Evaluation\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\nlearners = [\\n    Orange.multilabel.BinaryRelevanceLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"br\\\"\"\n        }), \"),\\n    Orange.multilabel.BinaryRelevanceLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"br\\\"\"\n        }), \", \\\\\\n        base_learner=Orange.classification.knn.kNNLearner),\\n    Orange.multilabel.LabelPowersetLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lp\\\"\"\n        }), \"),\\n    Orange.multilabel.LabelPowersetLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lp\\\"\"\n        }), \", \\\\\\n        base_learner=Orange.classification.knn.kNNLearner),\\n    Orange.multilabel.MLkNNLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"mlknn\\\"\"\n        }), \",k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \"),\\n    Orange.multilabel.BRkNNLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"brknn\\\"\"\n        }), \",k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \"),\\n]\\n\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"emotions.xml\\\"\"\n        }), \")\\n\\nres = Orange.evaluation.testing.cross_validation(learners, data,\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \")\\nloss = Orange.evaluation.scoring.mlc_hamming_loss(res)\\naccuracy = Orange.evaluation.scoring.mlc_accuracy(res)\\nprecision = Orange.evaluation.scoring.mlc_precision(res)\\nrecall = Orange.evaluation.scoring.mlc_recall(res)\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'loss='\"\n        }), \", loss\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'accuracy='\"\n        }), \", accuracy\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'precision='\"\n        }), \", precision\\n\", _jsx(_components.span, {\n        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Tsoumakas and I. Katakis. \", _jsx(_components.em, {\n          children: \"Multi-label classification: An overview\\\". International Journal of Data Warehousing and Mining, 3(3):1-13, 2007.\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"E. Spyromitros, G. Tsoumakas, and I. Vlahavas, \", _jsx(_components.em, {\n          children: \"An Empirical Study of Lazy Multilabel Classification Algorithms\"\n        }), \". Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008), Springer, Syros, Greece, 2008.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"M. Zhang and Z. Zhou. \", _jsx(_components.em, {\n          children: \"ML-KNN: A lazy learning approach to multi-label learning\"\n        }), \". Pattern Recognition, 40, 7 (Jul. 2007), 2038-2048.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"S. Godbole and S. Sarawagi. \", _jsx(_components.em, {\n          children: \"Discriminative Methods for Multi-labeled Classification\"\n        }), \", Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"R. E. Schapire and Y. Singer. \", _jsx(_components.em, {\n          children: \"Boostexter: a bossting-based system for text categorization\"\n        }), \", Machine Learning, vol.39, no.2/3, 2000, pp:135-68.\"]\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"gsoc-review-multi-label-classification-implementation"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-02 05:47:00+00:00","draft":false,"title":"GSoC Review: Multi-label Classification Implementation","blog":["classification","gsoc","multilabel"],"oldUrl":"/blog/2011/09/02/gsoc-review-multi-label-classification-implementation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    a: \"a\",\n    h3: \"h3\",\n    h4: \"h4\",\n    ul: \"ul\",\n    li: \"li\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label \", _jsx(_components.strong, {\n        children: \"l\"\n      }), \" from a set of disjoint labels \", _jsx(_components.strong, {\n        children: \"L\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"|L|\"\n      }), \" \u003e \", _jsx(_components.strong, {\n        children: \"1\"\n      }), \". 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As a result, the project \", _jsx(_components.em, {\n        children: \"Multi-label classification Implementation\"\n      }), \" has been proposed to extend Orange to support multi-label.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can group the existing methods for multi-label classification into two main categories: a) problem transformation method, and b) algorithm adaptation methods. In the former one, multi-label problems are converted to single-label, and then the traditional binary classification can apply; in the latter case, methods directly classify the multi-label data, instead.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In this project, two transformation methods and two algorithm adaptation methods are implemented. Along with the methods, their widgets are also added. As the evaluation metrics for multi-label data are different from the single-label ones, new evaluation measures are supported. The code is available in \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/trac/intertrac/browser%3Abranches/multilabel\",\n        children: \"SVN branch\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fortunately, benefiting from the Tab file format, the \", _jsx(_components.strong, {\n        children: \"ExampleTable\"\n      }), \" can store multi-label data without any modification. Now, we can add a special value – \", _jsx(_components.strong, {\n        children: \"label\"\n      }), \" into the \", _jsx(_components.strong, {\n        children: \"attributes\"\n      }), \" dictionary of the domain with value 1. In this way, if the attribute description has the keyword \", _jsx(_components.strong, {\n        children: \"label\"\n      }), \", then it is a label; otherwise, it is a normal feature.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"What have been done in this project\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Transformation methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"br – Binary Relevance Learner (Tsoumakas \u0026 Katakis, 2007)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"lp – Label Powerset Classification (Tsoumakas \u0026 Katakis, 2007)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Algorithm Adaptation methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"mlknn – Multi-kNN Classification (Zhang \u0026 Zhou, 2007)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"brknn – BR-kNN Classification (Spyromitros et al. 2008)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Evaluation methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"mlc_hamming_loss – Example-based Hamming Loss (Schapire and Singer 2000)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"mlc_accuracy, mlc_precision, mlc_recall – Example-based accuracy, precision, recall (Godbole \u0026 Sarawagi, 2004)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Widgets\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"OWBR – Widget for Binary Relevance Learner\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"OWLP – Widget for Label Powerset Classification\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"OWMLkNN – Widget for Multi-kNN Classification\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"OWBRkNN – Widget for BR-kNN Classification\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"OWTestLearner – Widget for Evaluation\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"File Format Extension\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"extend the loadARFF function to support sparse Weka format\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"new support \", _jsx(_components.a, {\n          href: \"http://mulan.sourceforge.net/format.html\",\n          children: \"mulan xml and arff format\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Plan for the future\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"add more classification methods for multi-label, such as PT1 to PT6\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"add feature extraction method\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"add ranking-based evaluation methods\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"How to use\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Basically, the way to use multi-label classification and evaluation is nearly the same as the single-label ones. The only difference between them is the different types of input data.\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Example for Classification\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"emotions.tab\\\"\"\n        }), \")\\n\\nclassifier = Orange.multilabel.BinaryRelevanceLearner(data)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = classifier(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\\npowerset_cliassifer = Orange.multilabel.LabelPowersetLearner(data)\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = powerset_cliassifer(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\\nmlknn_cliassifer = Orange.multilabel.MLkNNLearner(data,k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = mlknn_cliassifer(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\\nbr_cliassifer = Orange.multilabel.BRkNNLearner(data,k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \")\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" e \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" data:\\n    c,p = br_cliassifer(e,Orange.classification.Classifier.GetBoth)\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" c,p\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Example for Evaluation\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\\nlearners = [\\n    Orange.multilabel.BinaryRelevanceLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"br\\\"\"\n        }), \"),\\n    Orange.multilabel.BinaryRelevanceLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"br\\\"\"\n        }), \", \\\\\\n        base_learner=Orange.classification.knn.kNNLearner),\\n    Orange.multilabel.LabelPowersetLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lp\\\"\"\n        }), \"),\\n    Orange.multilabel.LabelPowersetLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"lp\\\"\"\n        }), \", \\\\\\n        base_learner=Orange.classification.knn.kNNLearner),\\n    Orange.multilabel.MLkNNLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"mlknn\\\"\"\n        }), \",k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \"),\\n    Orange.multilabel.BRkNNLearner(name=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"brknn\\\"\"\n        }), \",k=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"5\"\n        }), \"),\\n]\\n\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"emotions.xml\\\"\"\n        }), \")\\n\\nres = Orange.evaluation.testing.cross_validation(learners, data,\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \")\\nloss = Orange.evaluation.scoring.mlc_hamming_loss(res)\\naccuracy = Orange.evaluation.scoring.mlc_accuracy(res)\\nprecision = Orange.evaluation.scoring.mlc_precision(res)\\nrecall = Orange.evaluation.scoring.mlc_recall(res)\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'loss='\"\n        }), \", loss\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'accuracy='\"\n        }), \", accuracy\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'precision='\"\n        }), \", precision\\n\", _jsx(_components.span, {\n        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Tsoumakas and I. Katakis. \", _jsx(_components.em, {\n          children: \"Multi-label classification: An overview\\\". International Journal of Data Warehousing and Mining, 3(3):1-13, 2007.\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"E. Spyromitros, G. Tsoumakas, and I. Vlahavas, \", _jsx(_components.em, {\n          children: \"An Empirical Study of Lazy Multilabel Classification Algorithms\"\n        }), \". Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008), Springer, Syros, Greece, 2008.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"M. Zhang and Z. Zhou. \", _jsx(_components.em, {\n          children: \"ML-KNN: A lazy learning approach to multi-label learning\"\n        }), \". Pattern Recognition, 40, 7 (Jul. 2007), 2038-2048.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"S. Godbole and S. Sarawagi. \", _jsx(_components.em, {\n          children: \"Discriminative Methods for Multi-labeled Classification\"\n        }), \", Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"R. E. Schapire and Y. Singer. \", _jsx(_components.em, {\n          children: \"Boostexter: a bossting-based system for text categorization\"\n        }), \", Machine Learning, vol.39, no.2/3, 2000, pp:135-68.\"]\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"gsoc-review-multi-label-classification-implementation"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, plot, qt, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">GSoC Review: Visualizations with Qt</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 03, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>During the course of this summer, I created a new plotting library for Orange plot, replacing the use of PyQwt. I can say that I have succesfully completed my project, but the library (and especially the visualization widgets) could still use some more work. The new library supports a similar interface, so little change is needed to convert individual widgets, but it also has several advantages over the old implementation:</p>
 <ul>
 <li>Animations: When using a single curve to show all data points, data changes only move the points instead of replacing them. These moves are now animated, as are color and size changes.</li>
@@ -165,4 +165,4 @@
 <li>Axis labels: With a large number of long axis labels, the formatting gets rather ugly. This is a minor inconvenience, but it does make the plots look unprofessional.</li>
 </ul>
 <p>Fortunately, I have little school obligations this september, so I think I will be able to work on Orange some more, at least until school starts. I have already added gesture support and some minor improvements since the end of the program.</p>
-<p>Finally, I&#x27;d like to take this opportunity to thank the Orange team, especially my mentor Miha, for accepting me and helping me throughout the summer. It&#x27;s been an interesting project, and I&#x27;ll be happy to continue working with the same software and the same team.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-03 08:28:00+00:00","draft":false,"title":"GSoC Review: Visualizations with Qt","blog":["gsoc","plot","qt","visualization"],"oldUrl":"/blog/2011/09/03/gsoc-review-visualizations-with-qt/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"During the course of this summer, I created a new plotting library for Orange plot, replacing the use of PyQwt. 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+<p>Finally, I&#x27;d like to take this opportunity to thank the Orange team, especially my mentor Miha, for accepting me and helping me throughout the summer. It&#x27;s been an interesting project, and I&#x27;ll be happy to continue working with the same software and the same team.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-09-03 08:28:00+00:00","draft":false,"title":"GSoC Review: Visualizations with Qt","blog":["gsoc","plot","qt","visualization"],"oldUrl":"/blog/2011/09/03/gsoc-review-visualizations-with-qt/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"During the course of this summer, I created a new plotting library for Orange plot, replacing the use of PyQwt. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics</strong></p><h1 class="sc-c67a4900-0 loxVcf">Hands-on Orange at Functional Genomics Workshop</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 24, 2014</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Last week we have co-organized a <a href="http://biolab.github.io/functional-genomics-workshop/">Functional Genomics Workshop</a>. At University of Ljubljana we have hosted an inspiring pack of scientists from the <a href="http://tdccbr.med.utoronto.ca/">Donnelly Centre for Cellular and Biomolecular Research</a> from Toronto. Part of the event was a hands-on workshop Data mining without programing, where we have used Orange to analyze data from systems biology. Data included a subset of Charlie Boone&#x27;s famous yeast interaction data and data from chemical genomics. For the program, info about the speakers, and panckages and šmorn check out <a href="/blog/2014-10-hands-on-orange-at-functional-genomics-workshop/fg-times.pdf">workshop&#x27;s newspaper</a>.</p>
 <p>It is always a pleasure seeing a packed lecture room with all laptops running Orange. Attendees were assisted by members of the Biolab in Ljubljana. Hands-on program followed <a href="/blog/2014-10-hands-on-orange-at-functional-genomics-workshop/fungen-workshop-notes.pdf">a set of short lectures</a> we have crafted for intended audience – biologists. Everything ran smoothly. At the end, we got excited enough to promise a data import wizard for all those that have problems annotating the data with feature type tags. The deadline: two weeks from the end of the workshop.</p>
-<a href="/blog/2014-10-hands-on-orange-at-functional-genomics-workshop/__optimized-images__/fg-orange.jpg" data-gallery="true"><img alt="" loading="lazy" width="1280" height="848" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2014-10-hands-on-orange-at-functional-genomics-workshop/__optimized-images__/fg-orange.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2014-10-24 13:03:02+00:00","draft":false,"title":"Hands-on Orange at Functional Genomics Workshop","blog":["bioinformatics"],"oldUrl":"/blog/2014/10/24/hands-on-orange-at-functional-genomics-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week we have co-organized a \", _jsx(_components.a, {\n        href: \"http://biolab.github.io/functional-genomics-workshop/\",\n        children: \"Functional Genomics Workshop\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, teaching, machine learning</strong></p><h1 class="sc-c67a4900-0 loxVcf">Hands-On Training About Overfitting</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 05, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="https://journals.plos.org/ploscompbiol/">PLOS Computation Biology</a> has just published our paper on training about overfitting:</p>
 <ul>
 <li>Demšar J, Zupan B (2021) <a href="https://doi.org/10.1371/journal.pcbi.1008671">Hands-on training about overfitting</a>. PLoS Comput Biol 17(3): e1008671.</li>
@@ -164,4 +164,4 @@
 <ul>
 <li>Stražar M, Žagar L, Kokošar J, Tanko V, Erjavec A, Poličar P, Starič A, Demšar J, Shaulsky G, Menon V, Lamire A, Parikh A, and Zupan B (2019) <a href="https://doi.org/10.1093/bioinformatics/btz348">scOrange – A Tool for Hands-On Training of Concepts from Single Cell Data Analytics</a>, Bioinformatics 35(14):i4-i12.</li>
 <li>Godec P, Pančur M, Ilenič N, Čopar A, Stražar M, Erjavec A, Pretnar A, Demšar J, Starič A, Toplak M, Žagar L, Hartman J, Wang H, Bellazzi R, Petrovič U, Garagna S, Zuccotti M, Park D, Shaulsky G, Zupan B (2019) <a href="https://www.nature.com/articles/s41467-019-12397-x">Democratized image analytics by visual programming through integration of deep models and small-scale machine learning</a>, Nature Communications 10(1):4551.</li>
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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2021-03-05","draft":false,"title":"Hands-On Training About Overfitting","thumbImage":"2021-01-11-orange-in-education-small.png","frontPageImage":"2021-03-05-overfitting-tree-small.png","blog":["education","teaching","machine learning"],"shortExcerpt":"We have designed a course on overfitting.","longExcerpt":"PLOS Computation Biology has just published our paper on training about overfitting.","x2images":false,"oldUrl":"/blog/2021/2021-03-05-overfitting-course/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://journals.plos.org/ploscompbiol/\",\n        children: \"PLOS Computation Biology\"\n      }), \" has just published our paper on training about overfitting:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Demšar J, Zupan B (2021) \", _jsx(_components.a, {\n          href: \"https://doi.org/10.1371/journal.pcbi.1008671\",\n          children: \"Hands-on training about overfitting\"\n        }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, education, plot</strong></p><h1 class="sc-c67a4900-0 loxVcf">Hierarchical Clustering: A Simple Explanation</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 02, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>One of the key techniques of exploratory data mining is clustering – separating instances into distinct groups based on some measure of similarity. We can estimate the similarity between two data instances through euclidean (pythagorean), manhattan (sum of absolute differences between coordinates) and mahalanobis distance (distance from the mean by standard deviation), or, say, through Pearson correlation or Spearman correlation.</p>
 <p>Our main goal when clustering data is to get groups of data instances where:</p>
 <ul>
@@ -189,4 +189,4 @@
 <ul>
 <li>pros: sums up the data, good for small data sets</li>
 <li>cons: computationally demanding, fails on larger sets</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-12-02 13:37:49+00:00","draft":false,"title":"Hierarchical Clustering: A Simple Explanation","blog":["clustering","education","plot"],"oldUrl":"/blog/2015/12/02/hierarchical-clustering-a-simple-explanation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One of the key techniques of exploratory data mining is clustering – separating instances into distinct groups based on some measure of similarity. We can estimate the similarity between two data instances through euclidean (pythagorean), manhattan (sum of absolute differences between coordinates) and mahalanobis distance (distance from the mean by standard deviation), or, say, through Pearson correlation or Spearman correlation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our main goal when clustering data is to get groups of data instances where:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"each group (Ci) is a a subset of the training data (U): Ci ⊂ U\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"an intersection of all the sets is an empty set: Ci ∩ Cj = 0\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"a union of all groups equals the train data: Ci ∪ Cj = U\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This would be ideal. But we rarely get the data, where separation is so clear. One of the easiest techniques to cluster the data is hierarchical clustering. First, we take an instance from, say, 2D plot. Now we want to find its nearest neighbor. Nearest neighbor of course depends on the measure of distance we choose, but let’s go with euclidean for now as it is the easiest to visualize.\\n\", _jsx(_components.img, {\n        src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/hier-clust-blog-compare1.jpg\",\n        alt: \"\",\n        width: \"1200\",\n        height: \"540\"\n      }), \"\\nFirst steps of hierarchical clustering.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Euclidean distance is calculated as:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"https://upload.wikimedia.org/wikipedia/commons/thumb/5/55/Euclidean_distance_2d.svg/220px-Euclidean_distance_2d.svg.png\",\n      alt: \"\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Naturally, the shorter the distance the more similar the two instances are. In the beginning, all instances are in their own particular clusters. Then we seek for the closest instances of every instance in the plot. We pin down the closest instance and make a cluster of the original and the closest instance. Now we repeat the process again. What is the closest instances to our new cluster –\u003e add it to the cluster –\u003e find the closest instance. We repeat this procedure until all the instances are grouped in one single cluster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can write this down also in a form of a pseudocode:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"\\tevery instance is in its own cluster\\n\\n\\trepeat until instances are all in one group:\\n\\n\\t    find the closest instances to the group (distance has to be minimum)\\n\\n\\t    join closest instances with the group\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/hier-clust-blog6.png\",\n      alt: \"\",\n      width: \"876\",\n      height: \"749\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualization of this procedure is called a dendrogram, which is what Hierarchical clustering widget displays in Orange.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/hier-clust-blog-compare21.jpg\",\n      alt: \"\",\n      width: \"1200\",\n      height: \"360\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Single, complete and average linkage.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another thing to consider is the distance between instances when we have already two or more instances in a cluster. Do we go with the closest instance in a cluster or to the furthest one?\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Picture A shows the distances to the closest instance – single linkage.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Picture B shows the distance to the furthest instance – complete linkage.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Picture C shows the average of all distances in a cluster to the instance – average linkage.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/single-vs-complete.jpg\",\n        alt: \"\",\n        width: \"1200\",\n        height: \"480\"\n      }), \"\\nSingle vs complete linkage.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The downside of single linkage is, even by intuition, creating elongated, stretched clusters. Instances at the top part of the red C are in fact quite different from the lower part of the red C. Complete linkage does much better here as it centers clustering nicely. However, the downside of complete linkage is taking outliers too much into consideration. Naturally, each approach has its own pros and cons and it’s good to know how they work in order to use them correctly. One extra hint: single linkage works great for image recognition, exactly because it can follow the curve.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There’s a lot more we could say about hierarchical clustering, but to sum it up, let’s state pros and cons of this method:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"pros: sums up the data, good for small data sets\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"cons: computationally demanding, fails on larger sets\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"hierarchical-clustering-a-simple-explanation"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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We can estimate the similarity between two data instances through euclidean (pythagorean), manhattan (sum of absolute differences between coordinates) and mahalanobis distance (distance from the mean by standard deviation), or, say, through Pearson correlation or Spearman correlation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our main goal when clustering data is to get groups of data instances where:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"each group (Ci) is a a subset of the training data (U): Ci ⊂ U\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"an intersection of all the sets is an empty set: Ci ∩ Cj = 0\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"a union of all groups equals the train data: Ci ∪ Cj = U\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This would be ideal. But we rarely get the data, where separation is so clear. One of the easiest techniques to cluster the data is hierarchical clustering. First, we take an instance from, say, 2D plot. Now we want to find its nearest neighbor. Nearest neighbor of course depends on the measure of distance we choose, but let’s go with euclidean for now as it is the easiest to visualize.\\n\", _jsx(_components.img, {\n        src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/hier-clust-blog-compare1.jpg\",\n        alt: \"\",\n        width: \"1200\",\n        height: \"540\"\n      }), \"\\nFirst steps of hierarchical clustering.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Euclidean distance is calculated as:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"https://upload.wikimedia.org/wikipedia/commons/thumb/5/55/Euclidean_distance_2d.svg/220px-Euclidean_distance_2d.svg.png\",\n      alt: \"\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Naturally, the shorter the distance the more similar the two instances are. In the beginning, all instances are in their own particular clusters. Then we seek for the closest instances of every instance in the plot. We pin down the closest instance and make a cluster of the original and the closest instance. Now we repeat the process again. What is the closest instances to our new cluster –\u003e add it to the cluster –\u003e find the closest instance. We repeat this procedure until all the instances are grouped in one single cluster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can write this down also in a form of a pseudocode:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"\\tevery instance is in its own cluster\\n\\n\\trepeat until instances are all in one group:\\n\\n\\t    find the closest instances to the group (distance has to be minimum)\\n\\n\\t    join closest instances with the group\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/hier-clust-blog6.png\",\n      alt: \"\",\n      width: \"876\",\n      height: \"749\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualization of this procedure is called a dendrogram, which is what Hierarchical clustering widget displays in Orange.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/hier-clust-blog-compare21.jpg\",\n      alt: \"\",\n      width: \"1200\",\n      height: \"360\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Single, complete and average linkage.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another thing to consider is the distance between instances when we have already two or more instances in a cluster. Do we go with the closest instance in a cluster or to the furthest one?\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Picture A shows the distances to the closest instance – single linkage.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Picture B shows the distance to the furthest instance – complete linkage.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Picture C shows the average of all distances in a cluster to the instance – average linkage.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2015-12-hierarchical-clustering-a-simple-explanation/__optimized-images__/single-vs-complete.jpg\",\n        alt: \"\",\n        width: \"1200\",\n        height: \"480\"\n      }), \"\\nSingle vs complete linkage.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The downside of single linkage is, even by intuition, creating elongated, stretched clusters. Instances at the top part of the red C are in fact quite different from the lower part of the red C. Complete linkage does much better here as it centers clustering nicely. However, the downside of complete linkage is taking outliers too much into consideration. Naturally, each approach has its own pros and cons and it’s good to know how they work in order to use them correctly. One extra hint: single linkage works great for image recognition, exactly because it can follow the curve.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There’s a lot more we could say about hierarchical clustering, but to sum it up, let’s state pros and cons of this method:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"pros: sums up the data, good for small data sets\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"cons: computationally demanding, fails on larger sets\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"hierarchical-clustering-a-simple-explanation"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>correlations, NHTS, null hypothesis, p-value, statistics</strong></p><h1 class="sc-c67a4900-0 loxVcf">How to Abuse p-Values in Correlations</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 04, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In a parallel universe, not so far from ours, Orange’s Correlation widget looks like this.</p>
 <p>\</p>
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@@ -201,4 +201,4 @@
 <p>Imagine what they can do with the Correlations widget in the parallel universe! They compute correlations between all pairs, print out the first 5 % of them and start writing a paper without bothering to look at p-values at all. They know they should be statistically significant even if the data is random.</p>
 <p>Which is precisely the reason why our widget must not compute p-values: because people would use it for Texas sharpshooting. P-values make sense only in the context of the proper NHST procedure (still pretending for the sake of Christmas ceasefire). They cannot be computed using the data on which they were found.</p>
 <p>If so, why do we have the Correlation widget at all if it’s results are unpublishable? We can use it to find highly correlated pairs in a data sample. But we can’t just attach p-values to them and publish them. By finding these pairs (with assistance of Correlation widget) we just formulate hypotheses. This is only step 1 of the enshrined NHST procedure. We can’t skip the other two: the next step is to collect some new data (existing data won’t do!) and then use it to test the hypotheses (step 3).</p>
-<p>Following this procedure doesn’t save us from <a href="https://en.wikipedia.org/wiki/Data_dredging" target="_blank" rel="noreferrer">data dredging</a>. There are still plenty of ways to cheat. It is the most tempting to select the first 100 most correlated pairs (or, actually, any 100 pairs), (re)compute correlations on some new data and publish the top 5 % of these pairs. The official solution for this is a patchwork of various corrections for multiple hypotheses testing, but… Well, they don’t work, but we should say no more here. You know, Christmas ceasefire.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-01-04","draft":false,"title":"How to Abuse p-Values in Correlations","thumbImage":"correlations.png","image":"","blog":["correlations","NHTS","null hypothesis","p-value","statistics"],"shortExcerpt":"Why doesn't Orange show p-values for correlations coefficients? To save you from data dredging and Texas sharpshooter fallacy...","longExcerpt":"We have all attended Statistics 101, and we know that you can never trust correlation coefficients without looking at p-values to check that these correlations are real, right? So why on Earth doesn’t Orange show them?","oldUrl":"/blog/2019/1/4/how-to-abuse-p-values-in-correlations/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    br: \"br\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {Figure, LinkNew} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In a parallel universe, not so far from ours, Orange’s Correlation widget looks like this.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/correlations-p-values-1.png\",\n      src: \"40%\",\n      width: \"700\",\n      height: \"872\",\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/__optimized-images__/correlations-p-values-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Quite similar to ours, except that this one shows p-values instead of correlation coefficients. Which is actually better, isn’t it? I mean, we have all attended Statistics 101, and we know that you can never trust correlation coefficients without looking at p-values to check that these correlations are real, right? So why on Earth doesn’t Orange show them?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First a side note. It was Christmas not long ago. Let’s call a ceasefire on the frequentist vs. Bayesian war. Let us, for Christ’s sake, pretend, pardon, agree that null-hypothesis testing is not wrong per se.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The mantra of null-hypothesis significance testing goes like this:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Form hypothesis.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Collect data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Test hypothesis.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In contrast, the parallel-universe Correlation widget is (ab)used like this:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Collect data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Test all possible hypotheses.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Cherry pick those that are confirmed.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is like the \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Texas_sharpshooter_fallacy\",\n        name: \"Texas sharpshooter\"\n      }), \" who fires first and then draws targets around the shots. You should never formulate hypothesis based on some data and then use this same data to prove it. Because it usually (surprise!) works.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/TexasSharpShooter.png\",\n      src: \"50%\",\n      src: \"Illustration by Dirk-Jan Hoek (CC-BY).\",\n      width: \"357\",\n      height: \"300\",\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/__optimized-images__/TexasSharpShooter.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Back to the above snapshot. It shows correlations between 100 vegetables based on 100 different measurements (Ca and Mg content, their consumption in Finland, number of mentions in Star Trek DS9 series, likelihood of finding it on the Mars, and so forth). In other words, it’s all made it up. Just a 100×100 matrix of random numbers with column labels from the simple Wikipedia \", _jsx(LinkNew, {\n        url: \"https://simple.wikipedia.org/wiki/List_of_vegetables\",\n        name: \"list of vegetables\"\n      }), \". Yet the similarity between \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Mung_bean\",\n        name: \"mung bean\"\n      }), \" and \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Jerusalem_artichoke\",\n        name: \"sunchokes\"\n      }), \" surely cannot be dismissed (p \u003c 0.001). Those who like bell pepper should try cilantro, too, because it’s basically one and the same thing (p = 0.001). And I honestly can’t tell black bean from wasabi (p = 0.001).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are the p-values for the top 100 most correlated pairs.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"import numpy as np\\nimport scipy as sp\\na = np.random.random((100, 100))\\nsorted(stats.pearsonr(a[i], a[j])[1] for i in range(100) for j in range(i))[:100]\\n[0.0002774329730584203, 0.0004158786523819104, 0.0005008536192579852,\\n0.0007211022164265075, 0.0008268675086438253, 0.0010265740674904762,\\n(...91 values omitted to reduce the nonsense)\\n0.01844720610938738, 0.018465602922746942, 0.018662079618069056]\\nFirst 100 correlations are highly significant.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To learn a lesson we may have failed to grasp at the NHST 101 class, consider that there are 100 * 99 / 2 pairs. What is the significance of the pair at 5-th percentile?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"correlations = sorted(stats.pearsonr(a[i], a[j])[1] for i in range(100) for j in range(i))\\nnpairs = 100 * 99 / 2\\nprint(correlations[int(pairs * 0.05)]\\n0.0496868751692227\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Roughly 0.05. This is exactly what should have happened, because:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"correlations[int(npairs * 0.10)]\\n0.10004180592217532\\ncorrelations[int(npairs * 0.15)]\\n0.15236602574520097\\ncorrelations[int(npairs * 0.30)]\\n0.3026816170584785\\nThis proves only that p-values for the Pearson correlation coefficient are well calibrated (and that \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Mersenne_Twister\",\n        name: \"Mersenne twister\"\n      }), \" that is used to generate random numbers in numpy works well). In theory, the p-value for a certain statistics (like Pearson’s r) is the probability of getting such or even more extreme value if the null-hypothesis (of no correlation, in our case) is actually true. 5 % of random hypotheses should have a p-value below 0.05, 10 % a value below 10, and 23 % a value below 23.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Imagine what they can do with the Correlations widget in the parallel universe! They compute correlations between all pairs, print out the first 5 % of them and start writing a paper without bothering to look at p-values at all. They know they should be statistically significant even if the data is random.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Which is precisely the reason why our widget must not compute p-values: because people would use it for Texas sharpshooting. P-values make sense only in the context of the proper NHST procedure (still pretending for the sake of Christmas ceasefire). They cannot be computed using the data on which they were found.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If so, why do we have the Correlation widget at all if it’s results are unpublishable? We can use it to find highly correlated pairs in a data sample. But we can’t just attach p-values to them and publish them. By finding these pairs (with assistance of Correlation widget) we just formulate hypotheses. This is only step 1 of the enshrined NHST procedure. We can’t skip the other two: the next step is to collect some new data (existing data won’t do!) and then use it to test the hypotheses (step 3).\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Following this procedure doesn’t save us from \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Data_dredging\",\n        name: \"data dredging\"\n      }), \". There are still plenty of ways to cheat. It is the most tempting to select the first 100 most correlated pairs (or, actually, any 100 pairs), (re)compute correlations on some new data and publish the top 5 % of these pairs. The official solution for this is a patchwork of various corrections for multiple hypotheses testing, but… Well, they don’t work, but we should say no more here. You know, Christmas ceasefire.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":450,"height":300,"src":"/blog/2019-1-how-to-abuse-p-values-in-correlations/__optimized-images__/correlations.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"how-to-abuse-p-values-in-correlations"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Following this procedure doesn’t save us from <a href="https://en.wikipedia.org/wiki/Data_dredging" target="_blank" rel="noreferrer">data dredging</a>. There are still plenty of ways to cheat. It is the most tempting to select the first 100 most correlated pairs (or, actually, any 100 pairs), (re)compute correlations on some new data and publish the top 5 % of these pairs. The official solution for this is a patchwork of various corrections for multiple hypotheses testing, but… Well, they don’t work, but we should say no more here. You know, Christmas ceasefire.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-01-04","draft":false,"title":"How to Abuse p-Values in Correlations","thumbImage":"correlations.png","image":"","blog":["correlations","NHTS","null hypothesis","p-value","statistics"],"shortExcerpt":"Why doesn't Orange show p-values for correlations coefficients? To save you from data dredging and Texas sharpshooter fallacy...","longExcerpt":"We have all attended Statistics 101, and we know that you can never trust correlation coefficients without looking at p-values to check that these correlations are real, right? So why on Earth doesn’t Orange show them?","oldUrl":"/blog/2019/1/4/how-to-abuse-p-values-in-correlations/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    br: \"br\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {Figure, LinkNew} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In a parallel universe, not so far from ours, Orange’s Correlation widget looks like this.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/correlations-p-values-1.png\",\n      src: \"40%\",\n      width: \"700\",\n      height: \"872\",\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/__optimized-images__/correlations-p-values-1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Quite similar to ours, except that this one shows p-values instead of correlation coefficients. Which is actually better, isn’t it? I mean, we have all attended Statistics 101, and we know that you can never trust correlation coefficients without looking at p-values to check that these correlations are real, right? So why on Earth doesn’t Orange show them?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First a side note. It was Christmas not long ago. Let’s call a ceasefire on the frequentist vs. Bayesian war. Let us, for Christ’s sake, pretend, pardon, agree that null-hypothesis testing is not wrong per se.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The mantra of null-hypothesis significance testing goes like this:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Form hypothesis.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Collect data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Test hypothesis.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In contrast, the parallel-universe Correlation widget is (ab)used like this:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Collect data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Test all possible hypotheses.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Cherry pick those that are confirmed.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is like the \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Texas_sharpshooter_fallacy\",\n        name: \"Texas sharpshooter\"\n      }), \" who fires first and then draws targets around the shots. You should never formulate hypothesis based on some data and then use this same data to prove it. Because it usually (surprise!) works.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/TexasSharpShooter.png\",\n      src: \"50%\",\n      src: \"Illustration by Dirk-Jan Hoek (CC-BY).\",\n      width: \"357\",\n      height: \"300\",\n      src: \"/blog/2019-1-how-to-abuse-p-values-in-correlations/__optimized-images__/TexasSharpShooter.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Back to the above snapshot. It shows correlations between 100 vegetables based on 100 different measurements (Ca and Mg content, their consumption in Finland, number of mentions in Star Trek DS9 series, likelihood of finding it on the Mars, and so forth). In other words, it’s all made it up. Just a 100×100 matrix of random numbers with column labels from the simple Wikipedia \", _jsx(LinkNew, {\n        url: \"https://simple.wikipedia.org/wiki/List_of_vegetables\",\n        name: \"list of vegetables\"\n      }), \". Yet the similarity between \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Mung_bean\",\n        name: \"mung bean\"\n      }), \" and \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Jerusalem_artichoke\",\n        name: \"sunchokes\"\n      }), \" surely cannot be dismissed (p \u003c 0.001). Those who like bell pepper should try cilantro, too, because it’s basically one and the same thing (p = 0.001). And I honestly can’t tell black bean from wasabi (p = 0.001).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are the p-values for the top 100 most correlated pairs.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"import numpy as np\\nimport scipy as sp\\na = np.random.random((100, 100))\\nsorted(stats.pearsonr(a[i], a[j])[1] for i in range(100) for j in range(i))[:100]\\n[0.0002774329730584203, 0.0004158786523819104, 0.0005008536192579852,\\n0.0007211022164265075, 0.0008268675086438253, 0.0010265740674904762,\\n(...91 values omitted to reduce the nonsense)\\n0.01844720610938738, 0.018465602922746942, 0.018662079618069056]\\nFirst 100 correlations are highly significant.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To learn a lesson we may have failed to grasp at the NHST 101 class, consider that there are 100 * 99 / 2 pairs. What is the significance of the pair at 5-th percentile?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"correlations = sorted(stats.pearsonr(a[i], a[j])[1] for i in range(100) for j in range(i))\\nnpairs = 100 * 99 / 2\\nprint(correlations[int(pairs * 0.05)]\\n0.0496868751692227\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Roughly 0.05. This is exactly what should have happened, because:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"correlations[int(npairs * 0.10)]\\n0.10004180592217532\\ncorrelations[int(npairs * 0.15)]\\n0.15236602574520097\\ncorrelations[int(npairs * 0.30)]\\n0.3026816170584785\\nThis proves only that p-values for the Pearson correlation coefficient are well calibrated (and that \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Mersenne_Twister\",\n        name: \"Mersenne twister\"\n      }), \" that is used to generate random numbers in numpy works well). In theory, the p-value for a certain statistics (like Pearson’s r) is the probability of getting such or even more extreme value if the null-hypothesis (of no correlation, in our case) is actually true. 5 % of random hypotheses should have a p-value below 0.05, 10 % a value below 10, and 23 % a value below 23.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Imagine what they can do with the Correlations widget in the parallel universe! They compute correlations between all pairs, print out the first 5 % of them and start writing a paper without bothering to look at p-values at all. They know they should be statistically significant even if the data is random.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Which is precisely the reason why our widget must not compute p-values: because people would use it for Texas sharpshooting. P-values make sense only in the context of the proper NHST procedure (still pretending for the sake of Christmas ceasefire). They cannot be computed using the data on which they were found.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If so, why do we have the Correlation widget at all if it’s results are unpublishable? We can use it to find highly correlated pairs in a data sample. But we can’t just attach p-values to them and publish them. By finding these pairs (with assistance of Correlation widget) we just formulate hypotheses. This is only step 1 of the enshrined NHST procedure. We can’t skip the other two: the next step is to collect some new data (existing data won’t do!) and then use it to test the hypotheses (step 3).\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Following this procedure doesn’t save us from \", _jsx(LinkNew, {\n        url: \"https://en.wikipedia.org/wiki/Data_dredging\",\n        name: \"data dredging\"\n      }), \". There are still plenty of ways to cheat. It is the most tempting to select the first 100 most correlated pairs (or, actually, any 100 pairs), (re)compute correlations on some new data and publish the top 5 % of these pairs. The official solution for this is a patchwork of various corrections for multiple hypotheses testing, but… Well, they don’t work, but we should say no more here. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, pypi, sql</strong></p><h1 class="sc-c67a4900-0 loxVcf">How to enable SQL widget in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 16, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>A lot of you have been interested in enabling SQL widget in Orange, especially regarding the installation of a psycopg backend that makes the widget actually work. This post will be slightly more technical, but I will try to keep it to a minimum. Scroll to the bottom for installation instructions.</p>
 <p>Related: <a href="/blog/2015/10/19/sql-for-orange/">SQL for Orange</a></p>
 <h4></h4>
@@ -164,4 +164,4 @@ <h2>Installation instructions</h2>
 <p>And for Linux: <a href="https://pypi.python.org/packages/92/15/92b5c363243376ce9cb879bbec561bba196694eb663a6937b4cb967e230e/psycopg2-2.7.4-cp36-cp36m-manylinux1_x86_64.whl#md5=8288ce1eedf0b70e5f1d8c982fad5a41">psycopg2-2.7.4-cp36-cp36m-manylinux1_x86_64.whl</a></p>
 <p>Then open the add-on dialog in Orange (Options --&gt; Add-ons) and drag and drop the downloaded wheel into the add-on list. At the bottom, you will see psycopg2 with the tick next to it.</p>
 <a href="/blog/2018-02-how-to-enable-sql-widget-in-orange/__optimized-images__/Screen-Shot-2018-02-16-at-14.19.51.png" data-gallery="true"><img alt="" loading="lazy" width="852" height="750" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-02-how-to-enable-sql-widget-in-orange/__optimized-images__/Screen-Shot-2018-02-16-at-14.19.51.png"/></a>
-<p>Click OK to run the installation. Then re-start Orange and connect to your database with SQL widget. If you have any questions, drop them in the comment section!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-02-16 13:20:06+00:00","draft":false,"title":"How to enable SQL widget in Orange","blog":["data","pypi","sql"],"oldUrl":"/blog/2018/02/16/how-to-enable-sql-widget-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h4: \"h4\",\n    h2: \"h2\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"A lot of you have been interested in enabling SQL widget in Orange, especially regarding the installation of a psycopg backend that makes the widget actually work. 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+<p>Click OK to run the installation. Then re-start Orange and connect to your database with SQL widget. If you have any questions, drop them in the comment section!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-02-16 13:20:06+00:00","draft":false,"title":"How to enable SQL widget in Orange","blog":["data","pypi","sql"],"oldUrl":"/blog/2018/02/16/how-to-enable-sql-widget-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h4: \"h4\",\n    h2: \"h2\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"A lot of you have been interested in enabling SQL widget in Orange, especially regarding the installation of a psycopg backend that makes the widget actually work. 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Py version has to be cp34 or higher (latest Orange from Anaconda comes with Python 3.6, so look for cp36).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For OSX, you would for example need: \", _jsx(_components.a, {\n        href: \"https://pypi.python.org/packages/8c/a5/0e61d6f4a140a6e06a9ba40266c4b49123d834f1f97fe9a5ae0b6e45112b/psycopg2-2.7.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl#md5=1f2b2137c65dc50c16b341774cd822eb\",\n        children: \"psycopg2-2.7.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For 64-bit Windows: \", _jsx(_components.a, {\n        href: \"https://pypi.python.org/packages/f9/77/e29b792740ddec37a2d49431efa6c707cf3869c0cc7f28c7411bb6e96d91/psycopg2-2.7.4-cp36-cp36m-win_amd64.whl#md5=119eb3ab86ea8486ab10ef4ea3f67f15\",\n        children: \"psycopg2-2.7.4-cp36-cp36m-win_amd64.whl\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"And for Linux: \", _jsx(_components.a, {\n        href: \"https://pypi.python.org/packages/92/15/92b5c363243376ce9cb879bbec561bba196694eb663a6937b4cb967e230e/psycopg2-2.7.4-cp36-cp36m-manylinux1_x86_64.whl#md5=8288ce1eedf0b70e5f1d8c982fad5a41\",\n        children: \"psycopg2-2.7.4-cp36-cp36m-manylinux1_x86_64.whl\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then open the add-on dialog in Orange (Options --\u003e Add-ons) and drag and drop the downloaded wheel into the add-on list. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - How to grow trees in a marmalade factory?</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - How to grow trees in a marmalade factory?"/><meta property="og:description" content="We let the fourth grade students build their own decision tree to classify characters into professions."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2022-06-02"/><meta property="article:author" content="Zala Gruden"/><meta property="article:tag" content="Education"/><meta property="og:image" content="/blog/2022-06-blog-minions-new/__optimized-images__/otrokorange.jpg"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="17"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 <p>What if we told you that it is possible to teach data mining to kids in elementary school in an enjoyable way? You’d say,</p>
 <p>&quot;Impossible!&quot; and
@@ -177,4 +177,4 @@
 <p><em>A tree that classifies animals into mammals, birds, reptiles etc based on the data collected by children.</em></p>
 <p>We checked with a squirrel, a bear, a crow, a frog, and a sea lion. The tree has correctly classified all the instances. Still, there was some erroneous data, which provided us with a beautiful lesson on the importance of understanding data before analyzing it.</p>
 <p>Some variables were misunderstood. Some children described birds as hairy – a Slovenian word that could, with some imagination, also refer to feathers. The term &quot;ear&quot; also proved to be ambiguous. Some children have noted that all animals must have ears, but only some have visible ears. And the visible ear was one of the determining variables.</p>
-<p>We noticed that engaging children in creating data they created is valuable. This is particularly important for children of this age group (around 10 years old).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Zala Gruden","date":"2022-06-02","draft":false,"title":"How to grow trees in a marmalade factory?","thumbImage":"otrokorange.jpg","frontPageImage":"otrokorange.jpg","blog":["Education"],"shortExcerpt":"We let the fourth grade students build their own decision tree to classify characters into professions.","longExcerpt":"We visited fourthgraders in our local elementary school and taught them about clustering and data collection.","oldUrl":"/blog/2022/2022-06-01-blog-minions-new/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2022-06-blog-minions-new/2022-06-02-working-together.jpg\",\n      width: \"4928\",\n      height: \"3264\",\n      src: \"/blog/2022-06-blog-minions-new/__optimized-images__/2022-06-02-working-together.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What if we told you that it is possible to teach data mining to kids in elementary school in an enjoyable way? 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Janez showed them the collected data and constructed a decision tree using Orange.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We tested if the tree works correctly by picking an animal and \\\"manually” checking whether the tree made the correct classification.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-06-blog-minions-new/22-05-31-tree-with-animals.png\",\n      width: \"2528\",\n      height: \"990\",\n      src: \"/blog/2022-06-blog-minions-new/__optimized-images__/22-05-31-tree-with-animals.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"A tree that classifies animals into mammals, birds, reptiles etc based on the data collected by children.\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We checked with a squirrel, a bear, a crow, a frog, and a sea lion. The tree has correctly classified all the instances. 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+<p>We noticed that engaging children in creating data they created is valuable. This is particularly important for children of this age group (around 10 years old).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Zala Gruden","date":"2022-06-02","draft":false,"title":"How to grow trees in a marmalade factory?","thumbImage":"otrokorange.jpg","frontPageImage":"otrokorange.jpg","blog":["Education"],"shortExcerpt":"We let the fourth grade students build their own decision tree to classify characters into professions.","longExcerpt":"We visited fourthgraders in our local elementary school and taught them about clustering and data collection.","oldUrl":"/blog/2022/2022-06-01-blog-minions-new/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2022-06-blog-minions-new/2022-06-02-working-together.jpg\",\n      width: \"4928\",\n      height: \"3264\",\n      src: \"/blog/2022-06-blog-minions-new/__optimized-images__/2022-06-02-working-together.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What if we told you that it is possible to teach data mining to kids in elementary school in an enjoyable way? 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diff --git a/blog/how-to-identify-fake-news-with-document-embeddings/index.html b/blog/how-to-identify-fake-news-with-document-embeddings/index.html
index 34e6a381c..8a99aeba8 100644
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>text mining, corpus, classification</strong></p><h1 class="sc-c67a4900-0 loxVcf">How to identify fake news with document embeddings</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Primož Godec and Nikola Đukić</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 15, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Text is described by the sequence of character. Since every machine learning algorithm needs numbers, we need to transform it into vectors of real numbers before we can continue with the analysis. To do this, we can use various approaches. Orange currently offers bag-of-words approach and now also Document embedding by fastText.
 In this post, we explain what document embedding is, why it is useful, and show its usage on the classification example.</p>
 <h2>Word embedding and document embedding</h2>
@@ -185,4 +185,4 @@ <h2>Fake news identification</h2>
 <p>In the bottom part of the widget, we inspect the accuracies. In the column with name CA (classification accuracy), we can see that both models perform with around 80 % accuracy. In the table above, we can find cases where models made mistakes. If we select rows, we can check them in the Corpus Viewer widget which is connected to the Predictions widget. We have also connected the confusion matrix widget to our workflow, which shows the proportions between the predicted and actual classes.</p>
 <a href="/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-confusion.png" data-gallery="true"><img alt="" loading="lazy" width="2760" height="690" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-confusion.png"/></a>
 <p>We can see that Logistic regression is slightly more accurate in cases of real news while Random forest model is better for predicting fake news.</p>
-<p>It is just one example which shows how to use document embeddings.  You can also use them for other tasks such as clustering, regression or other types of analysis.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Primož Godec and Nikola Đukić","date":"2020-10-15","draft":false,"title":"How to identify fake news with document embeddings","thumbImage":"2020-10-15-document-embedding-widget.png","frontPageImage":"2020-10-15-document-embedding-title.png","blog":["text mining","corpus","classification"],"shortExcerpt":"New Document embedder widget and its use for classification","longExcerpt":"Presenting document embeddings widget and how to identify fake news.","x2images":true,"oldUrl":"/blog/2020/2020-10-15-document-embedders/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Text is described by the sequence of character. Since every machine learning algorithm needs numbers, we need to transform it into vectors of real numbers before we can continue with the analysis. To do this, we can use various approaches. Orange currently offers bag-of-words approach and now also Document embedding by fastText.\\nIn this post, we explain what document embedding is, why it is useful, and show its usage on the classification example.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Word embedding and document embedding\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Before we understand document embeddings, we need to understand the concept of word embeddings. Word embedding is a representation of a word in multidimensional spaces such that words with similar meanings have similar embedding. It means that each word is mapped to the vector of real numbers that represents the word.\\nEmbedding models are mostly based on neural networks.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Document embedding is computed in two steps. First, each word is embedded with the word embedding then word embeddings are aggregated. The most common type of aggregation is the average over each dimension.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why and when should we use embedders?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Compared to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/bagofwords-widget\",\n        children: \"bag-of-words\"\n      }), \", which counts the number of appearances of each token in the document, embeddings have two main advantages:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"They do not have a dimensionality problem\"\n        }), \" The result of bag-of-words is a table which has the number of features equal to the number of unique tokens in all documents in a corpus. Large corpora with long texts result in a large number of unique tokens. It results in huge tables which can exceed memory in the computer. Huge tables also increase the learning and evaluation time of machine learning models. Embedders have constant dimensionality of the vector, which is 300 for fastText embeddings that Orange uses.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"Most of the preprocessing is not required\"\n        }), \" In case of bag-of-words approach, we solve the dimensionality problem with the text preprocessing where we remove tokens (e.g. words) that seem to be less important for the analysis. It can also cause the removal of some important tokens. When using embedders, we do not need to remove tokens, so we are not losing the accuracy this way. Also most of the basic preprocessing can be omitted (such as normalization) in case of fastText embeddings.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"They can be pretrained\"\n        }), \" Word embedding models can be pretrained on large corpora with billions of tokens. That way, they capture the significant characteristics of the language and produce the embeddings of high quality. Pretrained models are then used to obtain embeddings of smaller datasets. Our Document Embedding widget uses pretrained fastText models and is suitable for corpora of any size.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The shortcoming of the embedders is that they are difficult to understand. For example, when we use a bag-of-words, we can easily observe which tokens are important for classification with \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/nomogram/\",\n        children: \"Nomogram widget\"\n      }), \" since tokens themselves are features. In the case of document embeddings, features are numbers which are not understandable to human by themselves.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Document Embedding widget\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange now offers document embedders through Document Embedding widget. We decided to use \", _jsx(_components.a, {\n        href: \"https://fasttext.cc/docs/en/crawl-vectors.html\",\n        children: \"fastText pretrained embedders\"\n      }), \", which support 157 languages. Orange's Document Embedding widget currently supports 31 most common languages.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-document-embedding-widget.png\",\n      width: \"608\",\n      height: \"486\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-document-embedding-widget.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the widget, the user sets the language of documents and the aggregation method -- it is how embeddings for each word in a document are aggregated into one document embedding.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"The Fake News dataset\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this tutorial, we use the sample of \", _jsx(_components.a, {\n        href: \"https://www.kaggle.com/c/fake-news/data\",\n        children: \"Fake News dataset\"\n      }), \". The dataset sample is available at \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/datasets/fake.zip\",\n        children: \"Orange's file repository\"\n      }), \". It is a zip archive containing two datasets: training set including 2725 text items and testing set with 275 items. Each item is an article which is labelled as a real or fake.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Fake news identification\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here we present a fake news identification. First, we will load a training part of the dataset with the Corpus widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-corpus.png\",\n      width: \"982\",\n      height: \"728\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-corpus.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"After the dataset is loaded, we make sure that the \", _jsx(_components.code, {\n        children: \"text\"\n      }), \" variable is selected in the Used text features field. It means that the text in this variable is used in the text analysis. When the dataset is loaded we connect the Corpus widget to the Document embedder widget which will compute text embeddings. Our workflow should look like this now:\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-workflow1.png\",\n      width: \"642\",\n      height: \"762\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-workflow1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the document embeddings widget, we check that language is set to English since texts in this dataset are English. We will use mean (average) aggregation in this experiment -- it is the most standard one. After minute documents are embedded -- embedding progress is shown with the bar around the widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When embeddings are ready, we can train models. In this tutorial, we train two models -- Logistic regression and Random forest. We will use default settings for both learners.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-workflow2.png\",\n      width: \"756\",\n      height: \"396\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-workflow2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When our models are trained, we prepare the testing data. To load testing data, we use another Corpus widget and connect it to the Document embedder widget. Settings are the same as before. The only difference is that this time we load testing part of the dataset in the second Corpus widget. To make predictions and inspect the prediction results on the testing dataset, we use the prediction widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-workflow3.png\",\n      width: \"1378\",\n      height: \"1272\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-workflow3.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the bottom part of the widget, we inspect the accuracies. In the column with name CA (classification accuracy), we can see that both models perform with around 80 % accuracy. In the table above, we can find cases where models made mistakes. If we select rows, we can check them in the Corpus Viewer widget which is connected to the Predictions widget. We have also connected the confusion matrix widget to our workflow, which shows the proportions between the predicted and actual classes.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-confusion.png\",\n      width: \"2760\",\n      height: \"690\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-confusion.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that Logistic regression is slightly more accurate in cases of real news while Random forest model is better for predicting fake news.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It is just one example which shows how to use document embeddings.  You can also use them for other tasks such as clustering, regression or other types of analysis.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":608,"height":486,"src":"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-document-embedding-widget.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"how-to-identify-fake-news-with-document-embeddings"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>It is just one example which shows how to use document embeddings.  You can also use them for other tasks such as clustering, regression or other types of analysis.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Primož Godec and Nikola Đukić","date":"2020-10-15","draft":false,"title":"How to identify fake news with document embeddings","thumbImage":"2020-10-15-document-embedding-widget.png","frontPageImage":"2020-10-15-document-embedding-title.png","blog":["text mining","corpus","classification"],"shortExcerpt":"New Document embedder widget and its use for classification","longExcerpt":"Presenting document embeddings widget and how to identify fake news.","x2images":true,"oldUrl":"/blog/2020/2020-10-15-document-embedders/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Text is described by the sequence of character. Since every machine learning algorithm needs numbers, we need to transform it into vectors of real numbers before we can continue with the analysis. To do this, we can use various approaches. Orange currently offers bag-of-words approach and now also Document embedding by fastText.\\nIn this post, we explain what document embedding is, why it is useful, and show its usage on the classification example.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Word embedding and document embedding\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Before we understand document embeddings, we need to understand the concept of word embeddings. Word embedding is a representation of a word in multidimensional spaces such that words with similar meanings have similar embedding. It means that each word is mapped to the vector of real numbers that represents the word.\\nEmbedding models are mostly based on neural networks.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Document embedding is computed in two steps. First, each word is embedded with the word embedding then word embeddings are aggregated. The most common type of aggregation is the average over each dimension.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why and when should we use embedders?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Compared to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/bagofwords-widget\",\n        children: \"bag-of-words\"\n      }), \", which counts the number of appearances of each token in the document, embeddings have two main advantages:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"They do not have a dimensionality problem\"\n        }), \" The result of bag-of-words is a table which has the number of features equal to the number of unique tokens in all documents in a corpus. Large corpora with long texts result in a large number of unique tokens. It results in huge tables which can exceed memory in the computer. Huge tables also increase the learning and evaluation time of machine learning models. Embedders have constant dimensionality of the vector, which is 300 for fastText embeddings that Orange uses.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"Most of the preprocessing is not required\"\n        }), \" In case of bag-of-words approach, we solve the dimensionality problem with the text preprocessing where we remove tokens (e.g. words) that seem to be less important for the analysis. It can also cause the removal of some important tokens. When using embedders, we do not need to remove tokens, so we are not losing the accuracy this way. Also most of the basic preprocessing can be omitted (such as normalization) in case of fastText embeddings.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"They can be pretrained\"\n        }), \" Word embedding models can be pretrained on large corpora with billions of tokens. That way, they capture the significant characteristics of the language and produce the embeddings of high quality. Pretrained models are then used to obtain embeddings of smaller datasets. Our Document Embedding widget uses pretrained fastText models and is suitable for corpora of any size.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The shortcoming of the embedders is that they are difficult to understand. For example, when we use a bag-of-words, we can easily observe which tokens are important for classification with \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/nomogram/\",\n        children: \"Nomogram widget\"\n      }), \" since tokens themselves are features. In the case of document embeddings, features are numbers which are not understandable to human by themselves.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Document Embedding widget\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Orange now offers document embedders through Document Embedding widget. We decided to use \", _jsx(_components.a, {\n        href: \"https://fasttext.cc/docs/en/crawl-vectors.html\",\n        children: \"fastText pretrained embedders\"\n      }), \", which support 157 languages. Orange's Document Embedding widget currently supports 31 most common languages.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-document-embedding-widget.png\",\n      width: \"608\",\n      height: \"486\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-document-embedding-widget.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the widget, the user sets the language of documents and the aggregation method -- it is how embeddings for each word in a document are aggregated into one document embedding.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"The Fake News dataset\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this tutorial, we use the sample of \", _jsx(_components.a, {\n        href: \"https://www.kaggle.com/c/fake-news/data\",\n        children: \"Fake News dataset\"\n      }), \". The dataset sample is available at \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/datasets/fake.zip\",\n        children: \"Orange's file repository\"\n      }), \". It is a zip archive containing two datasets: training set including 2725 text items and testing set with 275 items. Each item is an article which is labelled as a real or fake.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Fake news identification\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here we present a fake news identification. First, we will load a training part of the dataset with the Corpus widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-corpus.png\",\n      width: \"982\",\n      height: \"728\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-corpus.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"After the dataset is loaded, we make sure that the \", _jsx(_components.code, {\n        children: \"text\"\n      }), \" variable is selected in the Used text features field. It means that the text in this variable is used in the text analysis. When the dataset is loaded we connect the Corpus widget to the Document embedder widget which will compute text embeddings. Our workflow should look like this now:\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-workflow1.png\",\n      width: \"642\",\n      height: \"762\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-workflow1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the document embeddings widget, we check that language is set to English since texts in this dataset are English. We will use mean (average) aggregation in this experiment -- it is the most standard one. After minute documents are embedded -- embedding progress is shown with the bar around the widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When embeddings are ready, we can train models. In this tutorial, we train two models -- Logistic regression and Random forest. We will use default settings for both learners.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-workflow2.png\",\n      width: \"756\",\n      height: \"396\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-workflow2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When our models are trained, we prepare the testing data. To load testing data, we use another Corpus widget and connect it to the Document embedder widget. Settings are the same as before. The only difference is that this time we load testing part of the dataset in the second Corpus widget. To make predictions and inspect the prediction results on the testing dataset, we use the prediction widget.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-workflow3.png\",\n      width: \"1378\",\n      height: \"1272\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-workflow3.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the bottom part of the widget, we inspect the accuracies. In the column with name CA (classification accuracy), we can see that both models perform with around 80 % accuracy. In the table above, we can find cases where models made mistakes. If we select rows, we can check them in the Corpus Viewer widget which is connected to the Predictions widget. We have also connected the confusion matrix widget to our workflow, which shows the proportions between the predicted and actual classes.\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2020-10-document-embedders/2020-10-15-confusion.png\",\n      width: \"2760\",\n      height: \"690\",\n      src: \"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-confusion.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that Logistic regression is slightly more accurate in cases of real news while Random forest model is better for predicting fake news.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It is just one example which shows how to use document embeddings.  You can also use them for other tasks such as clustering, regression or other types of analysis.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":608,"height":486,"src":"/blog/2020-10-document-embedders/__optimized-images__/2020-10-15-document-embedding-widget.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"how-to-identify-fake-news-with-document-embeddings"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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@@ -173,4 +173,4 @@
 <p>Test &amp; Score will warn us we have test data present, but unused. Select Test on test data option and observe the results. These are now the proper scores for our models.</p>
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-<p>Seems like LogReg still performs well. Such procedure would normally be useful when testing a lot of models with different parameters (say +100), which you would not normally do in Orange. But it&#x27;s good to know how to do the scoring properly. Now we&#x27;re off to report on the results in Nature... ;)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-11-29 12:26:44+00:00","draft":false,"title":"How to Properly Test Models","blog":["analysis","classification","education","overfitting","predictive  analytics","scoring","workshop"],"oldUrl":"/blog/2017/11/29/how-to-properly-test-models/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"On Monday we finished the second part of the workshop for the \", _jsx(_components.a, {\n        href: \"http://www.stat.si/StatWeb/en\",\n        children: \"Statistical Office\"\n      }), \" of Republic of Slovenia. The crowd was tough - these guys knew their numbers and asked many challenging questions. And we loved it!\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/IMG_20171124_120523.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"722\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One thing we discussed was how to properly test your model. Ok, we know never to test on the same data you've built your model with, but even training and testing on separate data is sometimes not enough. Say I've tested Naive Bayes, Logistic Regression and Tree. Sure, I can select the one that gives the best performance, but we could potentially (over)fit our model, too.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To account for this, we would normally split the data to 3 parts:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"training data for building a model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"validation data for testing which parameters and which model to use\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"test data for estmating the accurracy of the model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us try this in Orange. Load \", _jsx(_components.em, {\n        children: \"heart-disease.tab\"\n      }), \" data set from \", _jsx(_components.em, {\n        children: \"Browse documentation data sets\"\n      }), \" in File widget. We have 303 patients diagnosed with blood vessel narrowing (1) or diagnosed as healthy (0).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.19.15.png\",\n      alt: \"\",\n      width: \"942\",\n      height: \"982\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, we will split the data into two parts, 85% of data for training and 15% for testing. 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We used cross-validation and discovered Logistic Regression scores the highest AUC. Say this is the model and parameters we want to go with.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.11.10.png\",\n      alt: \"\",\n      width: \"1008\",\n      height: \"674\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.11.57.png\",\n      alt: \"\",\n      width: \"1398\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now it is time to bring in our test data (the remaining 15%) for testing. Connect Data Sampler to Test \u0026 Score once again and set the connection Remaining Data - Test Data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-10.38.15.png\",\n      alt: \"\",\n      width: \"1164\",\n      height: \"628\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Test \u0026 Score will warn us we have test data present, but unused. Select Test on test data option and observe the results. These are now the proper scores for our models.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.12.56.png\",\n      alt: \"\",\n      width: \"982\",\n      height: \"668\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.00.17.png\",\n      alt: \"\",\n      width: \"1380\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Seems like LogReg still performs well. Such procedure would normally be useful when testing a lot of models with different parameters (say +100), which you would not normally do in Orange. But it's good to know how to do the scoring properly. 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+<p>Seems like LogReg still performs well. Such procedure would normally be useful when testing a lot of models with different parameters (say +100), which you would not normally do in Orange. But it&#x27;s good to know how to do the scoring properly. Now we&#x27;re off to report on the results in Nature... ;)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-11-29 12:26:44+00:00","draft":false,"title":"How to Properly Test Models","blog":["analysis","classification","education","overfitting","predictive  analytics","scoring","workshop"],"oldUrl":"/blog/2017/11/29/how-to-properly-test-models/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"On Monday we finished the second part of the workshop for the \", _jsx(_components.a, {\n        href: \"http://www.stat.si/StatWeb/en\",\n        children: \"Statistical Office\"\n      }), \" of Republic of Slovenia. 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Sure, I can select the one that gives the best performance, but we could potentially (over)fit our model, too.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To account for this, we would normally split the data to 3 parts:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"training data for building a model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"validation data for testing which parameters and which model to use\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"test data for estmating the accurracy of the model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us try this in Orange. Load \", _jsx(_components.em, {\n        children: \"heart-disease.tab\"\n      }), \" data set from \", _jsx(_components.em, {\n        children: \"Browse documentation data sets\"\n      }), \" in File widget. 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We will send the first 85% onwards to build a model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-10.37.18.png\",\n      alt: \"\",\n      width: \"330\",\n      height: \"176\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-10.37.24.png\",\n        alt: \"\",\n        width: \"530\",\n        height: \"1090\"\n      }), \"\\nWe sampled by a fixed proportion of data and went with 85%, which is 258 out of 303 patients.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will use Naive Bayes, Logistic Regression and Tree, but you can try other models, too. This is also a place and time to try different parameters. Now we will send the models to Test \u0026 Score. We used cross-validation and discovered Logistic Regression scores the highest AUC. Say this is the model and parameters we want to go with.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.11.10.png\",\n      alt: \"\",\n      width: \"1008\",\n      height: \"674\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.11.57.png\",\n      alt: \"\",\n      width: \"1398\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now it is time to bring in our test data (the remaining 15%) for testing. Connect Data Sampler to Test \u0026 Score once again and set the connection Remaining Data - Test Data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-10.38.15.png\",\n      alt: \"\",\n      width: \"1164\",\n      height: \"628\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Test \u0026 Score will warn us we have test data present, but unused. Select Test on test data option and observe the results. These are now the proper scores for our models.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.12.56.png\",\n      alt: \"\",\n      width: \"982\",\n      height: \"668\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-how-to-properly-test-models/__optimized-images__/Screen-Shot-2017-11-29-at-11.00.17.png\",\n      alt: \"\",\n      width: \"1380\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Seems like LogReg still performs well. Such procedure would normally be useful when testing a lot of models with different parameters (say +100), which you would not normally do in Orange. But it's good to know how to do the scoring properly. 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diff --git a/blog/hubbing-with-the-hub-widget/index.html b/blog/hubbing-with-the-hub-widget/index.html
index 2d63495ff..65095b4db 100644
--- a/blog/hubbing-with-the-hub-widget/index.html
+++ b/blog/hubbing-with-the-hub-widget/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, data, download, orange3, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Hubbing with the Hub widget</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 11, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>So you have painted two data sets and loaded another one from a file, and now you are testing predictions of logistic regression, classification trees and SVM on it? Tired of having to reconnect the <strong>Paint data</strong> widget and the <strong>File</strong> widget back and forth whenever you switch between them?</p>
 <p><strong>Say no more! Look no further!</strong> Here is the new <strong>Hub widget</strong>!</p>
 <a href="/blog/2015-09-hubbing-with-hub-widget/__optimized-images__/blog21.png" data-gallery="true"><img alt="" loading="lazy" width="502" height="481" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-09-hubbing-with-hub-widget/__optimized-images__/blog21.png"/></a>
@@ -158,4 +158,4 @@
 <p>(It also adheres to all applicable EU policies with respect to gender equality, and does not use cookies.)</p>
 <a href="/blog/2015-09-hubbing-with-hub-widget/__optimized-images__/blog11.png" data-gallery="true"><img alt="" loading="lazy" width="631" height="520" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-09-hubbing-with-hub-widget/__optimized-images__/blog11.png"/></a>
 <p>The <strong>Hub widget</strong> works like charm and is like the amazing <em>cast-to-void-and-back-to-anything</em> idiom in C. This strongful MacGyver of widgets can (almost) convert classification tree into data, or preprocessor into experimental results without ever touching the data. With its amazing capabilities, the <strong>Hub widget</strong> has the potential to cause an even greater havoc in your workflows than the famous <strong>Merge data</strong> widget.</p>
-<p><strong><a href="https://github.com/biolab/orange3-prototypes">Download, install - and start hubbing today !!</a></strong></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-09-11 06:56:16+00:00","draft":false,"title":"Hubbing with the Hub widget","blog":["addons","data","download","orange3","widget"],"oldUrl":"/blog/2015/09/11/hubbing-with-hub-widget/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"So you have painted two data sets and loaded another one from a file, and now you are testing predictions of logistic regression, classification trees and SVM on it? 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+<p><strong><a href="https://github.com/biolab/orange3-prototypes">Download, install - and start hubbing today !!</a></strong></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-09-11 06:56:16+00:00","draft":false,"title":"Hubbing with the Hub widget","blog":["addons","data","download","orange3","widget"],"oldUrl":"/blog/2015/09/11/hubbing-with-hub-widget/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"So you have painted two data sets and loaded another one from a file, and now you are testing predictions of logistic regression, classification trees and SVM on it? 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education</strong></p><h1 class="sc-c67a4900-0 loxVcf">Ideas and Notes for Teachers</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 16, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>On May 26, 2002, we had our first <a href="https://orangedatamining.com/blog/2022/2022-04-26-edu-webinar/">webinar targeting teaching with Orange</a>. During the first part of the webinar, I demonstrated how we could use Orange to introduce classification trees. On their own, classification trees are quite lousy classifiers. They are, nevertheless, important as we can assemble them into very powerful classifiers. The lessons I have presented deal with overfitting and the idea that we should never report classification accuracy on the training data. The trick we use when teaching this is to show the trainees that classification trees also perform well on data with random, that is, useless labels (see the figure below). And then, of course, we ask the audience what went wrong and why and how we should fix the workflow.</p>
 <a href="/blog/2022-06-training-material/__optimized-images__/2022-05-26-ouverfitting-trees.png" data-gallery="true"><img alt="" loading="lazy" width="977" height="837" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-06-training-material/__optimized-images__/2022-05-26-ouverfitting-trees.png"/></a>
 <p>I presented this and several other lessons this week during <a href="https://orangedatamining.com/blog/2022/2022-04-26-amia-tutorial/">the tutorial at Artificial Intelligence in Medicine conference</a>. This year <a href="http://aime22.aimedicine.info">AIME</a> took place in Halifax, and I am writing this blog on a plane to Brussels, where I will attend a kick-off meeting of an ARISA, a European project to devise certified lessons and training of AI for the EU industry. Training in AI is becoming a hot topic. :). Back to Halifax. I packed the presentation with a short showcase of lectures we usually use in training. The tutorial started at 14.00 with an introduction, and a complete showcase on image analytics, whereas the lessons I have presented included those from the following list. I am here showing the timing to appreciate what can one cover with Orange in a concise time:</p>
@@ -185,4 +185,4 @@
 <li><a href="http://youtube.com/orangedatamining">YouTube videos</a></li>
 <li>our publications in training with Orange on <a href="https://journals.plos.org/ploscompbiol/article/authors?id=10.1371/journal.pcbi.1008671">outliers</a>, <a href="https://academic.oup.com/bioinformatics/article/35/14/i4/5529249">gene expression analysis</a>, and <a href="https://www.nature.com/articles/s41467-019-12397-x">image analytics</a></li>
 </ul>
-<p>You are most welcome to review and use the above material. We also welcome any suggestions and comments. Let us know about your ideas in the <a href="https://discord.gg/FWrfeXV">support channel on Discord</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-06-16","draft":false,"title":"Ideas and Notes for Teachers","thumbImage":"2022-06-16-randomized-data.png","frontPageImage":"2022-06-16-randomized-data.png","blog":["education"],"shortExcerpt":"We are crafting free educational material to help in data science training.","longExcerpt":"We are crafting free educational material to help in data science training.","x2images":true,"oldUrl":"/blog/2022/2022-06-16-training-material/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"On May 26, 2002, we had our first \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/2022/2022-04-26-edu-webinar/\",\n        children: \"webinar targeting teaching with Orange\"\n      }), \". During the first part of the webinar, I demonstrated how we could use Orange to introduce classification trees. On their own, classification trees are quite lousy classifiers. They are, nevertheless, important as we can assemble them into very powerful classifiers. The lessons I have presented deal with overfitting and the idea that we should never report classification accuracy on the training data. The trick we use when teaching this is to show the trainees that classification trees also perform well on data with random, that is, useless labels (see the figure below). 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I am here showing the timing to appreciate what can one cover with Orange in a concise time:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"[15.00] Orange mechanics: data exploration on human development index data\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[15.10] Hierarchical clustering: data on grades in english and math\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[15.25] Cluster explanation: data on student grades, discussion on choosing the number of clusters\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[15.40] Clustering and geo maps: back to human development data, showcase of widgets in geo add-on\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[15.50] Outliers and inliers: silhouette score\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[15.55] K-means clustering on painted data\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.00] Introduction to classification tree: on Iris dataset, manual tree construction with widget that displays distributions\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.10] Overfitting: with classification trees on iris with randomized classes, accuracy estimate through random sampling, cross-validation\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.20] Regression: overfitting in training with polynomial expansion, introduction to regularization\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.30] Data projections: PCA, MDS and t-SNE on zoo dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.40] single cell analytics, data projections\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.50] Image analytics, outlier detection, example with images of traffic signs\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"[16.55] Gene expression analysis: dataset with single-cell gene expression analysis, classification, utility of gene markers, cell type discovery, discovery of new markers\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"During AIME, I also pointed out that the minimum time for training on different topics from above takes hours, not minutes. 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The material includes:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://github.com/biolab/orange-lecture-notes\",\n          children: \"Lecture notes, material in LaTeX\"\n        }), \", see also \", _jsx(_components.a, {\n          href: \"/blog/2022-06-training-material/\",\n          children: \"blog with examples of rendered notes\"\n        })]\n      }), \"\\n\", _jsx(_components.li, {\n        children: _jsx(_components.a, {\n          href: \"http://youtube.com/orangedatamining\",\n          children: \"YouTube videos\"\n        })\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"our publications in training with Orange on \", _jsx(_components.a, {\n          href: \"https://journals.plos.org/ploscompbiol/article/authors?id=10.1371/journal.pcbi.1008671\",\n          children: \"outliers\"\n        }), \", \", _jsx(_components.a, {\n          href: \"https://academic.oup.com/bioinformatics/article/35/14/i4/5529249\",\n          children: \"gene expression analysis\"\n        }), \", and \", _jsx(_components.a, {\n          href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n          children: \"image analytics\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You are most welcome to review and use the above material. 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+<p>You are most welcome to review and use the above material. We also welcome any suggestions and comments. Let us know about your ideas in the <a href="https://discord.gg/FWrfeXV">support channel on Discord</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-06-16","draft":false,"title":"Ideas and Notes for Teachers","thumbImage":"2022-06-16-randomized-data.png","frontPageImage":"2022-06-16-randomized-data.png","blog":["education"],"shortExcerpt":"We are crafting free educational material to help in data science training.","longExcerpt":"We are crafting free educational material to help in data science training.","x2images":true,"oldUrl":"/blog/2022/2022-06-16-training-material/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"On May 26, 2002, we had our first \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/2022/2022-04-26-edu-webinar/\",\n        children: \"webinar targeting teaching with Orange\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, clustering, embedding, images, interactive  data visualization, orange3, unsupervised</strong></p><h1 class="sc-c67a4900-0 loxVcf">Image Analytics: Clustering</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 03, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Data does not always come in a nice tabular form. It can also be a collection of text, audio recordings, video materials or even images. However, computers can only work with numbers, so for any data mining, we need to transform such unstructured data into a vector representation.</p>
 <p>For retrieving numbers from unstructured data, <a href="http://orange.biolab.si">Orange</a> can use deep network embedders. We have just started to include various embedders in Orange, and for now, they are available for text and images.</p>
 <p><strong>Related:</strong> <a href="https://www.youtube.com/watch?v=Iu8g2Twjn9U">Video on image clustering</a></p>
@@ -176,4 +176,4 @@
 <p>This image is quite different from the other images - it doesn&#x27;t have a white background, it&#x27;s a real (yet photoshopped) photo and the cow is facing us. Will the Image Embedding find the right numerical representation for this cow?</p>
 <a href="/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.30.41.png" data-gallery="true"><img alt="" loading="lazy" width="2590" height="1590" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.30.41.png"/></a>
 <p>Indeed it has. Milka is nicely put together with all the other cows.</p>
-<p>Image analytics is such an exciting field in machine learning and now Orange is a part of it too! You need to install the Image Analytics add on and you are all set for your research!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-04-03 12:36:22+00:00","draft":false,"title":"Image Analytics: Clustering","blog":["addons","analysis","clustering","embedding","images","interactive  data visualization","orange3","unsupervised"],"oldUrl":"/blog/2017/04/03/image-analytics-clustering/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Data does not always come in a nice tabular form. It can also be a collection of text, audio recordings, video materials or even images. However, computers can only work with numbers, so for any data mining, we need to transform such unstructured data into a vector representation.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For retrieving numbers from unstructured data, \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" can use deep network embedders. We have just started to include various embedders in Orange, and for now, they are available for text and images.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=Iu8g2Twjn9U\",\n        children: \"Video on image clustering\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here, we give an example of image embedding and show how easy is to use it in Orange. Technically, Orange would send the image to the server, where the server would push an image through a pre-trained deep neural network, like \", _jsx(_components.a, {\n        href: \"https://www.tensorflow.org/tutorials/image_recognition\",\n        children: \"Google's Inception v3\"\n      }), \". Deep networks were most often trained with some special purpose in mind. Inception v3, for instance, can classify images into any of \", _jsx(_components.a, {\n        href: \"http://image-net.org/challenges/LSVRC/2014/browse-synsets\",\n        children: \"1000 image classes\"\n      }), \". We can disregard the classification, consider instead the penultimate layer of the network with 2048 nodes (numbers) and use that for image's vector-based representation.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's see this on an example.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here we have 19 \", _jsx(_components.a, {\n        href: \"http://tinyurl.com/images-domestic-animals\",\n        children: \"images of domestic animals\"\n      }), \". First, download the images and unzip them. Then use Import Images widget from Orange's Image Analytics add-on and open the directory containing the images.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/ImportImages.png\",\n      alt: \"\",\n      width: \"790\",\n      height: \"316\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can visualize images in Image Viewer widget. Here is our workflow so far, with images shown in Image Viewer:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/image-viewer.png\",\n      alt: \"\",\n      width: \"436\",\n      height: \"348\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.07.36.png\",\n      alt: \"\",\n      width: \"1926\",\n      height: \"1478\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But what do we see in a data table? Only some useless description of images (file name, the location of the file, its size, and the image width and height).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.11.06.png\",\n      alt: \"\",\n      width: \"1780\",\n      height: \"1166\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This cannot help us with machine learning. As I said before, we need numbers. To acquire numerical representation of these images, we will send the images to Image Embedding widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.15.50.png\",\n      alt: \"\",\n      width: \"2480\",\n      height: \"1120\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Great! Now we have the numbers we wanted. There are 2048 of them (columns n0 to n2047). From now on, we can apply all the standard machine learning techniques, say, clustering.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us measure the distance between these images and see which are the most similar. We used Distances widget to measure the distance. Normally, cosine distance works best for images, but you can experiment on your own. Then we passed the distance matrix to Hierarchical Clustering to visualize similar pairs in a dendrogram.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.20.38.png\",\n      alt: \"\",\n      width: \"1522\",\n      height: \"1612\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This looks very promising! All the right animals are grouped together. But I can't see the results so well in the dendrogram. I want to see the images - with Image Viewer!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.23.38.png\",\n      alt: \"\",\n      width: \"2566\",\n      height: \"1590\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So cool! All the cow family is grouped together! Now we can click on different branches of the dendrogram and observe which animals belong to which group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But I know what you are going to say. You are going to say I am cheating. That I intentionally selected similar images to trick you.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I will prove you wrong. 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+<p>Image analytics is such an exciting field in machine learning and now Orange is a part of it too! You need to install the Image Analytics add on and you are all set for your research!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-04-03 12:36:22+00:00","draft":false,"title":"Image Analytics: Clustering","blog":["addons","analysis","clustering","embedding","images","interactive  data visualization","orange3","unsupervised"],"oldUrl":"/blog/2017/04/03/image-analytics-clustering/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Data does not always come in a nice tabular form. It can also be a collection of text, audio recordings, video materials or even images. However, computers can only work with numbers, so for any data mining, we need to transform such unstructured data into a vector representation.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For retrieving numbers from unstructured data, \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" can use deep network embedders. We have just started to include various embedders in Orange, and for now, they are available for text and images.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=Iu8g2Twjn9U\",\n        children: \"Video on image clustering\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here, we give an example of image embedding and show how easy is to use it in Orange. Technically, Orange would send the image to the server, where the server would push an image through a pre-trained deep neural network, like \", _jsx(_components.a, {\n        href: \"https://www.tensorflow.org/tutorials/image_recognition\",\n        children: \"Google's Inception v3\"\n      }), \". Deep networks were most often trained with some special purpose in mind. Inception v3, for instance, can classify images into any of \", _jsx(_components.a, {\n        href: \"http://image-net.org/challenges/LSVRC/2014/browse-synsets\",\n        children: \"1000 image classes\"\n      }), \". We can disregard the classification, consider instead the penultimate layer of the network with 2048 nodes (numbers) and use that for image's vector-based representation.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's see this on an example.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here we have 19 \", _jsx(_components.a, {\n        href: \"http://tinyurl.com/images-domestic-animals\",\n        children: \"images of domestic animals\"\n      }), \". First, download the images and unzip them. Then use Import Images widget from Orange's Image Analytics add-on and open the directory containing the images.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/ImportImages.png\",\n      alt: \"\",\n      width: \"790\",\n      height: \"316\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can visualize images in Image Viewer widget. Here is our workflow so far, with images shown in Image Viewer:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/image-viewer.png\",\n      alt: \"\",\n      width: \"436\",\n      height: \"348\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.07.36.png\",\n      alt: \"\",\n      width: \"1926\",\n      height: \"1478\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But what do we see in a data table? Only some useless description of images (file name, the location of the file, its size, and the image width and height).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.11.06.png\",\n      alt: \"\",\n      width: \"1780\",\n      height: \"1166\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This cannot help us with machine learning. As I said before, we need numbers. To acquire numerical representation of these images, we will send the images to Image Embedding widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.15.50.png\",\n      alt: \"\",\n      width: \"2480\",\n      height: \"1120\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Great! Now we have the numbers we wanted. There are 2048 of them (columns n0 to n2047). From now on, we can apply all the standard machine learning techniques, say, clustering.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us measure the distance between these images and see which are the most similar. We used Distances widget to measure the distance. Normally, cosine distance works best for images, but you can experiment on your own. Then we passed the distance matrix to Hierarchical Clustering to visualize similar pairs in a dendrogram.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.20.38.png\",\n      alt: \"\",\n      width: \"1522\",\n      height: \"1612\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This looks very promising! All the right animals are grouped together. But I can't see the results so well in the dendrogram. I want to see the images - with Image Viewer!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.23.38.png\",\n      alt: \"\",\n      width: \"2566\",\n      height: \"1590\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So cool! All the cow family is grouped together! Now we can click on different branches of the dendrogram and observe which animals belong to which group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But I know what you are going to say. You are going to say I am cheating. That I intentionally selected similar images to trick you.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I will prove you wrong. I will take a new cow, say, the most famous cow in Europe - Milka cow.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/milka_cow_by_miki3d.jpg\",\n      alt: \"\",\n      width: \"533\",\n      height: \"800\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This image is quite different from the other images - it doesn't have a white background, it's a real (yet photoshopped) photo and the cow is facing us. Will the Image Embedding find the right numerical representation for this cow?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-04-image-analytics-clustering/__optimized-images__/Screen-Shot-2017-03-29-at-10.30.41.png\",\n      alt: \"\",\n      width: \"2590\",\n      height: \"1590\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Indeed it has. Milka is nicely put together with all the other cows.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image analytics is such an exciting field in machine learning and now Orange is a part of it too! You need to install the Image Analytics add on and you are all set for your research!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"image-analytics-clustering"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, conference, embedding, images, visualization, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Image Analytics Workshop at AIUCD 2018</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 02, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This week, Primož and I flew to the south of Italy to hold a workshop on <a href="http://www.aiucd2018.uniba.it/workshops.html">Image Analytics through Data Mining</a> at <a href="http://www.aiucd2018.uniba.it/index.html">AIUCD 2018 conference</a>. The workshop was intended to familiarize digital humanities researchers with options that visual programming environments offer for image analysis.</p>
 <a href="/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/IMG_20180130_172350.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="615" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/IMG_20180130_172350.jpg"/></a>
 <p>In about 5 hours we discussed image embedding, clustering, finding closest neighbors and classification of images. While it is often a challenge to explain complex concepts in such a short time, it is much easier when working with Orange.</p>
@@ -174,4 +174,4 @@
 <p>Monet.jpg is our reference painting. We select it in Data Table.</p>
 <p>Now, all we need to do is to visualize the output. Connect Image Viewer to Neighbors and open it.</p>
 <a href="/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.48.13.png" data-gallery="true"><img alt="" loading="lazy" width="1598" height="762" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.48.13.png"/></a>
-<p>Voila! The widget has indeed found the second Monet&#x27;s painting. So useful when you have thousands of images in your archive!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-02-02 10:32:28+00:00","draft":false,"title":"Image Analytics Workshop at AIUCD 2018","blog":["addons","analysis","conference","embedding","images","visualization","workshop"],"oldUrl":"/blog/2018/02/02/image-analytics-workshop-at-aiucd-2018/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This week, Primož and I flew to the south of Italy to hold a workshop on \", _jsx(_components.a, {\n        href: \"http://www.aiucd2018.uniba.it/workshops.html\",\n        children: \"Image Analytics through Data Mining\"\n      }), \" at \", _jsx(_components.a, {\n        href: \"http://www.aiucd2018.uniba.it/index.html\",\n        children: \"AIUCD 2018 conference\"\n      }), \". The workshop was intended to familiarize digital humanities researchers with options that visual programming environments offer for image analysis.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/IMG_20180130_172350.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"615\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In about 5 hours we discussed image embedding, clustering, finding closest neighbors and classification of images. While it is often a challenge to explain complex concepts in such a short time, it is much easier when working with Orange.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2017/04/03/image-analytics-clustering/\",\n        children: \"Image Analytics: Clustering\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One of the workflows we learned at the workshop was the one for finding the most similar image in a set of images. This is better explained with an example.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We had 15 paintings from different authors. Two of them were painted by \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Claude_Monet\",\n        children: \"Claude Monet\"\n      }), \", a famous French impressionist painter. Our task was, given a reference image of Monet, to find his other painting in a collection.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.33.07.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1120\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A collection of images. It includes two Monet paintings.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, we loaded our \", _jsx(_components.a, {\n        href: \"http:file.biolab.si/images/Paintings.zip\",\n        children: \"data set\"\n      }), \" with Import Images. Then we sent our images to Image Embedding. We selected Painters embedder since it was specifically \", _jsx(_components.a, {\n        href: \"http:blog.kaggle.com/2016/11/17/painter-by-numbers-competition-1st-place-winners-interview-nejc-ilenic/\",\n        children: \"trained to recognize authors of paintings\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.33.54.png\",\n      alt: \"\",\n      width: \"456\",\n      height: \"358\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.34.51.png\",\n      alt: \"\",\n      width: \"758\",\n      height: \"680\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We used Painters embedder here.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Once we have described our paintings with vectors (embeddings), we can compare them by similarity. To find the second Monet in a data set, we will have to compute the similarity of paintings and find the one most similar one to our reference painting.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=Iu8g2Twjn9U\",\n        children: \"Video on image clustering\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us connect Image Embedding to Neighbors from Prototypes add-on. Neighbors widget is specifically intended to find a number of closest neighbors given a reference data point.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will need to adjust the widget a bit. First, we will need cosine distance, since we will be comparing images by the content, not the magnitude of features. Next, we will tick off \", _jsx(_components.em, {\n        children: \"Exclude reference\"\n      }), \", in order to receive the reference image on the output. We do this just for visualization purposes. Finally, we set the number of neighbors to 2. Again, this is just for a nicer visualization, since we know there are only two Monet's paintings in the data set.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.42.59.png\",\n      alt: \"\",\n      width: \"540\",\n      height: \"740\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Neighbors was set to provide a nice visualization. Hence we ticked off Exclude references and set Neighbors to 2.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then we need to give Neighbors a reference image, for which we want to retrieve the neighbors. We do this by adding Data Table to Image Embedding, selecting one of Monet's paintings in the spreadsheet and then connecting the Data Table to Neighbors. The widget will automatically consider the second input as a reference.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.47.57.png\",\n      alt: \"\",\n      width: \"1154\",\n      height: \"464\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.45.37.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"694\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Monet.jpg is our reference painting. We select it in Data Table.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, all we need to do is to visualize the output. Connect Image Viewer to Neighbors and open it.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.48.13.png\",\n      alt: \"\",\n      width: \"1598\",\n      height: \"762\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Voila! The widget has indeed found the second Monet's painting. So useful when you have thousands of images in your archive!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"image-analytics-workshop-at-aiucd-2018"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Voila! The widget has indeed found the second Monet&#x27;s painting. So useful when you have thousands of images in your archive!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-02-02 10:32:28+00:00","draft":false,"title":"Image Analytics Workshop at AIUCD 2018","blog":["addons","analysis","conference","embedding","images","visualization","workshop"],"oldUrl":"/blog/2018/02/02/image-analytics-workshop-at-aiucd-2018/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This week, Primož and I flew to the south of Italy to hold a workshop on \", _jsx(_components.a, {\n        href: \"http://www.aiucd2018.uniba.it/workshops.html\",\n        children: \"Image Analytics through Data Mining\"\n      }), \" at \", _jsx(_components.a, {\n        href: \"http://www.aiucd2018.uniba.it/index.html\",\n        children: \"AIUCD 2018 conference\"\n      }), \". The workshop was intended to familiarize digital humanities researchers with options that visual programming environments offer for image analysis.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/IMG_20180130_172350.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"615\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In about 5 hours we discussed image embedding, clustering, finding closest neighbors and classification of images. While it is often a challenge to explain complex concepts in such a short time, it is much easier when working with Orange.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2017/04/03/image-analytics-clustering/\",\n        children: \"Image Analytics: Clustering\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One of the workflows we learned at the workshop was the one for finding the most similar image in a set of images. This is better explained with an example.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We had 15 paintings from different authors. Two of them were painted by \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Claude_Monet\",\n        children: \"Claude Monet\"\n      }), \", a famous French impressionist painter. Our task was, given a reference image of Monet, to find his other painting in a collection.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.33.07.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1120\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A collection of images. It includes two Monet paintings.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, we loaded our \", _jsx(_components.a, {\n        href: \"http:file.biolab.si/images/Paintings.zip\",\n        children: \"data set\"\n      }), \" with Import Images. Then we sent our images to Image Embedding. We selected Painters embedder since it was specifically \", _jsx(_components.a, {\n        href: \"http:blog.kaggle.com/2016/11/17/painter-by-numbers-competition-1st-place-winners-interview-nejc-ilenic/\",\n        children: \"trained to recognize authors of paintings\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.33.54.png\",\n      alt: \"\",\n      width: \"456\",\n      height: \"358\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.34.51.png\",\n      alt: \"\",\n      width: \"758\",\n      height: \"680\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We used Painters embedder here.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Once we have described our paintings with vectors (embeddings), we can compare them by similarity. To find the second Monet in a data set, we will have to compute the similarity of paintings and find the one most similar one to our reference painting.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=Iu8g2Twjn9U\",\n        children: \"Video on image clustering\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us connect Image Embedding to Neighbors from Prototypes add-on. Neighbors widget is specifically intended to find a number of closest neighbors given a reference data point.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will need to adjust the widget a bit. First, we will need cosine distance, since we will be comparing images by the content, not the magnitude of features. Next, we will tick off \", _jsx(_components.em, {\n        children: \"Exclude reference\"\n      }), \", in order to receive the reference image on the output. We do this just for visualization purposes. Finally, we set the number of neighbors to 2. Again, this is just for a nicer visualization, since we know there are only two Monet's paintings in the data set.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.42.59.png\",\n      alt: \"\",\n      width: \"540\",\n      height: \"740\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Neighbors was set to provide a nice visualization. Hence we ticked off Exclude references and set Neighbors to 2.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then we need to give Neighbors a reference image, for which we want to retrieve the neighbors. We do this by adding Data Table to Image Embedding, selecting one of Monet's paintings in the spreadsheet and then connecting the Data Table to Neighbors. The widget will automatically consider the second input as a reference.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.47.57.png\",\n      alt: \"\",\n      width: \"1154\",\n      height: \"464\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.45.37.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"694\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Monet.jpg is our reference painting. We select it in Data Table.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, all we need to do is to visualize the output. Connect Image Viewer to Neighbors and open it.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-02-image-analytics-workshop-at-aiucd-2018/__optimized-images__/Screen-Shot-2018-02-02-at-10.48.13.png\",\n      alt: \"\",\n      width: \"1598\",\n      height: \"762\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Voila! The widget has indeed found the second Monet's painting. So useful when you have thousands of images in your archive!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"image-analytics-workshop-at-aiucd-2018"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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href="/blog/cookie-mining-2023/"><img alt="Cookie Mining" fetchpriority="high" width="512" height="512" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-mining-thumb.jpg"/></a></figure><div class="sc-3c206b28-6 kZnHmz"><p class="sc-3c206b28-3 hlZWra"><span><span>text mining</span>, </span><span><span>images</span></span></p><h1 class="sc-89e95305-0 iNzSUu"><a href="/blog/cookie-mining-2023/">Cookie Mining</a></h1><p class="sc-89e95305-2 innjCp">A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering.</p><p class="sc-89e95305-2 innjCp"><strong>Blaž Zupan</strong>, <!-- -->Dec 22, 2023</p></div></div><ul class="sc-3c206b28-0 cBWUWw"><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/our-christmas-present-free-orange-t-shirt/"><img alt="Our Christmas Present: Free Orange T-Shirt!" fetchpriority="high" width="2000" height="2000" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-12-survival-analysis-tutorial/__optimized-images__/t-shirt.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>T-shirt</span>, </span><span><span>survival analysis</span>, </span><span><span>tutorial</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/our-christmas-present-free-orange-t-shirt/">Our Christmas Present: Free Orange T-Shirt!</a></h2><p class="sc-89e95305-2 innjCp">Enroll in our survival analysis tutorial, and you might just unwrap a free Orange T-shirt along with a hefty dose of data science cheer!</p><p class="sc-89e95305-2 innjCp"><strong>Jaka Kokošar</strong>, <!-- -->Dec 20, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/dask-survival-analysis/"><img alt="From Data Portals to Portals of Doom: Avoiding it with Dask" fetchpriority="high" width="1396" height="854" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>dask</span>, </span><span><span>survival analysis</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/dask-survival-analysis/">From Data Portals to Portals of Doom: Avoiding it with Dask</a></h2><p class="sc-89e95305-2 innjCp">Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source&#x27;s authenticity and enabling custom workflows.</p><p class="sc-89e95305-2 innjCp"><strong>Jaka Kokošar</strong>, <!-- -->Nov 08, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/dask-spectroscopy/"><img alt="Can Orange explore a 13 GB data set?" fetchpriority="high" width="591" height="352" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-dask-spectroscopy/__optimized-images__/du.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>dask</span>, </span><span><span>spectroscopy</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/dask-spectroscopy/">Can Orange explore a 13 GB data set?</a></h2><p class="sc-89e95305-2 innjCp">An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.</p><p class="sc-89e95305-2 innjCp"><strong>Marko Toplak</strong>, <!-- -->Oct 28, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/dask-all-folks-preparing-large-datasets/"><img alt="Dask all Folks: Preparing Large Datasets" loading="lazy" width="726" height="416" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-dask-all-folks/__optimized-images__/2023-10-24-dask-all-folks.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>dask</span>, </span><span><span>development</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/dask-all-folks-preparing-large-datasets/">Dask all Folks: Preparing Large Datasets</a></h2><p class="sc-89e95305-2 innjCp">Preparing large HDF5 datasets that load into Orange as on-disk data.</p><p class="sc-89e95305-2 innjCp"><strong>Noah Novšak</strong>, <!-- -->Oct 24, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/recap-of-26th-international-conference-on-discovery-science/"><img alt="Recap of 26th International Conference on Discovery Science" loading="lazy" width="484" height="413" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-porto-conference/__optimized-images__/2023-10-20-Porto-thumb.jpg"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>conference</span>, </span><span><span>research</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/recap-of-26th-international-conference-on-discovery-science/">Recap of 26th International Conference on Discovery Science</a></h2><p class="sc-89e95305-2 innjCp">Highlighting our papers from the International Conference on Discovery Science</p><p class="sc-89e95305-2 innjCp"><strong>Martin Špendl</strong>, <!-- -->Oct 20, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/fall-season-brings-fresh-content-to-the-introduction-to-data-science-series/"><img alt="Fall Season Brings Fresh Content to the Introduction to Data Science Series" loading="lazy" width="726" height="392" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-announcement-nomogram/__optimized-images__/2023-10-17-LogRegress.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>update</span>, </span><span><span>video series</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/fall-season-brings-fresh-content-to-the-introduction-to-data-science-series/">Fall Season Brings Fresh Content to the Introduction to Data Science Series</a></h2><p class="sc-89e95305-2 innjCp">Updates in the Introduction to Data Science Video Series, new video logistic regression nomogram.</p><p class="sc-89e95305-2 innjCp"><strong>Erika Funa</strong>, <!-- -->Oct 17, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/why-removing-features-isnt-enough/"><img alt="Why Removing Features Isn&#x27;t Enough" loading="lazy" width="607" height="542" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/why-removing-features-isnt-enough/">Why Removing Features Isn&#x27;t Enough</a></h2><p class="sc-89e95305-2 innjCp">Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-reweighing-as-a-preprocessor/"><img alt="Orange Fairness - Reweighing as a preprocessor" loading="lazy" width="856" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>reweighing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-reweighing-as-a-preprocessor/">Orange Fairness - Reweighing as a preprocessor</a></h2><p class="sc-89e95305-2 innjCp">Expanding on the Orange fairness Reweighing widget: using it as a preprocessor and integrating new fairness scoring metrics.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-reweighing-a-dataset/"><img alt="Orange Fairness - Reweighing a Dataset" loading="lazy" width="678" height="455" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>reweighing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-reweighing-a-dataset/">Orange Fairness - Reweighing a Dataset</a></h2><p class="sc-89e95305-2 innjCp">Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-equal-odds-postprocessing/"><img alt="Orange Fairness - Equal Odds Postprocessing" loading="lazy" width="596" height="277" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>equal odds postprocessing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-equal-odds-postprocessing/">Orange Fairness - Equal Odds Postprocessing</a></h2><p class="sc-89e95305-2 innjCp">Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model&#x27;s fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-adversarial-debiasing/"><img alt="Orange Fairness - Adversarial Debiasing" loading="lazy" width="470" height="513" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>adversarial debiasing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-adversarial-debiasing/">Orange Fairness - Adversarial Debiasing</a></h2><p class="sc-89e95305-2 innjCp">Learn how to use the Adversarial Debiasing model in Orange for fairer machine learning.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-dataset-bias/"><img alt="Orange Fairness - Dataset Bias" loading="lazy" width="562" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>dataset bias</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-dataset-bias/">Orange Fairness - Dataset Bias</a></h2><p class="sc-89e95305-2 innjCp">Orange now supports methods for detecting and mitigating bias in machine learning.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 18, 2023</p></li></ul><div class="sc-a2f31186-0 bdlyrE"><button class="sc-a2f31186-1 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Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"blogs":[{"x2images":false,"fullFilePath":"public/blog/2023-12-cookie-mining-2023/2023-12-cookie-mining-2023.md","thumbImage":{"width":512,"height":512,"src":"/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-mining-thumb.jpg"},"publicFilePath":"/blog/2023-12-cookie-mining-2023/","oldSlug":"2023-12-cookie-mining-2023","draft":false,"title":"Cookie Mining","date":"2023-12-22","tags":["text mining","images"],"author":"Blaž Zupan","longExcerpt":"A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering.","shortExcerpt":"A 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Our case study reveals that models trained without these attributes still produce biased results. This is due to feature correlations that indirectly capture the protected information. Our conclusion? You cannot sidestep the need for specialized fairness algorithms.","shortExcerpt":"Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation.","url":"why-removing-features-isnt-enough","oldUrl":"/blog/2023/2023-09-19-fairness-hiding-protected-attribute/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor.md","thumbImage":{"width":856,"height":500,"src":"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-reweighing-preprocessor/","oldSlug":"2023-09-19-fairness-reweighing-preprocessor","draft":false,"title":"Orange Fairness - Reweighing as a preprocessor","date":"2023-09-19 04:00:00+00:00","tags":["fairness","reweighing"],"author":"Žan Mervič","longExcerpt":"Diving deeper into the Orange fairness Reweighing widget, we explore its use as a preprocessor for models. Discover the new widgets and fairness scoring metrics; all illustrated using the German credit dataset, supplemented with visual insights through box plots.","shortExcerpt":"Expanding on the Orange fairness Reweighing widget: using it as a preprocessor and integrating new fairness scoring metrics.","url":"orange-fairness-reweighing-as-a-preprocessor","oldUrl":"/blog/2023/2023-09-19-fairness-reweighing-preprocessor/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-reweighing-dataset/2023-09-19-fairness-reweighing-dataset.md","thumbImage":{"width":678,"height":455,"src":"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-reweighing-dataset/","oldSlug":"2023-09-19-fairness-reweighing-dataset","draft":false,"title":"Orange Fairness - Reweighing a Dataset","date":"2023-09-19 03:00:00+00:00","tags":["fairness","reweighing"],"author":"Žan Mervič","longExcerpt":"Building on our exploration of the Orange fairness addon, this blog delves into the Reweighing widget. By adjusting weights for dataset instances, the widget addresses bias, focusing on underrepresented groups. Using the Compas dataset as an example, we demonstrate how bias decreases post-reweighting, presenting visual insights into the distribution of adjusted weights and their impact on dataset fairness.","shortExcerpt":"Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation.","url":"orange-fairness-reweighing-a-dataset","oldUrl":"/blog/2023/2023-09-19-fairness-reweighing-dataset/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing.md","thumbImage":{"width":596,"height":277,"src":"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"},"publicFilePath":"/blog/2023-09-fairness-equal-odds-postprocessing/","oldSlug":"2023-09-19-fairness-equal-odds-postprocessing","draft":false,"title":"Orange Fairness - Equal Odds Postprocessing","date":"2023-09-19 02:00:00+00:00","tags":["fairness","equal odds postprocessing"],"author":"Žan Mervič","longExcerpt":"In this blog, we delve into the Equal Odds Postprocessing widget, a tool designed to enhance fairness in machine learning models. We break down how the algorithm works by modifying predictions to meet Equalized Odds criteria. Using a real-world example with the German credit dataset, we demonstrate its efficacy in improving fairness metrics while marginally affecting accuracy.","shortExcerpt":"Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model's fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset.","url":"orange-fairness-equal-odds-postprocessing","oldUrl":"/blog/2023/2023-09-19-fairness-equal-odds-postprocessing/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing.md","thumbImage":{"width":470,"height":513,"src":"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-adversarial-debiasing/","oldSlug":"2023-09-19-fairness-adversarial-debiasing","draft":false,"title":"Orange Fairness - Adversarial Debiasing","date":"2023-09-19 01:00:00+00:00","tags":["fairness","adversarial debiasing"],"author":"Žan Mervič","longExcerpt":"This blog post focuses on the Adversarial Debiasing model in Orange, a tool for enhancing fairness in your machine learning algorithms. We will walk through how to use it and explain the trade-offs that come with using fairness algorithms.","shortExcerpt":"Learn how to use the Adversarial Debiasing model in Orange for fairer machine learning.","url":"orange-fairness-adversarial-debiasing","oldUrl":"/blog/2023/2023-09-19-fairness-adversarial-debiasing/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias.md","thumbImage":{"width":562,"height":546,"src":"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-dataset-bias/","oldSlug":"2023-09-18-fairness-dataset-bias","draft":false,"title":"Orange Fairness - Dataset Bias","date":"2023-09-18","tags":["fairness","dataset bias"],"author":"Žan Mervič","longExcerpt":"In an era where AI drives decisions impacting real lives, fairness in machine learning is paramount. Take the `Adult` dataset, which shows discrepancies in salary predictions based on gender. Addressing such concerns, Orange introduces a fairness add-on. Using new widgets, users can identify and mitigate biases in their datasets or model predictions.","shortExcerpt":"Orange now supports methods for detecting and mitigating bias in machine learning.","url":"orange-fairness-dataset-bias","oldUrl":"/blog/2023/2023-09-18-fairness-dataset-bias/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-08-a-dash-of-dask/2023-08-31-a-dash-of-dask.md","thumbImage":{"width":1800,"height":1204,"src":"/blog/2023-08-a-dash-of-dask/__optimized-images__/2023-08-31-dask-logistic-regression.png"},"publicFilePath":"/blog/2023-08-a-dash-of-dask/","oldSlug":"2023-08-31-a-dash-of-dask","draft":false,"title":"A dash of Dask","date":"2023-08-31","tags":["dask","development"],"author":"Noah Novšak","longExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.","shortExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data 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dask.","url":"orange-you-going-to-ask-about-dask","oldUrl":"/blog/2022/2022-12-13-dask-table/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2022-12-clustering-videos/2022-11-25-clustering-videos.md","thumbImage":{"width":300,"height":200,"src":"/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-video-series-clustering.png"},"publicFilePath":"/blog/2022-12-clustering-videos/","oldSlug":"2022-11-25-clustering-videos","draft":false,"title":"Videos on hierarchical clustering","date":"2022-11-25","tags":["education"],"author":"Blaž Zupan","longExcerpt":"Six new videos explaining hierarchical clustering in Orange are now online on YouTube.","shortExcerpt":"Six new videos explaining hierarchical clustering in Orange are now online on 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synchrotron.","url":"orange-in-a-synchrotron","oldUrl":"/blog/2022/2022-11-14-orange-in-a-syncrotron/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2022-10-quick-preview/2022-10-10-quick-preview.md","thumbImage":{"width":766,"height":478,"src":"/blog/2022-10-quick-preview/__optimized-images__/2022_10_10_quick_preview.png"},"publicFilePath":"/blog/2022-10-quick-preview/","oldSlug":"2022-10-10-quick-preview","draft":false,"title":"Quick previews in Orange widgets","date":"2022-10-10","tags":["widgets"],"author":"Janez Demšar","longExcerpt":"Widgets can now show a quick preview of their output data.","shortExcerpt":"An exciting new feature in 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Belgrade.","url":"explaining-models-workshop-in-belgrade","oldUrl":"/blog/2019/2019-11-20-belgrade-workshop/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-11-telekom-workshop/2019-11-15-telekom-workshop.md","thumbImage":{"width":300,"height":200,"src":"/blog/2019-11-telekom-workshop/2019-11-15_ws-title.JPG"},"publicFilePath":"/blog/2019-11-telekom-workshop/","oldSlug":"2019-11-15-telekom-workshop","draft":false,"title":"Explaining Customer Segments for Business","date":"2019-11-15","tags":["workshop","telco","clustering","nomogram"],"author":"Ajda Pretnar","longExcerpt":"Explaining customer base for businesses to make informed decisions. We present the case for Telco companies.","shortExcerpt":"Explaining customer base enables businesses to make informed decisions. We present the case for Telco companies.","url":"explaining-customer-segments-for-business","oldUrl":"/blog/2019/2019-11-15-telekom-workshop/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-of-carrots-horses-and-fear-of-heights.md","thumbImage":{"width":970,"height":646,"src":"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-horses.jpg"},"publicFilePath":"/blog/2019-09-of-carrots-horses-and-fear-of-heights/","oldSlug":"2019-09-29-of-carrots-horses-and-fear-of-heights","draft":false,"title":"Of Carrots and Horses and the Fear of Heights","date":"2019-09-29","tags":["statistical significance","hypothesis testing","p-value","multiple hypothesis testing"],"author":"Janez Demšar","longExcerpt":"A cautionary tale of imaginary friend who made too many hypotheses to test - and how Orange is no different","shortExcerpt":"A cautionary tale of imaginary friend who made too many hypotheses to test - and how Orange is no different","url":"of-carrots-and-horses-and-the-fear-of-heights","oldUrl":"/blog/2019/2019-09-29-of-carrots-horses-and-fear-of-heights/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-09-on-expected-vomiting-time/2019-09-28-on-expected-vomiting-time.md","thumbImage":{"width":1280,"height":831,"src":"/blog/2019-09-on-expected-vomiting-time/__optimized-images__/2019-09-28-vomiting-pumpkin.jpg"},"publicFilePath":"/blog/2019-09-on-expected-vomiting-time/","oldSlug":"2019-09-28-on-expected-vomiting-time","draft":false,"title":"On Expected Vomiting Time","date":"2019-09-28","tags":["model performance","confusion matrix","education"],"author":"Janez Demšar","longExcerpt":"A report of an interesting ending to a lecture about setting probability thresholds for predictive models","shortExcerpt":"Computing meaningful performance scores of models should be creative","url":"on-expected-vomiting-time","oldUrl":"/blog/2019/2019-09-28-on-expected-vomiting-time/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-08-pivot-table/2019-08-27-pivot-table.md","thumbImage":{"width":460,"height":280,"src":"/blog/2019-08-pivot-table/__optimized-images__/2019-08-27_pivot-preview.png"},"publicFilePath":"/blog/2019-08-pivot-table/","oldSlug":"2019-08-27-pivot-table","draft":false,"title":"Aggregate, Group By and Pivot with... 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It can be used to transform the data on-the-fly and use the output for downstream analysis.","shortExcerpt":"Orange's brand new Pivot Table widget with many aggregation and grouping functionalities.","url":"aggregate-group-by-and-pivot-with-pivot-table","oldUrl":"/blog/2019/2019-08-27-pivot-table/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-07-doctoral_summer_school/2019-07-26-doctoral_summer_school.md","thumbImage":{"width":340,"height":200,"src":"/blog/2019-07-doctoral_summer_school/__optimized-images__/2019-07-26_ekonomska_small.jpg"},"publicFilePath":"/blog/2019-07-doctoral_summer_school/","oldSlug":"2019-07-26-doctoral_summer_school","draft":false,"title":"Doctoral Summer School","date":"2019-07-26","tags":["workshop","education","data science","summer school"],"author":"Ajda Pretnar","longExcerpt":"For the second year in a row we took part in the Ljubljana Doctoral Summer School, organized by the School of Economics and Business.","shortExcerpt":"This year's Data Science course at the Doctoral Summer School.","url":"doctoral-summer-school","oldUrl":"/blog/2019/2019-07-26-doctoral_summer_school/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-07-scorange/2019-07-25-scorange.md","thumbImage":{"width":459,"height":280,"src":"/blog/2019-07-scorange/__optimized-images__/scorange-annotation-thumb.png"},"publicFilePath":"/blog/2019-07-scorange/","oldSlug":"2019-07-25-scorange","draft":false,"title":"Orange at ISMB/ECCB 2019","date":"2019-07-25","tags":["education","bioinformatics"],"author":"Blaž Zupan","longExcerpt":"","shortExcerpt":"One of Orange's latest features is its add-on for single-cell data analytics.","url":"orange-at-ismbeccb-2019","oldUrl":"/blog/2019/2019-07-25-scorange/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-07-data-science-made-easy/2019-07-02-data-science-made-easy.md","thumbImage":{"width":2000,"height":1183,"src":"/blog/2019-07-data-science-made-easy/twitter-police-workshop-picture.jpeg"},"publicFilePath":"/blog/2019-07-data-science-made-easy/","oldSlug":"2019-07-02-data-science-made-easy","draft":false,"title":"Data Science Made Easy: How To Identify Hate Comments with AI","date":"2019-07-02","tags":["education","text mining","workshop"],"author":"Dr. Sven Bingert \u0026 Steffen Rörtgen","longExcerpt":"How to teach text mining and data science to the 9th grade students in 60 minutes? 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We focused on predictive modeling and explorations of predictions.","shortExcerpt":"We held a short workshop for the participants of the 32nd Bled eConference.","url":"orange-at-32nd-bled-econference","oldUrl":"/blog/2019/2019-06-28-bled-econference/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-06-gene-expression-profiles-with-line-plot/2019-06-03-gene-expression-profiles-with-line-plot.md","thumbImage":{"width":1600,"height":892,"src":"/blog/2019-06-gene-expression-profiles-with-line-plot/__optimized-images__/gene-blog.png"},"publicFilePath":"/blog/2019-06-gene-expression-profiles-with-line-plot/","oldSlug":"2019-06-03-gene-expression-profiles-with-line-plot","draft":false,"title":"Gene Expression Profiles with Line Plot","date":"2019-06-03","tags":["bioinformatics","gene expression","line plot"],"author":"Ajda Pretnar","longExcerpt":"Line Plot shows profiles of data instances – each instance is a line in the plot and its profile are values across all variables in the data. 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We are getting rid of the infobox on the top of the control tab, and moving it to the status bar.","shortExcerpt":"We are constantly optimizing Orange's look-and-feel. New features in the status bar will simplify the user interface.","url":"the-changing-status-bar","oldUrl":"/blog/2019/3/8/the-changing-status-bar/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-3-single-cell-data-science-for-everyone/single-cell-data-science-for-everyone.md","thumbImage":{"width":450,"height":300,"src":"/blog/2019-3-single-cell-data-science-for-everyone/__optimized-images__/2019-pavia-blog.jpg"},"publicFilePath":"/blog/2019-3-single-cell-data-science-for-everyone/","oldSlug":"single-cell-data-science-for-everyone","draft":false,"title":"Single-Cell Data Science for Everyone","date":"2019-03-01","tags":["gene ontology","genomics","RNA-seq","scOrange","single cell"],"author":"Blaz Zupan","longExcerpt":"We have been in Pavia, Italy, to carry out a five-hour workshop covered both the essentials of data analysis and single cell analytics. The topics included working with marker genes, differential expression analysis, and interpretation of clusters through gene ontology analysis.","shortExcerpt":"We have been in Pavia, Italy, to carry out an introductory workshop on single-cell data science.","url":"single-cell-data-science-for-everyone","oldUrl":"/blog/2019/3/1/single-cell-data-science-for-everyone/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-1-the-mystery-of-test-and-score/the-mystery-of-test-and-score.md","thumbImage":{"width":445,"height":300,"src":"/blog/2019-1-the-mystery-of-test-and-score/__optimized-images__/ts-blog.png"},"publicFilePath":"/blog/2019-1-the-mystery-of-test-and-score/","oldSlug":"the-mystery-of-test-and-score","draft":false,"title":"The Mystery of Test \u0026 Score","date":"2019-01-28","tags":["cross validation","leave one out","LOO","test and score"],"author":"Ajda Pretnar","longExcerpt":"Test \u0026 Score widget is used for evaluating model performance, but what do the methods do? 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So why on Earth doesn’t Orange show them?","shortExcerpt":"Why doesn't Orange show p-values for correlations coefficients? 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lives!","date":"2010-03-04 01:56:00+00:00","tags":["debian","distribution","download","packaging"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"debian-repository-lives","oldUrl":"/blog/2010/03/04/debian-repository-lives/","_type":"blog"}],"tags":["orange3","visualization","workshop","education","addons","analysis","data","classification","examples","gsoc"]},"__N_SSG":true},"page":"/blog","query":{},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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href="/blog/cookie-mining-2023/"><img alt="Cookie Mining" fetchpriority="high" width="512" height="512" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-mining-thumb.jpg"/></a></figure><div class="sc-3c206b28-6 kZnHmz"><p class="sc-3c206b28-3 hlZWra"><span><span>text mining</span>, </span><span><span>images</span></span></p><h1 class="sc-89e95305-0 iNzSUu"><a href="/blog/cookie-mining-2023/">Cookie Mining</a></h1><p class="sc-89e95305-2 innjCp">A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering.</p><p class="sc-89e95305-2 innjCp"><strong>Blaž Zupan</strong>, <!-- -->Dec 22, 2023</p></div></div><ul class="sc-3c206b28-0 cBWUWw"><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/our-christmas-present-free-orange-t-shirt/"><img alt="Our Christmas Present: Free Orange T-Shirt!" fetchpriority="high" width="2000" height="2000" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-12-survival-analysis-tutorial/__optimized-images__/t-shirt.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>T-shirt</span>, </span><span><span>survival analysis</span>, </span><span><span>tutorial</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/our-christmas-present-free-orange-t-shirt/">Our Christmas Present: Free Orange T-Shirt!</a></h2><p class="sc-89e95305-2 innjCp">Enroll in our survival analysis tutorial, and you might just unwrap a free Orange T-shirt along with a hefty dose of data science cheer!</p><p class="sc-89e95305-2 innjCp"><strong>Jaka Kokošar</strong>, <!-- -->Dec 20, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/dask-survival-analysis/"><img alt="From Data Portals to Portals of Doom: Avoiding it with Dask" fetchpriority="high" width="1396" height="854" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>dask</span>, </span><span><span>survival analysis</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/dask-survival-analysis/">From Data Portals to Portals of Doom: Avoiding it with Dask</a></h2><p class="sc-89e95305-2 innjCp">Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source&#x27;s authenticity and enabling custom workflows.</p><p class="sc-89e95305-2 innjCp"><strong>Jaka Kokošar</strong>, <!-- -->Nov 08, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/dask-spectroscopy/"><img alt="Can Orange explore a 13 GB data set?" fetchpriority="high" width="591" height="352" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-dask-spectroscopy/__optimized-images__/du.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>dask</span>, </span><span><span>spectroscopy</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/dask-spectroscopy/">Can Orange explore a 13 GB data set?</a></h2><p class="sc-89e95305-2 innjCp">An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.</p><p class="sc-89e95305-2 innjCp"><strong>Marko Toplak</strong>, <!-- -->Oct 28, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/dask-all-folks-preparing-large-datasets/"><img alt="Dask all Folks: Preparing Large Datasets" loading="lazy" width="726" height="416" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-dask-all-folks/__optimized-images__/2023-10-24-dask-all-folks.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>dask</span>, </span><span><span>development</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/dask-all-folks-preparing-large-datasets/">Dask all Folks: Preparing Large Datasets</a></h2><p class="sc-89e95305-2 innjCp">Preparing large HDF5 datasets that load into Orange as on-disk data.</p><p class="sc-89e95305-2 innjCp"><strong>Noah Novšak</strong>, <!-- -->Oct 24, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/recap-of-26th-international-conference-on-discovery-science/"><img alt="Recap of 26th International Conference on Discovery Science" loading="lazy" width="484" height="413" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-porto-conference/__optimized-images__/2023-10-20-Porto-thumb.jpg"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>conference</span>, </span><span><span>research</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/recap-of-26th-international-conference-on-discovery-science/">Recap of 26th International Conference on Discovery Science</a></h2><p class="sc-89e95305-2 innjCp">Highlighting our papers from the International Conference on Discovery Science</p><p class="sc-89e95305-2 innjCp"><strong>Martin Špendl</strong>, <!-- -->Oct 20, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/fall-season-brings-fresh-content-to-the-introduction-to-data-science-series/"><img alt="Fall Season Brings Fresh Content to the Introduction to Data Science Series" loading="lazy" width="726" height="392" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-announcement-nomogram/__optimized-images__/2023-10-17-LogRegress.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>update</span>, </span><span><span>video series</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/fall-season-brings-fresh-content-to-the-introduction-to-data-science-series/">Fall Season Brings Fresh Content to the Introduction to Data Science Series</a></h2><p class="sc-89e95305-2 innjCp">Updates in the Introduction to Data Science Video Series, new video logistic regression nomogram.</p><p class="sc-89e95305-2 innjCp"><strong>Erika Funa</strong>, <!-- -->Oct 17, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/why-removing-features-isnt-enough/"><img alt="Why Removing Features Isn&#x27;t Enough" loading="lazy" width="607" height="542" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/why-removing-features-isnt-enough/">Why Removing Features Isn&#x27;t Enough</a></h2><p class="sc-89e95305-2 innjCp">Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-reweighing-as-a-preprocessor/"><img alt="Orange Fairness - Reweighing as a preprocessor" loading="lazy" width="856" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>reweighing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-reweighing-as-a-preprocessor/">Orange Fairness - Reweighing as a preprocessor</a></h2><p class="sc-89e95305-2 innjCp">Expanding on the Orange fairness Reweighing widget: using it as a preprocessor and integrating new fairness scoring metrics.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-reweighing-a-dataset/"><img alt="Orange Fairness - Reweighing a Dataset" loading="lazy" width="678" height="455" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>reweighing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-reweighing-a-dataset/">Orange Fairness - Reweighing a Dataset</a></h2><p class="sc-89e95305-2 innjCp">Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-equal-odds-postprocessing/"><img alt="Orange Fairness - Equal Odds Postprocessing" loading="lazy" width="596" height="277" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>equal odds postprocessing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-equal-odds-postprocessing/">Orange Fairness - Equal Odds Postprocessing</a></h2><p class="sc-89e95305-2 innjCp">Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model&#x27;s fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-adversarial-debiasing/"><img alt="Orange Fairness - Adversarial Debiasing" loading="lazy" width="470" height="513" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>adversarial debiasing</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-adversarial-debiasing/">Orange Fairness - Adversarial Debiasing</a></h2><p class="sc-89e95305-2 innjCp">Learn how to use the Adversarial Debiasing model in Orange for fairer machine learning.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 19, 2023</p></li><li class="sc-3c206b28-1 ciKioJ"><figure class="sc-3c206b28-2 bmXcZD"><a href="/blog/orange-fairness-dataset-bias/"><img alt="Orange Fairness - Dataset Bias" loading="lazy" width="562" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"/></a></figure><p class="sc-3c206b28-3 gRZIks"><span><span>fairness</span>, </span><span><span>dataset bias</span></span></p><h2 class="sc-89e95305-1 eaoeFt"><a href="/blog/orange-fairness-dataset-bias/">Orange Fairness - Dataset Bias</a></h2><p class="sc-89e95305-2 innjCp">Orange now supports methods for detecting and mitigating bias in machine learning.</p><p class="sc-89e95305-2 innjCp"><strong>Žan Mervič</strong>, <!-- -->Sep 18, 2023</p></li></ul><div class="sc-a2f31186-0 bdlyrE"><button class="sc-a2f31186-1 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This post outlines the specifics of the HDF5 format used by Orange and provides Python code that will help you prepare your own large datasets.","shortExcerpt":"Preparing large HDF5 datasets that load into Orange as on-disk data.","url":"dask-all-folks-preparing-large-datasets","oldUrl":"/blog/2023/2023-10-24-dask-all-folks/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-10-porto-conference/2023-10-20-porto-conference.md","thumbImage":{"width":484,"height":413,"src":"/blog/2023-10-porto-conference/__optimized-images__/2023-10-20-Porto-thumb.jpg"},"publicFilePath":"/blog/2023-10-porto-conference/","oldSlug":"2023-10-20-porto-conference","draft":false,"title":"Recap of 26th International Conference on Discovery Science","date":"2023-10-20","tags":["conference","research"],"author":"Martin Špendl","longExcerpt":"Two of our PhD students presented their work on the 26th International Conference on Discovery Science in Porto.","shortExcerpt":"Highlighting our papers from the International Conference on Discovery Science","url":"recap-of-26th-international-conference-on-discovery-science","oldUrl":"/blog/2023/2023-10-20-porto-conference/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-10-announcement-nomogram/2023-10-17-announcement-nomogram.md","thumbImage":{"width":726,"height":392,"src":"/blog/2023-10-announcement-nomogram/__optimized-images__/2023-10-17-LogRegress.png"},"publicFilePath":"/blog/2023-10-announcement-nomogram/","oldSlug":"2023-10-17-announcement-nomogram","draft":false,"title":"Fall Season Brings Fresh Content to the Introduction to Data Science Series","date":"2023-10-17","tags":["update","video series"],"author":"Erika Funa","longExcerpt":"Updates in the Introduction to Data Science Video Series, new video logistic regression nomogram.","shortExcerpt":"Updates in the Introduction to Data Science Video Series, new video logistic regression nomogram.","url":"fall-season-brings-fresh-content-to-the-introduction-to-data-science-series","oldUrl":"/blog/2023/2023-09-01-announcement-nomogram/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute.md","thumbImage":{"width":607,"height":542,"src":"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-hiding-protected-attribute/","oldSlug":"2023-09-19-fairness-hiding-protected-attribute","draft":false,"title":"Why Removing Features Isn't Enough","date":"2023-09-19 05:00:00+00:00","tags":["fairness"],"author":"Žan Mervič","longExcerpt":"In this blog, we confront the common misconception that merely removing a protected attribute from a dataset eliminates bias in model predictions. Our case study reveals that models trained without these attributes still produce biased results. This is due to feature correlations that indirectly capture the protected information. Our conclusion? You cannot sidestep the need for specialized fairness algorithms.","shortExcerpt":"Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation.","url":"why-removing-features-isnt-enough","oldUrl":"/blog/2023/2023-09-19-fairness-hiding-protected-attribute/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor.md","thumbImage":{"width":856,"height":500,"src":"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-reweighing-preprocessor/","oldSlug":"2023-09-19-fairness-reweighing-preprocessor","draft":false,"title":"Orange Fairness - Reweighing as a preprocessor","date":"2023-09-19 04:00:00+00:00","tags":["fairness","reweighing"],"author":"Žan Mervič","longExcerpt":"Diving deeper into the Orange fairness Reweighing widget, we explore its use as a preprocessor for models. Discover the new widgets and fairness scoring metrics; all illustrated using the German credit dataset, supplemented with visual insights through box plots.","shortExcerpt":"Expanding on the Orange fairness Reweighing widget: using it as a preprocessor and integrating new fairness scoring metrics.","url":"orange-fairness-reweighing-as-a-preprocessor","oldUrl":"/blog/2023/2023-09-19-fairness-reweighing-preprocessor/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-reweighing-dataset/2023-09-19-fairness-reweighing-dataset.md","thumbImage":{"width":678,"height":455,"src":"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-reweighing-dataset/","oldSlug":"2023-09-19-fairness-reweighing-dataset","draft":false,"title":"Orange Fairness - Reweighing a Dataset","date":"2023-09-19 03:00:00+00:00","tags":["fairness","reweighing"],"author":"Žan Mervič","longExcerpt":"Building on our exploration of the Orange fairness addon, this blog delves into the Reweighing widget. By adjusting weights for dataset instances, the widget addresses bias, focusing on underrepresented groups. Using the Compas dataset as an example, we demonstrate how bias decreases post-reweighting, presenting visual insights into the distribution of adjusted weights and their impact on dataset fairness.","shortExcerpt":"Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation.","url":"orange-fairness-reweighing-a-dataset","oldUrl":"/blog/2023/2023-09-19-fairness-reweighing-dataset/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing.md","thumbImage":{"width":596,"height":277,"src":"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"},"publicFilePath":"/blog/2023-09-fairness-equal-odds-postprocessing/","oldSlug":"2023-09-19-fairness-equal-odds-postprocessing","draft":false,"title":"Orange Fairness - Equal Odds Postprocessing","date":"2023-09-19 02:00:00+00:00","tags":["fairness","equal odds postprocessing"],"author":"Žan Mervič","longExcerpt":"In this blog, we delve into the Equal Odds Postprocessing widget, a tool designed to enhance fairness in machine learning models. We break down how the algorithm works by modifying predictions to meet Equalized Odds criteria. Using a real-world example with the German credit dataset, we demonstrate its efficacy in improving fairness metrics while marginally affecting accuracy.","shortExcerpt":"Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model's fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset.","url":"orange-fairness-equal-odds-postprocessing","oldUrl":"/blog/2023/2023-09-19-fairness-equal-odds-postprocessing/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing.md","thumbImage":{"width":470,"height":513,"src":"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-adversarial-debiasing/","oldSlug":"2023-09-19-fairness-adversarial-debiasing","draft":false,"title":"Orange Fairness - Adversarial Debiasing","date":"2023-09-19 01:00:00+00:00","tags":["fairness","adversarial debiasing"],"author":"Žan Mervič","longExcerpt":"This blog post focuses on the Adversarial Debiasing model in Orange, a tool for enhancing fairness in your machine learning algorithms. We will walk through how to use it and explain the trade-offs that come with using fairness algorithms.","shortExcerpt":"Learn how to use the Adversarial Debiasing model in Orange for fairer machine learning.","url":"orange-fairness-adversarial-debiasing","oldUrl":"/blog/2023/2023-09-19-fairness-adversarial-debiasing/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias.md","thumbImage":{"width":562,"height":546,"src":"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"},"publicFilePath":"/blog/2023-09-fairness-dataset-bias/","oldSlug":"2023-09-18-fairness-dataset-bias","draft":false,"title":"Orange Fairness - Dataset Bias","date":"2023-09-18","tags":["fairness","dataset bias"],"author":"Žan Mervič","longExcerpt":"In an era where AI drives decisions impacting real lives, fairness in machine learning is paramount. Take the `Adult` dataset, which shows discrepancies in salary predictions based on gender. Addressing such concerns, Orange introduces a fairness add-on. Using new widgets, users can identify and mitigate biases in their datasets or model predictions.","shortExcerpt":"Orange now supports methods for detecting and mitigating bias in machine learning.","url":"orange-fairness-dataset-bias","oldUrl":"/blog/2023/2023-09-18-fairness-dataset-bias/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2023-08-a-dash-of-dask/2023-08-31-a-dash-of-dask.md","thumbImage":{"width":1800,"height":1204,"src":"/blog/2023-08-a-dash-of-dask/__optimized-images__/2023-08-31-dask-logistic-regression.png"},"publicFilePath":"/blog/2023-08-a-dash-of-dask/","oldSlug":"2023-08-31-a-dash-of-dask","draft":false,"title":"A dash of Dask","date":"2023-08-31","tags":["dask","development"],"author":"Noah Novšak","longExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.","shortExcerpt":"Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data 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data.","url":"data-mining-covid-19-epidemics-part-1","oldUrl":"/blog/2020/2020-04-02-covid-19-basic/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2020-02-anaconda-navigator/2020-02-24-anaconda-navigator.md","thumbImage":{"width":300,"height":200,"src":"/blog/2020-02-anaconda-navigator/__optimized-images__/2020-02-24-navigator-small.png"},"publicFilePath":"/blog/2020-02-anaconda-navigator/","oldSlug":"2020-02-24-anaconda-navigator","draft":false,"title":"Installing with Anaconda Navigator","date":"2020-02-24","tags":["installation","anaconda","navigator"],"author":"Ajda Pretnar","longExcerpt":"Essential information for installing Orange via Anaconda Navigator.","shortExcerpt":"Essential information for installing Orange via Anaconda 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online.","url":"orange-lecture-notes","oldUrl":"/blog/2020/2020-02-08-lecture-notes/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2020-01-machine-anthropology/2020-01-29-machine-anthropology.md","thumbImage":{"width":300,"height":200,"src":"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-small.png"},"publicFilePath":"/blog/2020-01-machine-anthropology/","oldSlug":"2020-01-29-machine-anthropology","draft":false,"title":"What is Machine Anthropology?","date":"2020-01-29","tags":["machine","anthropology","big data","pivot table"],"author":"Ajda Pretnar","longExcerpt":"At the recent Machine Anthropology workshop we used Orange to explore anthropological data.","shortExcerpt":"At the recent Machine Anthropology workshop we used Orange to explore anthropological 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look-alikes.","url":"look-alike-images","oldUrl":"/blog/2020/2020-01-08-neighbors-images/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-11-belgrade-workshop/2019-11-20-belgrade-workshop.md","thumbImage":{"width":300,"height":200,"src":"/blog/2019-11-belgrade-workshop/__optimized-images__/2019-11-20_workshop_small.jpg"},"publicFilePath":"/blog/2019-11-belgrade-workshop/","oldSlug":"2019-11-20-belgrade-workshop","draft":false,"title":"Explaining Models: Workshop in Belgrade","date":"2019-11-20","tags":["workshop","belgrade","classification","nomogram","naive bayes","decision tree"],"author":"Ajda Pretnar","longExcerpt":"We explained how different models mean different things and how to interpret them at a recent tutorial in Belgrade.","shortExcerpt":"We explained how different models mean different things and how to interpret them at a recent tutorial in 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We present the case for Telco companies.","shortExcerpt":"Explaining customer base enables businesses to make informed decisions. 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It can be used to transform the data on-the-fly and use the output for downstream analysis.","shortExcerpt":"Orange's brand new Pivot Table widget with many aggregation and grouping functionalities.","url":"aggregate-group-by-and-pivot-with-pivot-table","oldUrl":"/blog/2019/2019-08-27-pivot-table/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-07-doctoral_summer_school/2019-07-26-doctoral_summer_school.md","thumbImage":{"width":340,"height":200,"src":"/blog/2019-07-doctoral_summer_school/__optimized-images__/2019-07-26_ekonomska_small.jpg"},"publicFilePath":"/blog/2019-07-doctoral_summer_school/","oldSlug":"2019-07-26-doctoral_summer_school","draft":false,"title":"Doctoral Summer School","date":"2019-07-26","tags":["workshop","education","data science","summer school"],"author":"Ajda Pretnar","longExcerpt":"For the second year in a row we took part in the Ljubljana Doctoral Summer School, organized by the School of Economics and Business.","shortExcerpt":"This year's Data Science course at the Doctoral Summer School.","url":"doctoral-summer-school","oldUrl":"/blog/2019/2019-07-26-doctoral_summer_school/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-07-scorange/2019-07-25-scorange.md","thumbImage":{"width":459,"height":280,"src":"/blog/2019-07-scorange/__optimized-images__/scorange-annotation-thumb.png"},"publicFilePath":"/blog/2019-07-scorange/","oldSlug":"2019-07-25-scorange","draft":false,"title":"Orange at ISMB/ECCB 2019","date":"2019-07-25","tags":["education","bioinformatics"],"author":"Blaž Zupan","longExcerpt":"","shortExcerpt":"One of Orange's latest features is its add-on for single-cell data analytics.","url":"orange-at-ismbeccb-2019","oldUrl":"/blog/2019/2019-07-25-scorange/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-07-data-science-made-easy/2019-07-02-data-science-made-easy.md","thumbImage":{"width":2000,"height":1183,"src":"/blog/2019-07-data-science-made-easy/twitter-police-workshop-picture.jpeg"},"publicFilePath":"/blog/2019-07-data-science-made-easy/","oldSlug":"2019-07-02-data-science-made-easy","draft":false,"title":"Data Science Made Easy: How To Identify Hate Comments with AI","date":"2019-07-02","tags":["education","text mining","workshop"],"author":"Dr. Sven Bingert \u0026 Steffen Rörtgen","longExcerpt":"How to teach text mining and data science to the 9th grade students in 60 minutes? 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We focused on predictive modeling and explorations of predictions.","shortExcerpt":"We held a short workshop for the participants of the 32nd Bled eConference.","url":"orange-at-32nd-bled-econference","oldUrl":"/blog/2019/2019-06-28-bled-econference/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-06-gene-expression-profiles-with-line-plot/2019-06-03-gene-expression-profiles-with-line-plot.md","thumbImage":{"width":1600,"height":892,"src":"/blog/2019-06-gene-expression-profiles-with-line-plot/__optimized-images__/gene-blog.png"},"publicFilePath":"/blog/2019-06-gene-expression-profiles-with-line-plot/","oldSlug":"2019-06-03-gene-expression-profiles-with-line-plot","draft":false,"title":"Gene Expression Profiles with Line Plot","date":"2019-06-03","tags":["bioinformatics","gene expression","line plot"],"author":"Ajda Pretnar","longExcerpt":"Line Plot shows profiles of data instances – each instance is a line in the plot and its profile are values across all variables in the data. 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We have shown how to work with geospatial data in Orange.","shortExcerpt":"In late March 2019, we have been invited to present Orange during a GIS Ostrava conference.","url":"orange-at-gis-ostrava","oldUrl":"/blog/2019/4/24/orange-at-gis-ostrava/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-3-the-changing-status-bar/the-changing-status-bar.md","thumbImage":{"width":681,"height":330,"src":"/blog/2019-3-the-changing-status-bar/__optimized-images__/changing-blog.png"},"publicFilePath":"/blog/2019-3-the-changing-status-bar/","oldSlug":"the-changing-status-bar","draft":false,"title":"The Changing Status Bar","date":"2019-03-08","tags":["release","Status Bar"],"author":"Blaz Zupan","longExcerpt":"We are constantly optimizing Orange's look-and-feel. New features in the status bar will simplify the user interface. We are getting rid of the infobox on the top of the control tab, and moving it to the status bar.","shortExcerpt":"We are constantly optimizing Orange's look-and-feel. New features in the status bar will simplify the user interface.","url":"the-changing-status-bar","oldUrl":"/blog/2019/3/8/the-changing-status-bar/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-3-single-cell-data-science-for-everyone/single-cell-data-science-for-everyone.md","thumbImage":{"width":450,"height":300,"src":"/blog/2019-3-single-cell-data-science-for-everyone/__optimized-images__/2019-pavia-blog.jpg"},"publicFilePath":"/blog/2019-3-single-cell-data-science-for-everyone/","oldSlug":"single-cell-data-science-for-everyone","draft":false,"title":"Single-Cell Data Science for Everyone","date":"2019-03-01","tags":["gene ontology","genomics","RNA-seq","scOrange","single cell"],"author":"Blaz Zupan","longExcerpt":"We have been in Pavia, Italy, to carry out a five-hour workshop covered both the essentials of data analysis and single cell analytics. The topics included working with marker genes, differential expression analysis, and interpretation of clusters through gene ontology analysis.","shortExcerpt":"We have been in Pavia, Italy, to carry out an introductory workshop on single-cell data science.","url":"single-cell-data-science-for-everyone","oldUrl":"/blog/2019/3/1/single-cell-data-science-for-everyone/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2019-1-the-mystery-of-test-and-score/the-mystery-of-test-and-score.md","thumbImage":{"width":445,"height":300,"src":"/blog/2019-1-the-mystery-of-test-and-score/__optimized-images__/ts-blog.png"},"publicFilePath":"/blog/2019-1-the-mystery-of-test-and-score/","oldSlug":"the-mystery-of-test-and-score","draft":false,"title":"The Mystery of Test \u0026 Score","date":"2019-01-28","tags":["cross validation","leave one out","LOO","test and score"],"author":"Ajda Pretnar","longExcerpt":"Test \u0026 Score widget is used for evaluating model performance, but what do the methods do? We explain cross validation, random sampling, leave one out and cross validation by feature in a few lines.","shortExcerpt":"Test \u0026 Score widget is used for evaluating model performance, but what do the methods do? 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So why on Earth doesn’t Orange show them?","shortExcerpt":"Why doesn't Orange show p-values for correlations coefficients? 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22:10:00+00:00","tags":["gsoc","matrixfactorization"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"orange-gsoc-mf-techniques-for-data-mining","oldUrl":"/blog/2011/06/24/orange-gsoc-mf-techniques-for-data-mining/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-06-orange-t-shirts/orange-t-shirts.md","thumbImage":null,"publicFilePath":"/blog/2011-06-orange-t-shirts/","oldSlug":"orange-t-shirts","draft":false,"title":"Orange T-shirts","date":"2011-06-24 10:43:00+00:00","tags":["orange3"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"orange-t-shirts","oldUrl":"/blog/2011/06/24/orange-t-shirts/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-06-contact-us/contact-us.md","thumbImage":null,"publicFilePath":"/blog/2011-06-contact-us/","oldSlug":"contact-us","draft":false,"title":"Contact us!","date":"2011-06-14 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08:22:00+00:00","tags":["gsoc"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"accepted-gsoc-2011-students-announced","oldUrl":"/blog/2011/04/25/accepted-gsoc-2011-students-announced/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-04-student-application-period-for-gsoc-2011-has-ended/student-application-period-for-gsoc-2011-has-ended.md","thumbImage":null,"publicFilePath":"/blog/2011-04-student-application-period-for-gsoc-2011-has-ended/","oldSlug":"student-application-period-for-gsoc-2011-has-ended","draft":false,"title":"Student application period for GSoC 2011 has ended","date":"2011-04-08 07:53:00+00:00","tags":["gsoc"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"student-application-period-for-gsoc-2011-has-ended","oldUrl":"/blog/2011/04/08/student-application-period-for-gsoc-2011-has-ended/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-03-our-gsoc-2011-posters/our-gsoc-2011-posters.md","thumbImage":null,"publicFilePath":"/blog/2011-03-our-gsoc-2011-posters/","oldSlug":"our-gsoc-2011-posters","draft":false,"title":"Our GSoC 2011 posters","date":"2011-03-29 10:12:00+00:00","tags":["gsoc"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"our-gsoc-2011-posters","oldUrl":"/blog/2011/03/29/our-gsoc-2011-posters/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-03-data-loading-speedups/data-loading-speedups.md","thumbImage":null,"publicFilePath":"/blog/2011-03-data-loading-speedups/","oldSlug":"data-loading-speedups","draft":false,"title":"Data loading speedups","date":"2011-03-28 13:04:00+00:00","tags":["dataloading","performance"],"author":"MARKO","longExcerpt":"","shortExcerpt":"","url":"data-loading-speedups","oldUrl":"/blog/2011/03/28/data-loading-speedups/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-03-orange-has-been-accepted-into-gsoc-2011/orange-has-been-accepted-into-gsoc-2011.md","thumbImage":null,"publicFilePath":"/blog/2011-03-orange-has-been-accepted-into-gsoc-2011/","oldSlug":"orange-has-been-accepted-into-gsoc-2011","draft":false,"title":"Orange has been accepted into GSoC 2011","date":"2011-03-18 21:10:00+00:00","tags":["gsoc"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"orange-has-been-accepted-into-gsoc-2011","oldUrl":"/blog/2011/03/18/orange-has-been-accepted-into-gsoc-2011/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2011-02-biolab-retreat-februar-2011/biolab-retreat-februar-2011.md","thumbImage":null,"publicFilePath":"/blog/2011-02-biolab-retreat-februar-2011/","oldSlug":"biolab-retreat-februar-2011","draft":false,"title":"Biolab retreat Februar 2011","date":"2011-02-11 13:41:00+00:00","tags":["bohinj","orange25","retreat"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"biolab-retreat-februar-2011","oldUrl":"/blog/2011/02/11/biolab-retreat-februar-2011/","_type":"blog"},{"x2images":false,"fullFilePath":"public/blog/2010-03-debian-repository-lives/debian-repository-lives.md","thumbImage":null,"publicFilePath":"/blog/2010-03-debian-repository-lives/","oldSlug":"debian-repository-lives","draft":false,"title":"Debian repository lives!","date":"2010-03-04 01:56:00+00:00","tags":["debian","distribution","download","packaging"],"author":"BIOLAB","longExcerpt":"","shortExcerpt":"","url":"debian-repository-lives","oldUrl":"/blog/2010/03/04/debian-repository-lives/","_type":"blog"}],"tags":["orange3","visualization","workshop","education","addons","analysis","data","classification","examples","gsoc"]},"__N_SSG":true},"page":"/blog","query":{},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, download, pypi, release, update</strong></p><h1 class="sc-c67a4900-0 loxVcf">Installing Add-ons Works Again</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 23, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Dear Orange users,</p>
 <p>Some of you might have an issue installing add-ons with the following issue popping up:</p>
 <p><code>xmlrpc.client.Fault: &lt;Fault -32601: &#x27;server error; requested method not found&#x27;&gt;</code></p>
@@ -157,4 +157,4 @@
 <p>In order to make the add-on installer work (again), please <a href="https://orange.biolab.si/download/">download</a> the latest version of Orange (3.13.0).</p>
 <p>We apologize for any inconvenience and wish you a fruitful data analysis in the future.</p>
 <p>Yours truly,</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>installation, anaconda, navigator</strong></p><h1 class="sc-c67a4900-0 loxVcf">Installing with Anaconda Navigator</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 24, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We are fortunate enough to be featured on the front page of Anaconda Navigator, a graphical user interface for conda package management. Orange has been a conda package for some time now, since this is the easiest way to provide pre-compiled packages for Windows. And since most of our user base uses Windows, this was the way to go.</p>
 <p>If you are an avid Anaconda user and you wish to install Orange with Anaconda Navigator, there are some steps you need to take to ensure everything works correctly. First, install Orange in the home screen. Once Orange is installed, it will appear at the top.</p>
 <a href="/blog/2020-02-anaconda-navigator/__optimized-images__/2020-02-24-navig2.png" data-gallery="true"><img alt="" loading="lazy" width="1099" height="676" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-02-anaconda-navigator/__optimized-images__/2020-02-24-navig2.png"/></a>
@@ -161,4 +161,4 @@
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 <p>That&#x27;s it. Your channel is set. Now you can update Orange to the latest version and use add-on that require pre-compiled packages, such as Text, Network, and so on.</p>
 <a href="/blog/2020-02-anaconda-navigator/__optimized-images__/2020-02-24-env4.png" data-gallery="true"><img alt="" loading="lazy" width="1099" height="676" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-02-anaconda-navigator/__optimized-images__/2020-02-24-env4.png"/></a>
-<p>Make sure to regularly update Orange to get the latest bug fixes and features.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-02-24","draft":false,"title":"Installing with Anaconda Navigator","thumbImage":"2020-02-24-navigator-small.png","frontPageImage":"2020-02-24-navigator-small.png","blog":["installation","anaconda","navigator"],"shortExcerpt":"Essential information for installing Orange via Anaconda Navigator.","longExcerpt":"Essential information for installing Orange via Anaconda Navigator.","oldUrl":"/blog/2020/2020-02-24-anaconda-navigator/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We are fortunate enough to be featured on the front page of Anaconda Navigator, a graphical user interface for conda package management. 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+<p>Make sure to regularly update Orange to get the latest bug fixes and features.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-02-24","draft":false,"title":"Installing with Anaconda Navigator","thumbImage":"2020-02-24-navigator-small.png","frontPageImage":"2020-02-24-navigator-small.png","blog":["installation","anaconda","navigator"],"shortExcerpt":"Essential information for installing Orange via Anaconda Navigator.","longExcerpt":"Essential information for installing Orange via Anaconda Navigator.","oldUrl":"/blog/2020/2020-02-24-anaconda-navigator/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We are fortunate enough to be featured on the front page of Anaconda Navigator, a graphical user interface for conda package management. 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diff --git a/blog/interactive-k-means/index.html b/blog/interactive-k-means/index.html
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--- a/blog/interactive-k-means/index.html
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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Interactive k-Means</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Interactive k-Means"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-08-12 12:45:36+00:00"/><meta property="article:author" content="PRIMOZGODEC"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="education"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="gsoc2016"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="23"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Interactive k-Means</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Interactive k-Means"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-08-12 12:45:36+00:00"/><meta property="article:author" content="PRIMOZGODEC"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="education"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="gsoc2016"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="23"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -178,4 +178,4 @@
 <p>Interactive k-means works great in combination with <a href="http://orange3.readthedocs.io/en/latest/widgets/data/paintdata.html">Paint Data</a>. There, we can design data sets where k-mains fails, and observe why.</p>
 <a href="/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-failt.png" data-gallery="true"><img alt="" loading="lazy" width="1653" height="972" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-failt.png"/></a>
 <p>We could also design data sets where k-means fails under specific initialization of centroids. Ah, I did not tell you that you can freely move the centroids and then restart the algorithm. Below we show the case of centroid initialization and how this leads to non-optimal clustering.</p>
-<a href="/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-f-join.png" data-gallery="true"><img alt="" loading="lazy" width="1826" height="641" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-f-join.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"PRIMOZGODEC","date":"2016-08-12 12:45:36+00:00","draft":false,"title":"Interactive k-Means","blog":["addons","clustering","education","gsoc","gsoc2016","orange3","widget"],"oldUrl":"/blog/2016/08/12/interactive-k-means/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    ol: \"ol\",\n    li: \"li\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As a part of my \", _jsx(_components.a, {\n        href: \"https://summerofcode.withgoogle.com/\",\n        children: \"Google Summer of Code\"\n      }), \" project I started developing educational widgets and assemble them in an \", _jsx(_components.a, {\n        href: \"http://orange3-educational.readthedocs.io/\",\n        children: \"Educational Add-On\"\n      }), \" for \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \". Educational widgets can be used by students to understand how some key data mining algorithms work and by teachers to demonstrate the working of these algorithms.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here I describe an educational widget for interactive \", _jsxs(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-means_clustering\",\n        children: [_jsx(_components.em, {\n          children: \"k\"\n        }), \"-means clustering\"]\n      }), \", an algorithm that splits the data into clusters by finding cluster centroids such that the distance between data points and their corresponding centroid is minimized. Number of clusters in \", _jsx(_components.em, {\n        children: \"k\"\n      }), \"-means algorithm is denoted with \", _jsx(_components.em, {\n        children: \"k\"\n      }), \" and has to be specified manually.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The algorithm starts by randomly positioning the centroids in the data space, and then improving their position by repetition of the following two steps:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Assign each point to the closest centroid.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Move centroids to the mean position of points assigned to the centroid.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget needs the data that can come from \", _jsx(_components.a, {\n        href: \"http://orange3.readthedocs.io/en/latest/widgets/data/file.html\",\n        children: \"File\"\n      }), \" widget, and outputs the information on clusters (Annotated Data) and centroids:\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmans_shema.png\",\n      alt: \"\",\n      width: \"520\",\n      height: \"162\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Educational widget for k-means works finds clusters based on two continuous features only, all other features are ignored. The screenshot shows plot of an Iris data set and clustering with \", _jsx(_components.em, {\n        children: \"k\"\n      }), \"=3. That is partially cheating, because we know that iris data set has three classes, so that we can check if clusters correspond well to original classes:\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans2-stamped.png\",\n      alt: \"\",\n      width: \"903\",\n      height: \"640\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select two features that are used in \", _jsx(_components.em, {\n          children: \"k\"\n        }), \"-means\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set number of centroids\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Randomize positions of centroids\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Show lines between centroids and corresponding points\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Perform the algorithm step by step. Reassign membership connects points to nearest centroid, Recompute centroids moves centroids.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Step back in the algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set speed of automatic stepping\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Perform the whole algorithm as fast preview\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \" Anytime we can change number of centroids with spinner or with click in desired position in the graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If we want to see the correspondence of clusters that are denoted by \", _jsx(_components.em, {\n        children: \"k\"\n      }), \"-means and classes, we can open \", _jsx(_components.em, {\n        children: \"Data Table\"\n      }), \" widget where we see that all \", _jsx(_components.em, {\n        children: \"Iris-setosas\"\n      }), \" are clustered in one cluster and but there are just few \", _jsx(_components.em, {\n        children: \"Iris-versicolor\"\n      }), \" that are classified is same cluster together with \", _jsx(_components.em, {\n        children: \"Iris-virginica\"\n      }), \" and vice versa.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans3-4.png\",\n      alt: \"\",\n      width: \"1503\",\n      height: \"893\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Interactive k-means works great in combination with \", _jsx(_components.a, {\n        href: \"http://orange3.readthedocs.io/en/latest/widgets/data/paintdata.html\",\n        children: \"Paint Data\"\n      }), \". There, we can design data sets where k-mains fails, and observe why.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-failt.png\",\n      alt: \"\",\n      width: \"1653\",\n      height: \"972\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We could also design data sets where k-means fails under specific initialization of centroids. Ah, I did not tell you that you can freely move the centroids and then restart the algorithm. Below we show the case of centroid initialization and how this leads to non-optimal clustering.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-f-join.png\",\n      alt: \"\",\n      width: \"1826\",\n      height: \"641\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"interactive-k-means"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<a href="/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-f-join.png" data-gallery="true"><img alt="" loading="lazy" width="1826" height="641" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-f-join.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"PRIMOZGODEC","date":"2016-08-12 12:45:36+00:00","draft":false,"title":"Interactive k-Means","blog":["addons","clustering","education","gsoc","gsoc2016","orange3","widget"],"oldUrl":"/blog/2016/08/12/interactive-k-means/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    ol: \"ol\",\n    li: \"li\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As a part of my \", _jsx(_components.a, {\n        href: \"https://summerofcode.withgoogle.com/\",\n        children: \"Google Summer of Code\"\n      }), \" project I started developing educational widgets and assemble them in an \", _jsx(_components.a, {\n        href: \"http://orange3-educational.readthedocs.io/\",\n        children: \"Educational Add-On\"\n      }), \" for \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \". Educational widgets can be used by students to understand how some key data mining algorithms work and by teachers to demonstrate the working of these algorithms.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here I describe an educational widget for interactive \", _jsxs(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-means_clustering\",\n        children: [_jsx(_components.em, {\n          children: \"k\"\n        }), \"-means clustering\"]\n      }), \", an algorithm that splits the data into clusters by finding cluster centroids such that the distance between data points and their corresponding centroid is minimized. Number of clusters in \", _jsx(_components.em, {\n        children: \"k\"\n      }), \"-means algorithm is denoted with \", _jsx(_components.em, {\n        children: \"k\"\n      }), \" and has to be specified manually.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The algorithm starts by randomly positioning the centroids in the data space, and then improving their position by repetition of the following two steps:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Assign each point to the closest centroid.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Move centroids to the mean position of points assigned to the centroid.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget needs the data that can come from \", _jsx(_components.a, {\n        href: \"http://orange3.readthedocs.io/en/latest/widgets/data/file.html\",\n        children: \"File\"\n      }), \" widget, and outputs the information on clusters (Annotated Data) and centroids:\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmans_shema.png\",\n      alt: \"\",\n      width: \"520\",\n      height: \"162\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Educational widget for k-means works finds clusters based on two continuous features only, all other features are ignored. The screenshot shows plot of an Iris data set and clustering with \", _jsx(_components.em, {\n        children: \"k\"\n      }), \"=3. That is partially cheating, because we know that iris data set has three classes, so that we can check if clusters correspond well to original classes:\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans2-stamped.png\",\n      alt: \"\",\n      width: \"903\",\n      height: \"640\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select two features that are used in \", _jsx(_components.em, {\n          children: \"k\"\n        }), \"-means\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set number of centroids\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Randomize positions of centroids\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Show lines between centroids and corresponding points\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Perform the algorithm step by step. Reassign membership connects points to nearest centroid, Recompute centroids moves centroids.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Step back in the algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set speed of automatic stepping\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Perform the whole algorithm as fast preview\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \" Anytime we can change number of centroids with spinner or with click in desired position in the graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If we want to see the correspondence of clusters that are denoted by \", _jsx(_components.em, {\n        children: \"k\"\n      }), \"-means and classes, we can open \", _jsx(_components.em, {\n        children: \"Data Table\"\n      }), \" widget where we see that all \", _jsx(_components.em, {\n        children: \"Iris-setosas\"\n      }), \" are clustered in one cluster and but there are just few \", _jsx(_components.em, {\n        children: \"Iris-versicolor\"\n      }), \" that are classified is same cluster together with \", _jsx(_components.em, {\n        children: \"Iris-virginica\"\n      }), \" and vice versa.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans3-4.png\",\n      alt: \"\",\n      width: \"1503\",\n      height: \"893\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Interactive k-means works great in combination with \", _jsx(_components.a, {\n        href: \"http://orange3.readthedocs.io/en/latest/widgets/data/paintdata.html\",\n        children: \"Paint Data\"\n      }), \". There, we can design data sets where k-mains fails, and observe why.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-failt.png\",\n      alt: \"\",\n      width: \"1653\",\n      height: \"972\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We could also design data sets where k-means fails under specific initialization of centroids. Ah, I did not tell you that you can freely move the centroids and then restart the algorithm. Below we show the case of centroid initialization and how this leads to non-optimal clustering.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-interactive-k-means/__optimized-images__/kmeans-f-join.png\",\n      alt: \"\",\n      width: \"1826\",\n      height: \"641\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"interactive-k-means"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/interval-sliders/index.html b/blog/interval-sliders/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>development</strong></p><h1 class="sc-c67a4900-0 loxVcf">Interval Sliders</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 10, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><a href="/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-heatmap.png" data-gallery="true"><img alt="" loading="lazy" width="1326" height="962" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-heatmap.png"/></a>
 <p>This post is not about the Heat Map widget. It&#x27;s about the new double-handled slider at the top of the heatmap. Not about what it does, but about how it does it. The post should be interesting to readers with some knowledge of Qt. Others, who won&#x27;t understand it, are expected to be impressed by the complexity of implementing something as simple as adding another handle to a slider. On the other hand (wait for the pun!), this complexity is not so surprising: attaching another, fully functional hand to a human would be quite a feat, too.</p>
 <h2>Why do we need it?</h2>
@@ -211,4 +211,4 @@ <h2>How do we use interval sliders?</h2>
 <pre><code class="hljs language-python">slider = IntervalSlider(low, high * <span class="hljs-number">100</span>, minimum=<span class="hljs-number">0</span>, maximum=<span class="hljs-number">100</span>)
 self.slider.intervalChanged.connect(self.__on_slider_moved)
 </code></pre>
-<p>Done.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2023-03-10","draft":false,"title":"Interval Sliders","thumbImage":"2023-03-10-interval-slider-thumb.png","frontPageImage":"2023-03-10-interval-slider-thumb.png","blog":["development"],"shortExcerpt":"What does it take to add a second handle to a slider.","longExcerpt":"What does it take to add a second handle to a slider.","oldUrl":"/blog/2023/2023-03-10-interval-slider/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\",\n    code: \"code\",\n    pre: \"pre\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-interval-slider/2023-03-10-heatmap.png\",\n      width: \"1326\",\n      height: \"962\",\n      src: \"/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-heatmap.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This post is not about the Heat Map widget. It's about the new double-handled slider at the top of the heatmap. Not about what it does, but about how it does it. The post should be interesting to readers with some knowledge of Qt. Others, who won't understand it, are expected to be impressed by the complexity of implementing something as simple as adding another handle to a slider. On the other hand (wait for the pun!), this complexity is not so surprising: attaching another, fully functional hand to a human would be quite a feat, too.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why do we need it?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"That is, why we need another handle on slider, not another hand. (Though an extra hand would be useful, too - how cool would it be to take a sip of tea while typing?)\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A user posted \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/issues/6323\",\n        children: \"an issue\"\n      }), \", which he called \\\"\", _jsx(_components.em, {\n        children: \"Not a problem. More a frustration\"\n      }), \"\\\". Some widgets from the Text add-on allow the user to specify a range of values, for instance for filtering, which they do with a pair of spin boxes (those controls where you can enter a number manually or change it by pressing little up and down button on their right) or a pair of sliders. He suggested using a single slider with two handles. He noted that Orange currently doesn't have such a control in its repertoire.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With a bit of exaggeration, I use such sliders almost daily to specify the price range when ordering bicycle parts, tools and clothes. While I'm no stranger to such sliders, I don't particularly like them. Entering numbers manually is much easier. But then ... they could be useful in cases where numbers are not intepretable, so sliding a handle is more natural than guessing random numbers. A good example is setting scales for color palettes: the user has no idea which particular number will make the image look good.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Drawing native-looking widgets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Problem: Qt doesn't have such widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Qt is great - consistent, fast, powerful, and it has native look on all platforms. But if you want to make any small change to some element, its painting falls back to a Qt-based implementation which looks ... not only out of place but, well, ugly. See what becomes of a push button if you would, for whatever reason, want to increase its font size.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-interval-slider/2023-03-10-ugly-button.png\",\n      width: \"548\",\n      height: \"92\",\n      src: \"/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-ugly-button.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, can we create a double-handled slider that looks as it should? Not with some ugly square-ish handles?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"OP provided us with a \", _jsx(_components.a, {\n        href: \"https://stackoverflow.com/a/62665367\",\n        children: \"link\"\n      }), \" to implementation of such a slider. Our version is different, but based on the same trick.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Widget is (re)painted upon receiving a \", _jsx(_components.a, {\n        href: \"https://doc.qt.io/qt-6/qwidget.html#paintEvent\",\n        children: _jsx(_components.code, {\n          children: \"paintEvent\"\n        })\n      }), \". It must construct a painter, initialize it with proper options and paint whatever needs to be painted. \", _jsx(_components.code, {\n        children: \"QPushButton\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QCheckBox\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QLineEdit\"\n      }), \" ... and many other widgets must look different on different platforms. 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(The \", _jsx(_components.a, {\n        href: \"https://github.com/radekp/qt/blob/master/src/gui/widgets/qpushbutton.cpp#L444\",\n        children: \"actual code\"\n      }), \" uses a convenience class \", _jsx(_components.code, {\n        children: \"QStylePainter\"\n      }), \"). \", _jsx(_components.code, {\n        children: \"drawControl\"\n      }), \" then draws all elements of the button -- its text, icon, any frames, borders.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Unlike \", _jsx(_components.code, {\n        children: \"QPushButton\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QSlider\"\n      }), \" is a complex control, consisting of subcontrols that represent the slider's groove, tickmarks and handle. Complex controls are not painted through \", _jsx(_components.code, {\n        children: \"QStyle.drawControl\"\n      }), \" but through another method, \", _jsx(_components.code, {\n        children: \"drawComplexControl\"\n      }), \". Its signature is similar, but \", _jsx(_components.code, {\n        children: \"option\"\n      }), \" has a mask \", _jsx(_components.code, {\n        children: \"subcontrols\"\n      }), \", which define the active subcontrols. Inactive controls are not painted and the user can't interact with them.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The trick to paint a two-handled slider is to call \", _jsx(_components.code, {\n        children: \"drawComplexControl\"\n      }), \" multiple times: first to draw the groove and tickmarks, and then twice, once for each handle. In the code fragment below, \", _jsx(_components.code, {\n        children: \"self.opt\"\n      }), \" is a \", _jsx(_components.code, {\n        children: \"QSliderOption\"\n      }), \". Method \", _jsx(_components.code, {\n        children: \"drawComplexControl\"\n      }), \" will put a handle at the position stored in \", _jsx(_components.code, {\n        children: \"self.opt.sliderPosition\"\n      }), \", hence we iterate it through \", _jsx(_components.code, {\n        children: \"self._pos\"\n      }), \", which stores the positions of our two handles.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Draw groove and tickmarks\"\n        }), \"\\nself.opt.subControls = QStyle.SC_SliderGroove | QStyle.SC_SliderTickmarks\\nself.style().drawComplexControl(QStyle.CC_Slider, self.opt, painter)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Draw handles\"\n        }), \"\\nself.opt.subControls = QStyle.SC_SliderHandle\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" self.opt.sliderPosition \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" self._pos:\\n    self.style().drawComplexControl(QStyle.CC_Slider, self.opt, painter)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L206\",\n        children: \"code\"\n      }), \" adds some overhead (initializing options) and marks the part of the groove between the two handles in blue.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Controlling controls\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Drawing a native looking widget is only one part of the job. What happens when the user clicks it? The widget receives a \", _jsx(_components.code, {\n        children: \"mousePressEvent\"\n      }), \" - but which part of the widget was clicked? This is known only by the one who's drawn the control: \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.code, {\n        children: \"QStyle\"\n      }), \" implements several methods needed for interaction with complex controls. One is already used in \", _jsx(_components.code, {\n        children: \"paintEvent\"\n      }), \": we call \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \"'s \", _jsx(_components.code, {\n        children: \"subControlRect(QStyle.CC_Slider, self.opt, QStyle.SC_SliderHandle)\"\n      }), \" to \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L223\",\n        children: \"obtain the logical coordinates of each handle\"\n      }), \" in order to mark the interval between them.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"args = QStyle.CC_Slider, self.opt, QStyle.SC_SliderHandle\\nself.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\nx_left_handle = self.style().subControlRect(*args).right()\\nself.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]\\nx_right_handle = self.style().subControlRect(*args).left()\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.code, {\n        children: \"mousePressEvent\"\n      }), \", we call \", _jsx(_components.code, {\n        children: \"hitTestComplexControl(QStyle.CC_Slider, self.opt, event.pos(), self)\"\n      }), \", which tells us which subcontrol (if any) of the slider contains \", _jsx(_components.code, {\n        children: \"event.pos()\"\n      }), \". As in \", _jsx(_components.code, {\n        children: \"paintEvent\"\n      }), \", we set \", _jsx(_components.code, {\n        children: \"self.opt.sliderPosition\"\n      }), \" to each handle's position in turn, to see if \", _jsx(_components.code, {\n        children: \"hitTestComplexControl\"\n      }), \" returns \", _jsx(_components.code, {\n        children: \"QStyle.SC_SliderHandle\"\n      }), \" for any of them.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"mousePressEvent\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-params\",\n          children: \"self, event: QMouseEvent\"\n        }), \") -\u003e \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"None\"\n        }), \":\\n    self._dragged = self.NoHandle\\n    args = (QStyle.CC_Slider, self.opt, event.pos(), self)\\n    self.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" self.style().hitTestComplexControl(*args) == QStyle.SC_SliderHandle:\\n        self._dragged |= self.LowHandle\\n    self.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" self.style().hitTestComplexControl(*args) == QStyle.SC_SliderHandle:\\n        self._dragged |= self.HighHandle\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" self._dragged != self.NoHandle:\\n        self.sliderPressed.emit(self._dragged)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L156\",\n        children: \"code\"\n      }), \" then records which handles are being dragged (if they overlap, both are marked as being dragged \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L175\",\n        children: \"until we see whether the user drags left or right\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The third method of \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \", which we use in \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L169\",\n        children: _jsx(_components.code, {\n          children: \"mouseEvent\"\n        })\n      }), \" determines the slider value from handles position - which is again known only by the class that paints a platform-dependent slider. \", _jsx(_components.code, {\n        children: \"sliderValueFromPosition\"\n      }), \" is given the slider span, (mouse) position and the total bounding rectangle of the slider, and returns the slider's value for that position.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"distance = self.opt.maximum - self.opt.minimum\\npos = self.style().sliderValueFromPosition(\\n    \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", distance, event.pos().x(), self.rect().width())\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"How do we use interval sliders?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Using interval sliders is as trivial as using ordinary \", _jsx(_components.code, {\n        children: \"QSliders\"\n      }), \": initialize it with starting position, interval and options (if any), and connect the necessary signals.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"slider = IntervalSlider(low, high * \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"100\"\n        }), \", minimum=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", maximum=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"100\"\n        }), \")\\nself.slider.intervalChanged.connect(self.__on_slider_moved)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Done.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":565,"height":369,"src":"/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-interval-slider-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"interval-sliders"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Done.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2023-03-10","draft":false,"title":"Interval Sliders","thumbImage":"2023-03-10-interval-slider-thumb.png","frontPageImage":"2023-03-10-interval-slider-thumb.png","blog":["development"],"shortExcerpt":"What does it take to add a second handle to a slider.","longExcerpt":"What does it take to add a second handle to a slider.","oldUrl":"/blog/2023/2023-03-10-interval-slider/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\",\n    code: \"code\",\n    pre: \"pre\",\n    span: \"span\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-interval-slider/2023-03-10-heatmap.png\",\n      width: \"1326\",\n      height: \"962\",\n      src: \"/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-heatmap.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This post is not about the Heat Map widget. It's about the new double-handled slider at the top of the heatmap. Not about what it does, but about how it does it. The post should be interesting to readers with some knowledge of Qt. Others, who won't understand it, are expected to be impressed by the complexity of implementing something as simple as adding another handle to a slider. On the other hand (wait for the pun!), this complexity is not so surprising: attaching another, fully functional hand to a human would be quite a feat, too.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why do we need it?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"That is, why we need another handle on slider, not another hand. (Though an extra hand would be useful, too - how cool would it be to take a sip of tea while typing?)\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A user posted \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/issues/6323\",\n        children: \"an issue\"\n      }), \", which he called \\\"\", _jsx(_components.em, {\n        children: \"Not a problem. More a frustration\"\n      }), \"\\\". Some widgets from the Text add-on allow the user to specify a range of values, for instance for filtering, which they do with a pair of spin boxes (those controls where you can enter a number manually or change it by pressing little up and down button on their right) or a pair of sliders. He suggested using a single slider with two handles. He noted that Orange currently doesn't have such a control in its repertoire.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With a bit of exaggeration, I use such sliders almost daily to specify the price range when ordering bicycle parts, tools and clothes. While I'm no stranger to such sliders, I don't particularly like them. Entering numbers manually is much easier. But then ... they could be useful in cases where numbers are not intepretable, so sliding a handle is more natural than guessing random numbers. A good example is setting scales for color palettes: the user has no idea which particular number will make the image look good.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Drawing native-looking widgets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Problem: Qt doesn't have such widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Qt is great - consistent, fast, powerful, and it has native look on all platforms. But if you want to make any small change to some element, its painting falls back to a Qt-based implementation which looks ... not only out of place but, well, ugly. See what becomes of a push button if you would, for whatever reason, want to increase its font size.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-interval-slider/2023-03-10-ugly-button.png\",\n      width: \"548\",\n      height: \"92\",\n      src: \"/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-ugly-button.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, can we create a double-handled slider that looks as it should? Not with some ugly square-ish handles?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"OP provided us with a \", _jsx(_components.a, {\n        href: \"https://stackoverflow.com/a/62665367\",\n        children: \"link\"\n      }), \" to implementation of such a slider. Our version is different, but based on the same trick.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Widget is (re)painted upon receiving a \", _jsx(_components.a, {\n        href: \"https://doc.qt.io/qt-6/qwidget.html#paintEvent\",\n        children: _jsx(_components.code, {\n          children: \"paintEvent\"\n        })\n      }), \". It must construct a painter, initialize it with proper options and paint whatever needs to be painted. \", _jsx(_components.code, {\n        children: \"QPushButton\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QCheckBox\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QLineEdit\"\n      }), \" ... and many other widgets must look different on different platforms. The actual painting is thus not coded within widgets, but relegated to \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \". \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \" is however an class: the actual \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \" in use by the application is \", _jsxs(_components.a, {\n        href: \"https://doc.qt.io/qt-6/qstyle.html#details\",\n        children: [\"one of \", _jsx(_components.code, {\n          children: \"QStyle\"\n        }), \"'s subclasses\"]\n      }), \" for different platforms and looks.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For instance, \", _jsx(_components.code, {\n        children: \"QPushButton.paintEvent\"\n      }), \" just calls \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \"'s \", _jsx(_components.code, {\n        children: \"drawControl(QStyle.CE_PushButton, option, painter)\"\n      }), \", where \", _jsx(_components.code, {\n        children: \"CE_PushButton\"\n      }), \" is a constant giving the control to paint, option is an instance of \", _jsx(_components.code, {\n        children: \"QStyleOptionButton\"\n      }), \" with the necessary data (the icon, text and button's features) and painter is an ordinary \", _jsx(_components.code, {\n        children: \"QPainter\"\n      }), \". (The \", _jsx(_components.a, {\n        href: \"https://github.com/radekp/qt/blob/master/src/gui/widgets/qpushbutton.cpp#L444\",\n        children: \"actual code\"\n      }), \" uses a convenience class \", _jsx(_components.code, {\n        children: \"QStylePainter\"\n      }), \"). \", _jsx(_components.code, {\n        children: \"drawControl\"\n      }), \" then draws all elements of the button -- its text, icon, any frames, borders.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Unlike \", _jsx(_components.code, {\n        children: \"QPushButton\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QSlider\"\n      }), \" is a complex control, consisting of subcontrols that represent the slider's groove, tickmarks and handle. Complex controls are not painted through \", _jsx(_components.code, {\n        children: \"QStyle.drawControl\"\n      }), \" but through another method, \", _jsx(_components.code, {\n        children: \"drawComplexControl\"\n      }), \". Its signature is similar, but \", _jsx(_components.code, {\n        children: \"option\"\n      }), \" has a mask \", _jsx(_components.code, {\n        children: \"subcontrols\"\n      }), \", which define the active subcontrols. Inactive controls are not painted and the user can't interact with them.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The trick to paint a two-handled slider is to call \", _jsx(_components.code, {\n        children: \"drawComplexControl\"\n      }), \" multiple times: first to draw the groove and tickmarks, and then twice, once for each handle. In the code fragment below, \", _jsx(_components.code, {\n        children: \"self.opt\"\n      }), \" is a \", _jsx(_components.code, {\n        children: \"QSliderOption\"\n      }), \". Method \", _jsx(_components.code, {\n        children: \"drawComplexControl\"\n      }), \" will put a handle at the position stored in \", _jsx(_components.code, {\n        children: \"self.opt.sliderPosition\"\n      }), \", hence we iterate it through \", _jsx(_components.code, {\n        children: \"self._pos\"\n      }), \", which stores the positions of our two handles.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Draw groove and tickmarks\"\n        }), \"\\nself.opt.subControls = QStyle.SC_SliderGroove | QStyle.SC_SliderTickmarks\\nself.style().drawComplexControl(QStyle.CC_Slider, self.opt, painter)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# Draw handles\"\n        }), \"\\nself.opt.subControls = QStyle.SC_SliderHandle\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" self.opt.sliderPosition \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" self._pos:\\n    self.style().drawComplexControl(QStyle.CC_Slider, self.opt, painter)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L206\",\n        children: \"code\"\n      }), \" adds some overhead (initializing options) and marks the part of the groove between the two handles in blue.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Controlling controls\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Drawing a native looking widget is only one part of the job. What happens when the user clicks it? The widget receives a \", _jsx(_components.code, {\n        children: \"mousePressEvent\"\n      }), \" - but which part of the widget was clicked? This is known only by the one who's drawn the control: \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.code, {\n        children: \"QStyle\"\n      }), \" implements several methods needed for interaction with complex controls. One is already used in \", _jsx(_components.code, {\n        children: \"paintEvent\"\n      }), \": we call \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \"'s \", _jsx(_components.code, {\n        children: \"subControlRect(QStyle.CC_Slider, self.opt, QStyle.SC_SliderHandle)\"\n      }), \" to \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L223\",\n        children: \"obtain the logical coordinates of each handle\"\n      }), \" in order to mark the interval between them.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"args = QStyle.CC_Slider, self.opt, QStyle.SC_SliderHandle\\nself.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\nx_left_handle = self.style().subControlRect(*args).right()\\nself.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]\\nx_right_handle = self.style().subControlRect(*args).left()\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.code, {\n        children: \"mousePressEvent\"\n      }), \", we call \", _jsx(_components.code, {\n        children: \"hitTestComplexControl(QStyle.CC_Slider, self.opt, event.pos(), self)\"\n      }), \", which tells us which subcontrol (if any) of the slider contains \", _jsx(_components.code, {\n        children: \"event.pos()\"\n      }), \". As in \", _jsx(_components.code, {\n        children: \"paintEvent\"\n      }), \", we set \", _jsx(_components.code, {\n        children: \"self.opt.sliderPosition\"\n      }), \" to each handle's position in turn, to see if \", _jsx(_components.code, {\n        children: \"hitTestComplexControl\"\n      }), \" returns \", _jsx(_components.code, {\n        children: \"QStyle.SC_SliderHandle\"\n      }), \" for any of them.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"mousePressEvent\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-params\",\n          children: \"self, event: QMouseEvent\"\n        }), \") -\u003e \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"None\"\n        }), \":\\n    self._dragged = self.NoHandle\\n    args = (QStyle.CC_Slider, self.opt, event.pos(), self)\\n    self.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" self.style().hitTestComplexControl(*args) == QStyle.SC_SliderHandle:\\n        self._dragged |= self.LowHandle\\n    self.opt.sliderPosition = self._pos[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" self.style().hitTestComplexControl(*args) == QStyle.SC_SliderHandle:\\n        self._dragged |= self.HighHandle\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" self._dragged != self.NoHandle:\\n        self.sliderPressed.emit(self._dragged)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L156\",\n        children: \"code\"\n      }), \" then records which handles are being dragged (if they overlap, both are marked as being dragged \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L175\",\n        children: \"until we see whether the user drags left or right\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The third method of \", _jsx(_components.code, {\n        children: \"QStyle\"\n      }), \", which we use in \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/5330c9fb5ed8dc08ed20350e21b026fbe54b6320/Orange/widgets/utils/intervalslider.py#L169\",\n        children: _jsx(_components.code, {\n          children: \"mouseEvent\"\n        })\n      }), \" determines the slider value from handles position - which is again known only by the class that paints a platform-dependent slider. \", _jsx(_components.code, {\n        children: \"sliderValueFromPosition\"\n      }), \" is given the slider span, (mouse) position and the total bounding rectangle of the slider, and returns the slider's value for that position.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"distance = self.opt.maximum - self.opt.minimum\\npos = self.style().sliderValueFromPosition(\\n    \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", distance, event.pos().x(), self.rect().width())\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"How do we use interval sliders?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Using interval sliders is as trivial as using ordinary \", _jsx(_components.code, {\n        children: \"QSliders\"\n      }), \": initialize it with starting position, interval and options (if any), and connect the necessary signals.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"slider = IntervalSlider(low, high * \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"100\"\n        }), \", minimum=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", maximum=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"100\"\n        }), \")\\nself.slider.intervalChanged.connect(self.__on_slider_moved)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Done.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":565,"height":369,"src":"/blog/2023-03-interval-slider/__optimized-images__/2023-03-10-interval-slider-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"interval-sliders"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/intro-to-data-mining-for-life-scientists/index.html b/blog/intro-to-data-mining-for-life-scientists/index.html
index 9f45d609e..3ad530e51 100644
--- a/blog/intro-to-data-mining-for-life-scientists/index.html
+++ b/blog/intro-to-data-mining-for-life-scientists/index.html
@@ -1,4 +1,4 @@
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Intro to Data Mining for Life Scientists</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Intro to Data Mining for Life Scientists"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-10-02 11:21:20+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="bioinformatics"/><meta property="article:tag" content="bioorange"/><meta property="article:tag" content="education"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="tutorial"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics, bioorange, education, orange3, tutorial, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Intro to Data Mining for Life Scientists</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 02, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://www.helmholtz-muenchen.de/rna-club/index.html">RNA Club Munich</a> has organized <a href="https://www.stemcells2016.org">Molecular Life of Stem Cells Conference</a> in Ljubljana this past Thursday, Friday and Saturday. They asked us to organize a <a href="https://www.stemcells2016.org/program/workshops/2-introduction-to-data-mining/index.html">four-hour workshop on data mining</a>. And here we were: four of us, Ajda, Anze, Marko and myself (Blaz) run a workshop for 25 students with molecular biology and biochemistry background.</p>
 <a href="/blog/2016-10-intro-to-data-mining-for-life-scientists/__optimized-images__/IMG_20160929_133840.jpg" data-gallery="true"><img alt="" loading="lazy" width="3803" height="2216" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-10-intro-to-data-mining-for-life-scientists/__optimized-images__/IMG_20160929_133840.jpg"/></a>
 <p>We have covered some basic data visualization, modeling (classification) and model scoring, hierarchical clustering and data projection, and finished with a touch of deep-learning by diving into image analysis by deep learning-based embedding.</p>
@@ -161,4 +161,4 @@
 <p>The hard part of any short course that includes machine learning is how to explain overfitting. The concept is not trivial for data science newcomers, but it is so important it simply cannot be left out. Luckily, Orange has some cool widgets to help us understanding the overfitting. Below is a workflow we have used. We read some data (this time it was a yeast gene expression data set called brown-selected that comes with Orange), “destroyed the data” by randomly permuting the column with class values, trained a classification tree, and observed near perfect results when the model was checked on the training data.</p>
 <a href="/blog/2016-10-intro-to-data-mining-for-life-scientists/__optimized-images__/yeast-overfitting-distributions.png" data-gallery="true"><img alt="" loading="lazy" width="1021" height="801" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-10-intro-to-data-mining-for-life-scientists/__optimized-images__/yeast-overfitting-distributions.png"/></a>
 <p>Sure this works, you are probably saying. The models should have been scored on a separate test set! Exactly, and this is what we have done next with <a href="/widget-catalog/data/datasampler/">Data Sampler</a>, which lead us to cross-validation and <a href="/widget-catalog/evaluate/testandscore/">Test &amp; Score widget</a>.</p>
-<p>This was a great and interesting short course and we were happy to contribute to the success of the student-run <a href="https://www.stemcells2016.org">MLSC-2016</a> conference.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-10-02 11:21:20+00:00","draft":false,"title":"Intro to Data Mining for Life Scientists","blog":["bioinformatics","bioorange","education","orange3","tutorial","workshop"],"oldUrl":"/blog/2016/10/02/intro-to-data-mining-for-life-scientists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.helmholtz-muenchen.de/rna-club/index.html\",\n        children: \"RNA Club Munich\"\n      }), \" has organized \", _jsx(_components.a, {\n        href: \"https://www.stemcells2016.org\",\n        children: \"Molecular Life of Stem Cells Conference\"\n      }), \" in Ljubljana this past Thursday, Friday and Saturday. 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+<p>This was a great and interesting short course and we were happy to contribute to the success of the student-run <a href="https://www.stemcells2016.org">MLSC-2016</a> conference.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-10-02 11:21:20+00:00","draft":false,"title":"Intro to Data Mining for Life Scientists","blog":["bioinformatics","bioorange","education","orange3","tutorial","workshop"],"oldUrl":"/blog/2016/10/02/intro-to-data-mining-for-life-scientists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.helmholtz-muenchen.de/rna-club/index.html\",\n        children: \"RNA Club Munich\"\n      }), \" has organized \", _jsx(_components.a, {\n        href: \"https://www.stemcells2016.org\",\n        children: \"Molecular Life of Stem Cells Conference\"\n      }), \" in Ljubljana this past Thursday, Friday and Saturday. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, tree</strong></p><h1 class="sc-c67a4900-0 loxVcf">It&#x27;s Sailing Time (Again)</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 11, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Every fall I teach a course on Introduction to Data Mining. And while the course is really on statistical learning and its applications, I also venture into <a href="https://en.wikipedia.org/wiki/Decision_tree_learning">classification trees</a>. For several reasons. First, I can introduce <a href="https://en.wikipedia.org/wiki/Information_gain_ratio">information gain</a> and with it feature scoring and ranking. Second, classification trees are one of the first machine learning approaches co-invented by engineers (<a href="https://en.wikipedia.org/wiki/Ross_Quinlan">Ross Quinlan</a>) and statisticians (<a href="https://www.amazon.com/Classification-Regression-Wadsworth-Statistics-Probability/dp/0412048418/ref=sr_1_1?ie=UTF8&amp;qid=1501848607&amp;sr=8-1&amp;keywords=classification+and+regression+trees">Leo Breiman, Jerome Friedman, Charles J. Stone, Richard A. Olshen</a>). And finally, because they make the base of random forests, one of the most accurate machine learning models for smaller and mid-size data sets.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2/">Introduction to Data Mining Course in Houston</a></p>
 <p>Lecture on classification trees has to start with the data. Years back I have crafted a data set on sailing. Every data set has to have a story. Here is one:</p>
@@ -166,4 +166,4 @@
 <p>Turns out that Sara is a social person. When the company is big, she goes sailing no matter what. When the company is smaller, she would not go sailing if the weather is bad. But when it is sunny, sailing is fun, even when being alone.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/07/29/pythagorean-trees-and-forests/">Pythagorean Trees and Forests</a></p>
 <p>Classification trees are not very stable classifiers. Even with small changes in the data, the trees can change substantially. This is an important concept that leads to the use of ensembles like random forests. It is also here, during my lecture, that I need to demonstrate this instability. I use Data Sampler and show a classification tree under the current sampling. Pressing on Sample Data button the tree changes every time. The workflow I use is below, but if you really want to see this in action, well, try it in Orange.</p>
-<a href="/blog/2017-08-its-sailing-time-again/__optimized-images__/sailing-sampled-trees.png" data-gallery="true"><img alt="" loading="lazy" width="1005" height="716" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-08-its-sailing-time-again/__optimized-images__/sailing-sampled-trees.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-08-11 08:59:54+00:00","draft":false,"title":"It's Sailing Time (Again)","blog":["classification","tree"],"oldUrl":"/blog/2017/08/11/its-sailing-time-again/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Every fall I teach a course on Introduction to Data Mining. 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And finally, because they make the base of random forests, one of the most accurate machine learning models for smaller and mid-size data sets.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/09/15/data-mining-in-houston-2/\",\n        children: \"Introduction to Data Mining Course in Houston\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Lecture on classification trees has to start with the data. Years back I have crafted a data set on sailing. Every data set has to have a story. Here is one:\"\n    }), \"\\n\", _jsx(\"blockquote\", {\n      children: \"Sara likes weekend sailing. Though, not under any condition. Past\\ntwenty Wednesdays I have asked her if she will have any company, what\\nkind of boat she can rent, and I have checked the weather\\nforecast. 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+<a href="/blog/2017-08-its-sailing-time-again/__optimized-images__/sailing-sampled-trees.png" data-gallery="true"><img alt="" loading="lazy" width="1005" height="716" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-08-its-sailing-time-again/__optimized-images__/sailing-sampled-trees.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-08-11 08:59:54+00:00","draft":false,"title":"It's Sailing Time (Again)","blog":["classification","tree"],"oldUrl":"/blog/2017/08/11/its-sailing-time-again/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Every fall I teach a course on Introduction to Data Mining. 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And finally, because they make the base of random forests, one of the most accurate machine learning models for smaller and mid-size data sets.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/09/15/data-mining-in-houston-2/\",\n        children: \"Introduction to Data Mining Course in Houston\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Lecture on classification trees has to start with the data. Years back I have crafted a data set on sailing. Every data set has to have a story. Here is one:\"\n    }), \"\\n\", _jsx(\"blockquote\", {\n      children: \"Sara likes weekend sailing. Though, not under any condition. Past\\ntwenty Wednesdays I have asked her if she will have any company, what\\nkind of boat she can rent, and I have checked the weather\\nforecast. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>article, jmlr, scripting, toolbox</strong></p><h1 class="sc-c67a4900-0 loxVcf">JMLR Publishes Article on Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 03, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Journal of Machine Learning Research has just published our <a href="http://jmlr.org/papers/v14/demsar13a.html">paper on Orange</a>. In the paper we focus on its Python scripting part. We have last reported on Orange scripting at <a href="http://ecmlpkdd.isti.cnr.it/">ECML/PKDD 2004</a>. The manuscript was well received (over 270 citations on Google Scholar), but it is now entirely outdated. This was also our only formal publication on Orange scripting. With publication in JMLR this is now a current description of Orange and will be, for a while :-), Orange’s primary reference.</p>
 <p>Here&#x27;s a reference:</p>
 <p>Demšar, J., Curk, T., &amp; Erjavec, A. et al. Orange: Data Mining Toolbox in Python; Journal of Machine Learning Research 14(Aug):2349−2353, 2013.</p>
@@ -168,4 +168,4 @@
   pages   = {2349-2353},
   url     = {http://jmlr.org/papers/v14/demsar13a.html}
 }
-</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2013-10-03 02:50:00+00:00","draft":false,"title":"JMLR Publishes Article on Orange","blog":["article","jmlr","scripting","toolbox"],"oldUrl":"/blog/2013/10/03/jmlr-publishes-article-on-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Journal of Machine Learning Research has just published our \", _jsx(_components.a, {\n        href: \"http://jmlr.org/papers/v14/demsar13a.html\",\n        children: \"paper on Orange\"\n      }), \". 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+</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2013-10-03 02:50:00+00:00","draft":false,"title":"JMLR Publishes Article on Orange","blog":["article","jmlr","scripting","toolbox"],"oldUrl":"/blog/2013/10/03/jmlr-publishes-article-on-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Journal of Machine Learning Research has just published our \", _jsx(_components.a, {\n        href: \"http://jmlr.org/papers/v14/demsar13a.html\",\n        children: \"paper on Orange\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, tutorial, unsupervised, youtube</strong></p><h1 class="sc-c67a4900-0 loxVcf">k-Means and Silhouette Score</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 17, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>k-Means is one of the most popular unsupervised learning algorithms for finding interesting groups in our data. It can be useful in customer segmentation, finding gene families, determining document types, improving human resource management and so on.</p>
 <p>But... have you ever wondered how k-means works? In the following three videos we explain how to construct a data analysis workflow using k-means, how k-means works, how to find a good k value and how silhouette score can help us find the inliers and the outliers.</p>
 <p><strong>#1 Constructing workflow with k-means</strong></p>
@@ -160,4 +160,4 @@
 <p><strong>#2 How k-means works [interactive visualization]</strong></p>
 <div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/I0e0Qyev8Ac" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div>
 <p><strong>#3 How silhouette score works and why it is useful</strong></p>
-<div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/5TPldC_dC0s" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-03-17 17:05:25+00:00","draft":false,"title":"k-Means and Silhouette Score","blog":["clustering","tutorial","unsupervised","youtube"],"oldUrl":"/blog/2017/03/17/k-means-silhouette-score/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"k-Means is one of the most popular unsupervised learning algorithms for finding interesting groups in our data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">KDnuggets is asking if you have been using Orange lately</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">May 19, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://www.kdnuggets.com/">KDnuggets</a>, one of leading data mining community websites, is having its yearly poll <a href="http://www.kdnuggets.com/2012/05/new-poll-analytics-data-mining-software-used.html">asking its visitors which analytics/data mining software they used in the past 12 months</a>. Among listed is also Orange, our fruity visually pleasing open source pythonic data mining suite. So we are asking you, have you been using Orange lately, that is, in the past 12 months? How do you feel about telling that to the world?</p>
-<p>If so, we would also like to hear more about how you are using Orange in your projects, research, competitions, or data mining play. We would be glad to publish your story on our blog, or link to your blog post. Feel free to <a href="/contact/">contact us</a> if you are interested.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-05-19 23:38:00+00:00","draft":false,"title":"KDnuggets is asking if you have been using Orange lately","blog":["orange3"],"oldUrl":"/blog/2012/05/19/kdnuggets-is-asking-if-you-have-been-using-orange-lately/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.kdnuggets.com/\",\n        children: \"KDnuggets\"\n      }), \", one of leading data mining community websites, is having its yearly poll \", _jsx(_components.a, {\n        href: \"http://www.kdnuggets.com/2012/05/new-poll-analytics-data-mining-software-used.html\",\n        children: \"asking its visitors which analytics/data mining software they used in the past 12 months\"\n      }), \". Among listed is also Orange, our fruity visually pleasing open source pythonic data mining suite. So we are asking you, have you been using Orange lately, that is, in the past 12 months? How do you feel about telling that to the world?\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If so, we would also like to hear more about how you are using Orange in your projects, research, competitions, or data mining play. We would be glad to publish your story on our blog, or link to your blog post. Feel free to \", _jsx(_components.a, {\n        href: \"/contact/\",\n        children: \"contact us\"\n      }), \" if you are interested.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"kdnuggets-is-asking-if-you-have-been-using-orange-lately"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>If so, we would also like to hear more about how you are using Orange in your projects, research, competitions, or data mining play. We would be glad to publish your story on our blog, or link to your blog post. Feel free to <a href="/contact/">contact us</a> if you are interested.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-05-19 23:38:00+00:00","draft":false,"title":"KDnuggets is asking if you have been using Orange lately","blog":["orange3"],"oldUrl":"/blog/2012/05/19/kdnuggets-is-asking-if-you-have-been-using-orange-lately/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.kdnuggets.com/\",\n        children: \"KDnuggets\"\n      }), \", one of leading data mining community websites, is having its yearly poll \", _jsx(_components.a, {\n        href: \"http://www.kdnuggets.com/2012/05/new-poll-analytics-data-mining-software-used.html\",\n        children: \"asking its visitors which analytics/data mining software they used in the past 12 months\"\n      }), \". Among listed is also Orange, our fruity visually pleasing open source pythonic data mining suite. So we are asking you, have you been using Orange lately, that is, in the past 12 months? How do you feel about telling that to the world?\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If so, we would also like to hear more about how you are using Orange in your projects, research, competitions, or data mining play. We would be glad to publish your story on our blog, or link to your blog post. Feel free to \", _jsx(_components.a, {\n        href: \"/contact/\",\n        children: \"contact us\"\n      }), \" if you are interested.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"kdnuggets-is-asking-if-you-have-been-using-orange-lately"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - LDAvis: visualization for LDA topic modelling</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - LDAvis: visualization for LDA topic modelling"/><meta property="og:description" content="Text add-on now offers a way to explain topics with LDAvis."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2022-03-18"/><meta property="article:author" content="Ajda Pretnar Žagar"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="topic modelling"/><meta property="article:tag" content="lda"/><meta property="article:tag" content="visualization"/><meta property="og:image" content="/blog/2022-03-ldavis/__optimized-images__/2022-03-18_ldavis_small.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>text mining, topic modelling, lda, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">LDAvis: visualization for LDA topic modelling</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar Žagar</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 18, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Topic modelling is a great way to uncover hidden topics in a large collection of documents. The method is extremely popular in digital humanities, so it is not just about the performance, but also the explainability.</p>
 <p>Among topic modelling methods, many researchers still go with LDA, a generative model that observe word frequencies in the corpus and iteratively constructs a topic model for a given number of topics.</p>
 <p>Topic Modelling widget computes the LDA topic model. The widget also shows the top 10 words that describe each topic. We construct the below example of <em>grimm-tales-selected</em>, which we loaded with the <strong>Corpus</strong> widget. Then we preprocess the data with <strong>Preprocess Text</strong>, where we used the default preprocessing. The most important thing is to add <strong>Bag of Words</strong> with the TF-IDF transform. LDA works a lot better when using this transform, as it descreases the importance of overly frequent words.</p>
@@ -171,4 +171,4 @@
 <a href="/blog/2022-03-ldavis/__optimized-images__/2022-03-18_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="710" height="254" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-03-ldavis/__optimized-images__/2022-03-18_workflow.png"/></a>
 <p>This is it - a quick way to recreate LDAvis in Orange.</p>
 <h4>References</h4>
-<p>Sievert, C. and K. E. Shirley. 2014. &quot;LDAvis: A method for visualizing and interpreting topics.&quot; Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70, Baltimore, Maryland, USA.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-03-18","draft":false,"title":"LDAvis: visualization for LDA topic modelling","thumbImage":"2022-03-18_ldavis_small.png","frontPageImage":"2022-03-18_ldavis_small.png","blog":["text mining","topic modelling","lda","visualization"],"shortExcerpt":"Text add-on now offers a way to explain topics with LDAvis.","longExcerpt":"Text add-on recently got extended with LDAvis widget, a visualization that enables exploring word frequencies in LDA-generated topics. See how to construct LDAvis pipeline in Orange.","x2images":true,"oldUrl":"/blog/2022/2022-03-18-ldavis/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    strong: \"strong\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Topic modelling is a great way to uncover hidden topics in a large collection of documents. 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Perhaps most tales talk about grandmothers, so the top word for the tenth topic is not really informative.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To explore the relationship between frequent and specific words in the topic, we can use LDAvis (Siever and Shirley 2014). Be careful to connect the right output to the widget. Topic Modelling outputs three things: corpus with topics, the selected topic, and all topics (topic-term matrix). We need last output for LDAvis to work.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_edit-links.png\",\n      width: \"688\",\n      height: \"343\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_edit-links.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"LDAvis shows top ranked words for each topic, weighted by relevance. Generally, topic modelling show the top N words by the word's topic probability. In other words, the more frequent is the word in the topic, the higher the word will be on the list.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Relevance defines the relationship between the word's topic probability and its lift. Lift is the ratio of the probability of the word in the topic to the probability of the word in the corpus. The more frequent is the word in the corpus, the lower will be its lift. Hence lift will expose words that appear almost exclusively in the topic.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Relevance is set to 0.5 by default, meaning there will be a balance between the probability of the word in the topic and its lift. Feel free to change the value and explore how the word ranking changes.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_ldavis.png\",\n      width: \"888\",\n      height: \"634\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_ldavis.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Grandmother from the topic 10 actually seems like it appears almost exlusively in this topic. Wolf, for example, is also characteristic of the topic, but it is quite frequent in the corpus in general.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Siever and Shirley's LDAvis has another component, which shows marginal topic frequency in an MDS projection. Connect \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" output from Topic Modelling to \", _jsx(_components.strong, {\n        children: \"MDS\"\n      }), \". 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+<p>Sievert, C. and K. E. Shirley. 2014. &quot;LDAvis: A method for visualizing and interpreting topics.&quot; Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70, Baltimore, Maryland, USA.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-03-18","draft":false,"title":"LDAvis: visualization for LDA topic modelling","thumbImage":"2022-03-18_ldavis_small.png","frontPageImage":"2022-03-18_ldavis_small.png","blog":["text mining","topic modelling","lda","visualization"],"shortExcerpt":"Text add-on now offers a way to explain topics with LDAvis.","longExcerpt":"Text add-on recently got extended with LDAvis widget, a visualization that enables exploring word frequencies in LDA-generated topics. See how to construct LDAvis pipeline in Orange.","x2images":true,"oldUrl":"/blog/2022/2022-03-18-ldavis/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    strong: \"strong\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Topic modelling is a great way to uncover hidden topics in a large collection of documents. The method is extremely popular in digital humanities, so it is not just about the performance, but also the explainability.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Among topic modelling methods, many researchers still go with LDA, a generative model that observe word frequencies in the corpus and iteratively constructs a topic model for a given number of topics.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Topic Modelling widget computes the LDA topic model. The widget also shows the top 10 words that describe each topic. We construct the below example of \", _jsx(_components.em, {\n        children: \"grimm-tales-selected\"\n      }), \", which we loaded with the \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget. Then we preprocess the data with \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \", where we used the default preprocessing. The most important thing is to add \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" with the TF-IDF transform. LDA works a lot better when using this transform, as it descreases the importance of overly frequent words.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_topic-modelling.png\",\n      width: \"992\",\n      height: \"442\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_topic-modelling.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \" we use the LDA method with 10 topics. Ok, the first topic is a about animal tales, even more so the second. The fourth topic is about fish and the tenth topic probably contains the Little Red Riding Hood tale. But how frequent are grandmothers in the corpus overall? Perhaps most tales talk about grandmothers, so the top word for the tenth topic is not really informative.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To explore the relationship between frequent and specific words in the topic, we can use LDAvis (Siever and Shirley 2014). Be careful to connect the right output to the widget. Topic Modelling outputs three things: corpus with topics, the selected topic, and all topics (topic-term matrix). We need last output for LDAvis to work.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_edit-links.png\",\n      width: \"688\",\n      height: \"343\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_edit-links.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"LDAvis shows top ranked words for each topic, weighted by relevance. Generally, topic modelling show the top N words by the word's topic probability. In other words, the more frequent is the word in the topic, the higher the word will be on the list.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Relevance defines the relationship between the word's topic probability and its lift. Lift is the ratio of the probability of the word in the topic to the probability of the word in the corpus. The more frequent is the word in the corpus, the lower will be its lift. Hence lift will expose words that appear almost exclusively in the topic.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Relevance is set to 0.5 by default, meaning there will be a balance between the probability of the word in the topic and its lift. Feel free to change the value and explore how the word ranking changes.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_ldavis.png\",\n      width: \"888\",\n      height: \"634\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_ldavis.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Grandmother from the topic 10 actually seems like it appears almost exlusively in this topic. Wolf, for example, is also characteristic of the topic, but it is quite frequent in the corpus in general.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Siever and Shirley's LDAvis has another component, which shows marginal topic frequency in an MDS projection. Connect \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" output from Topic Modelling to \", _jsx(_components.strong, {\n        children: \"MDS\"\n      }), \". 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Voila, this is a PCA-initiated MDS projection, where the size of the point corresponds to the marginal topic probability and the position of the point to its similarity with other topics.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_mds.png\",\n      width: \"1082\",\n      height: \"785\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_mds.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Topic 4, which is about the fish, seems to be quite well-represented in the corpus.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-03-ldavis/2022-03-18_workflow.png\",\n      width: \"710\",\n      height: \"254\",\n      src: \"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is it - a quick way to recreate LDAvis in Orange.\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"References\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sievert, C. and K. E. Shirley. 2014. \\\"LDAvis: A method for visualizing and interpreting topics.\\\" Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70, Baltimore, Maryland, USA.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-03-ldavis/__optimized-images__/2022-03-18_ldavis_small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"ldavis-visualization-for-lda-topic-modelling"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Learn with Paint Data</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Learn with Paint Data"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-07-10 07:00:54+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="data"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="plot"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, clustering, data, examples, plot, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Learn with Paint Data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 10, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><strong>Paint Data</strong> widget might initially look like a kids’ game, but in combination with other Orange widgets it becomes a very simple and useful tool for conveying statistical concepts, such as <strong>k-means</strong>, <strong>hierarchical clustering</strong> and <strong>prediction models</strong> (like SVM, logistical regression, etc.).</p>
 <p>The widget enables you to draw your data on a 2-D plane. You can name the x and y axes, select the number of classes (which are represented by different colors) and then position the points on a graph.</p>
 <a href="/blog/2015-07-learn-with-paint-data/__optimized-images__/PaintData-Example.png" data-gallery="true"><img alt="" loading="lazy" width="803" height="626" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-learn-with-paint-data/__optimized-images__/PaintData-Example.png"/></a>
@@ -160,4 +160,4 @@
 <p>Another way to use <strong>Paint Data</strong> is to observe the performance of classification methods, where we can alter the graph to demonstrate improvement or deterioration of prediction models. By painting the data points we can try to construct the data set, which would be difficult for one but easy for another classifier. Say, why does linear SVM fail on the data set below?</p>
 <a href="/blog/2015-07-learn-with-paint-data/__optimized-images__/PaintData-TestLearners.png" data-gallery="true"><img alt="" loading="lazy" width="1200" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-learn-with-paint-data/__optimized-images__/PaintData-TestLearners.png"/></a>
 <p>Use Paint Data to compare prediction quality of several classifiers.</p>
-<p>Happy painting!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-10 07:00:54+00:00","draft":false,"title":"Learn with Paint Data","blog":["classification","clustering","data","examples","plot","visualization"],"oldUrl":"/blog/2015/07/10/learn-with-paint-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Paint Data\"\n      }), \" widget might initially look like a kids’ game, but in combination with other Orange widgets it becomes a very simple and useful tool for conveying statistical concepts, such as \", _jsx(_components.strong, {\n        children: \"k-means\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"hierarchical clustering\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"prediction models\"\n      }), \" (like SVM, logistical regression, etc.).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget enables you to draw your data on a 2-D plane. 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Jitter and magnet are converse tools which allow either to spread the instances or draw them closer together.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The data will be represented in a data table with two attributes, where their instances correspond to coordinates in the system. Such data set is great for demonstrating k-means and hierarchical clustering methods. Just like we do below. In the screenshot we see that k-means, with our particular settings, recognizes clusters way better than hierarchical clustering. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. 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+<p>Happy painting!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-10 07:00:54+00:00","draft":false,"title":"Learn with Paint Data","blog":["classification","clustering","data","examples","plot","visualization"],"oldUrl":"/blog/2015/07/10/learn-with-paint-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Paint Data\"\n      }), \" widget might initially look like a kids’ game, but in combination with other Orange widgets it becomes a very simple and useful tool for conveying statistical concepts, such as \", _jsx(_components.strong, {\n        children: \"k-means\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"hierarchical clustering\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"prediction models\"\n      }), \" (like SVM, logistical regression, etc.).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget enables you to draw your data on a 2-D plane. 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index a3f881eff..396a488bb 100644
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@@ -215,4 +215,4 @@
 <p><em>MajorityLearner()</em></p>
 <p><em>RandomForestLearner()</em></p>
 <p><em>SVMLearner()</em></p>
-<p>For other learners, you can find all the parameters and descriptions <a href="https://docs.biolab.si/orange/3/data-mining-library/tutorial/classification.html">in the documentation</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-10-16 08:32:43+00:00","draft":false,"title":"Learners in Python","blog":["classification","examples","orange3","python","scripting"],"oldUrl":"/blog/2015/10/16/learners-in-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We've already written about \", _jsx(_components.a, {\n        href: \"/blog/2015/08/14/classifying-instances-with-orange-in-python/\",\n        children: \"classifying instances in Python\"\n      }), \". However, it's always nice to have a comprehensive list of classifiers and a step-by-step procedure at hand.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"TRAINING THE CLASSIFIER\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We start with simply importing Orange module into Python and loading our data set.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003e import Orange\\n    \u003e\u003e\u003e\u003e data = Orange.data.Table(\\\"titanic\\\")\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are using 'titanic.tab' data. You can load any data set you want, but it does have to have a categorical class variable (for numeric targets use regression). Now we want to train our classifier.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003e learner = Orange.classification.LogisticRegressionLearner()\\n    \u003e\u003e\u003e\u003e classifier = learner(data)\\n    \u003e\u003e\u003e\u003e classifier(data[0])\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Python returns the index of the value, as usual.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    array[0.]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To check what's in the class variable we print:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003eprint(\\\"Name of the variable: \\\", data.domain.class_var.name)\\n    \u003e\u003e\u003e\u003eprint(\\\"Class values: \\\", data.domain.class_var.values)\\n    \u003e\u003e\u003e\u003eprint(\\\"Value of our instance: \\\", data.domain.class_var.values[0])\\n    \\n    Name of the variable: survived\\n    Class values: no, yes\\n    Value of our instance: no\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"PREDICTIONS\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you want to get predictions for the entire data set, just give the classifier the entire data set.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003e classifier(data)\\n    \\n    array[0, 0, 0, ..., 1, 1, 1]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we want to append predictions to the data table, first use classifier on the data, then create a new domain with an additional meta attribute and finally form a new data table with appended predictions:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    svm = classifier(data)\\n    \\n    new_domain = Orange.data.Domain(data.domain.attributes, data.domain.class_vars, [data.domain.class_var])\\n    \\n    table2 = Orange.data.Table(new_domain, data.X, data.Y, svm.reshape(-1, 1))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We use .reshape to transform vector data into a reshaped array. Then we print out the data.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print(table2)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"PARAMETERS\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Want to use another classifier? The procedure is the same, simply use:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Orange.classification.\u003calgorithm-name\u003e()\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For most classifiers, you can set a whole range of parameters. Logistic Regression, for example, uses the following:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    learner = Orange.classification.LogisticRegressionLearner(**penalty**='l2', **dual**=False, **tol**=0.0001, **C**=1.0, **fit_intercept**=True, **intercept_scaling**=1, **class_weight**=None, **random_state**=None **preprocessors**=None)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To check the parameters for the classifier, use:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print(Orange.classification.SVMLearner())\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"PROBABILITIES\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another thing you can check with classifiers are the probabilities.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    classifier(data[0], Orange.classification.Model.ValueProbs)\\n    \\n    \u003e\u003e\u003e(array([ 0.]), array([[ 1.,  0.]]))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first array is the value for your selected instance (data[0]), while the second array contains probabilities for class values (probability for ‘no’ is 1 and for ‘yes’ 0).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"CLASSIFIERS\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And because we care about you, we’re giving you here a full list of classifier names:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"LogisticRegressionLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"NaiveBayesLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"KNNLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"TreeLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"MajorityLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"RandomForestLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"SVMLearner()\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For other learners, you can find all the parameters and descriptions \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/orange/3/data-mining-library/tutorial/classification.html\",\n        children: \"in the documentation\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"learners-in-python"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>For other learners, you can find all the parameters and descriptions <a href="https://docs.biolab.si/orange/3/data-mining-library/tutorial/classification.html">in the documentation</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-10-16 08:32:43+00:00","draft":false,"title":"Learners in Python","blog":["classification","examples","orange3","python","scripting"],"oldUrl":"/blog/2015/10/16/learners-in-python/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We've already written about \", _jsx(_components.a, {\n        href: \"/blog/2015/08/14/classifying-instances-with-orange-in-python/\",\n        children: \"classifying instances in Python\"\n      }), \". However, it's always nice to have a comprehensive list of classifiers and a step-by-step procedure at hand.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"TRAINING THE CLASSIFIER\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We start with simply importing Orange module into Python and loading our data set.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003e import Orange\\n    \u003e\u003e\u003e\u003e data = Orange.data.Table(\\\"titanic\\\")\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are using 'titanic.tab' data. You can load any data set you want, but it does have to have a categorical class variable (for numeric targets use regression). Now we want to train our classifier.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003e learner = Orange.classification.LogisticRegressionLearner()\\n    \u003e\u003e\u003e\u003e classifier = learner(data)\\n    \u003e\u003e\u003e\u003e classifier(data[0])\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Python returns the index of the value, as usual.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    array[0.]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To check what's in the class variable we print:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003eprint(\\\"Name of the variable: \\\", data.domain.class_var.name)\\n    \u003e\u003e\u003e\u003eprint(\\\"Class values: \\\", data.domain.class_var.values)\\n    \u003e\u003e\u003e\u003eprint(\\\"Value of our instance: \\\", data.domain.class_var.values[0])\\n    \\n    Name of the variable: survived\\n    Class values: no, yes\\n    Value of our instance: no\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"PREDICTIONS\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you want to get predictions for the entire data set, just give the classifier the entire data set.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e\u003e classifier(data)\\n    \\n    array[0, 0, 0, ..., 1, 1, 1]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we want to append predictions to the data table, first use classifier on the data, then create a new domain with an additional meta attribute and finally form a new data table with appended predictions:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    svm = classifier(data)\\n    \\n    new_domain = Orange.data.Domain(data.domain.attributes, data.domain.class_vars, [data.domain.class_var])\\n    \\n    table2 = Orange.data.Table(new_domain, data.X, data.Y, svm.reshape(-1, 1))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We use .reshape to transform vector data into a reshaped array. Then we print out the data.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print(table2)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"PARAMETERS\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Want to use another classifier? The procedure is the same, simply use:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Orange.classification.\u003calgorithm-name\u003e()\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For most classifiers, you can set a whole range of parameters. Logistic Regression, for example, uses the following:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    learner = Orange.classification.LogisticRegressionLearner(**penalty**='l2', **dual**=False, **tol**=0.0001, **C**=1.0, **fit_intercept**=True, **intercept_scaling**=1, **class_weight**=None, **random_state**=None **preprocessors**=None)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To check the parameters for the classifier, use:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    print(Orange.classification.SVMLearner())\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"PROBABILITIES\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another thing you can check with classifiers are the probabilities.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    classifier(data[0], Orange.classification.Model.ValueProbs)\\n    \\n    \u003e\u003e\u003e(array([ 0.]), array([[ 1.,  0.]]))\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first array is the value for your selected instance (data[0]), while the second array contains probabilities for class values (probability for ‘no’ is 1 and for ‘yes’ 0).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"CLASSIFIERS\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And because we care about you, we’re giving you here a full list of classifier names:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"LogisticRegressionLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"NaiveBayesLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"KNNLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"TreeLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"MajorityLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"RandomForestLearner()\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"SVMLearner()\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For other learners, you can find all the parameters and descriptions \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/orange/3/data-mining-library/tutorial/classification.html\",\n        children: \"in the documentation\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"learners-in-python"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, dataloading, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Loading your data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 18, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>By a popular demand, we have just published a <a href="https://docs.biolab.si/orange/3/visual-programming/loading-your-data/index.html">tutorial</a> on how to load the data table into Orange. Besides its own <a href="https://docs.biolab.si/orange/3/data-mining-library/reference/data.io.html">.tab format</a>, Orange can load any tab or comma delimited data set. The details are though in writing header rows that tell Orange about the type and domain of each attribute. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>neighbors, images</strong></p><h1 class="sc-c67a4900-0 loxVcf">Look-alike Images</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 08, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>There is a cool and perhaps not often used widget in Orange called Neighbors. The widget accepts the data and a reference data item and outputs the nearest neighbors of that item.</p>
 <p>Related: <a href="/blog/2018/02/02/image-analytics-workshop-at-aiucd-2018/" target="_blank" rel="noreferrer">Image Analytics Workshop at AIUCD 2018</a></p>
 <p>Here I will show how to use it to display a set of images most similar to a selected reference image. I will use the following workflow:</p>
@@ -171,4 +171,4 @@
 <a href="/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-bicycles.png" data-gallery="true"><img alt="" loading="lazy" width="1071" height="672" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-bicycles.png"/></a>
 <p><br/>
 <!-- -->\</p>
-<p>We skipped any details on image embedding, measuring distances and so on. For more on these, check out our recent <a href="https://www.nature.com/articles/s41467-019-12397-x">paper in Nature Communications</a> or see our <a href="https://www.youtube.com/watch?v=Iu8g2Twjn9U">set of image analytics videos on YouTube</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2020-01-08","draft":false,"title":"Look-alike Images","thumbImage":"2020-01-08-neighbors-images-small.png","frontPageImage":"2020-01-08-neighbors-images-small.png","blog":["neighbors","images"],"shortExcerpt":"We show how to use Neighbors widget on image embedding space to find image look-alikes.","longExcerpt":"We show how to use Neighbors widget on image embedding space to find image look-alikes.","oldUrl":"/blog/2020/2020-01-08-neighbors-images/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    br: \"br\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"There is a cool and perhaps not often used widget in Orange called Neighbors. 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+<p>We skipped any details on image embedding, measuring distances and so on. For more on these, check out our recent <a href="https://www.nature.com/articles/s41467-019-12397-x">paper in Nature Communications</a> or see our <a href="https://www.youtube.com/watch?v=Iu8g2Twjn9U">set of image analytics videos on YouTube</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2020-01-08","draft":false,"title":"Look-alike Images","thumbImage":"2020-01-08-neighbors-images-small.png","frontPageImage":"2020-01-08-neighbors-images-small.png","blog":["neighbors","images"],"shortExcerpt":"We show how to use Neighbors widget on image embedding space to find image look-alikes.","longExcerpt":"We show how to use Neighbors widget on image embedding space to find image look-alikes.","oldUrl":"/blog/2020/2020-01-08-neighbors-images/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    br: \"br\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"There is a cool and perhaps not often used widget in Orange called Neighbors. The widget accepts the data and a reference data item and outputs the nearest neighbors of that item.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/02/02/image-analytics-workshop-at-aiucd-2018/\",\n        name: \"Image Analytics Workshop at AIUCD 2018\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here I will show how to use it to display a set of images most similar to a selected reference image. I will use the following workflow:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-neighbors-images/2020-01-08-neighbors-images-workflow.png\",\n      width: \"715\",\n      height: \"288\",\n      src: \"/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We may use any collection of images, and for this blog, I have decided to pull out some image sets available from Datasets widget. To use your own collection of images, check out our \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=Iu8g2Twjn9U\",\n        children: \"YouTube video on image clustering\"\n      }), \" to see how to prepare it. In the Dataset widget, filter the data sets to list only those that include images:\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-neighbors-images/2020-01-08-neighbors-images-datasets.png\",\n      width: \"955\",\n      height: \"458\",\n      src: \"/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-datasets.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I will use Bone Healing image set from our recent \", _jsx(_components.a, {\n        href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n        children: \"Nature Communications paper\"\n      }), \". In the workflow, we first embed the images in the vector space. We send all the embedded images to the Neighbors widget through its input data channel and display them in Image Viewer. In the viewer, we can select an image. Image Viewer sends its output -- the selected image -- to the reference channel of the Neighbors widget. I have instructed this widget to send out three data items that are the most similar to the item in the reference, and on the output excluded the reference item:\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-neighbors-images/2020-01-08-neighbors-images-neighbors.png\",\n      width: \"443\",\n      height: \"208\",\n      src: \"/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-neighbors.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image Viewer (1) at the end of the pipeline displays three images that are most like the chosen image:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-neighbors-images/2020-01-08-neighbors-images-result.png\",\n      width: \"1064\",\n      height: \"390\",\n      src: \"/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-result.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Any other set of images works as well. Here is our image look-alike for traffic signs:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-neighbors-images/2020-01-08-neighbors-images-bicycles.png\",\n      width: \"1071\",\n      height: \"672\",\n      src: \"/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-bicycles.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We skipped any details on image embedding, measuring distances and so on. For more on these, check out our recent \", _jsx(_components.a, {\n        href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n        children: \"paper in Nature Communications\"\n      }), \" or see our \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=Iu8g2Twjn9U\",\n        children: \"set of image analytics videos on YouTube\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":300,"height":200,"src":"/blog/2020-01-neighbors-images/__optimized-images__/2020-01-08-neighbors-images-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"look-alike-images"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Machine Learning Jargon</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Machine Learning Jargon"/><meta property="og:description" content="What do &#x27;target variable&#x27;, &#x27;attribute&#x27;, and &#x27;learner&#x27; mean?"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2022-02-01"/><meta property="article:author" content="Ajda Pretnar Žagar"/><meta property="article:tag" content="orange"/><meta property="article:tag" content="education"/><meta property="article:tag" content="machine learning"/><meta property="og:image" content="/blog/2022-02-machine-learning-jargon/__optimized-images__/2022-02-01-word-cloud.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -233,4 +233,4 @@ <h3>Modelling</h3>
 </ul>
 <a href="/blog/2022-02-machine-learning-jargon/__optimized-images__/2022-02-01-learner.png" data-gallery="true"><img alt="" loading="lazy" width="380" height="190" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-02-machine-learning-jargon/__optimized-images__/2022-02-01-learner.png"/></a>
 <h3>To sum up</h3>
-<p>We hope you found this informative. This information should help you navigate Orange easily and talk more precisely about your data mining and machine learning tasks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-02-01","draft":false,"title":"Machine Learning Jargon","thumbImage":"2022-02-01-word-cloud.png","frontPageImage":"2022-02-01-word-cloud.png","blog":["orange","education","machine learning"],"shortExcerpt":"What do 'target variable', 'attribute', and 'learner' mean?","longExcerpt":"Data scientists have a specific language. Learn about what certain terms mean and become more confident in your data science speak!","x2images":false,"oldUrl":"/blog/2022/2022-02-01-machine-learning-jargon/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We all know each profession has its own jargon. Most of us remember from any hospital series something like: \\\"\\\"Pass me the scalpel, stat!\\\" Meaning \\\"give me the scalpel quickly\\\".\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data scientists are no different. We have our own terminology that requires some getting used to. Here's a list of common terms that we use in machine learning. Some terms are specific to Orange and might not be used elsewhere. The post is intended to get you familiar with concepts used in Orange.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-machine-learning-jargon/2022-02-01-word-cloud.png\",\n      width: \"600\",\n      height: \"400\",\n      src: \"/blog/2022-02-machine-learning-jargon/__optimized-images__/2022-02-01-word-cloud.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Data\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"variable\"\n          }), \", also known as \", _jsx(_components.strong, {\n            children: \"attribute\"\n          }), \" or \", _jsx(_components.strong, {\n            children: \"feature\"\n          }), \" (sometimes even \", _jsx(_components.strong, {\n            children: \"predictor\"\n          }), \"). In statistics they would be called \", _jsx(_components.strong, {\n            children: \"independent variables\"\n          }), \". This all refers to descriptions of data samples. If you have patients in rows, that variables are in columns and they describe these patients (i.e. with name, date of birth, cholesterol level, heart rate, and so on).\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"data sample\"\n          }), \", also known as \", _jsx(_components.strong, {\n            children: \"data instance\"\n          }), \". These are the rows in your table and represent the objects or subjects you are researching. For example, patients, passengers on Titanic, species of iris, or a children's fairy tales.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"categorical\"\n          }), \" or \", _jsx(_components.strong, {\n            children: \"discrete\"\n          }), \" variable. This refers to a variable with categories as values, for example eye color, nationality, ticket class (first, second, third), and so on. In Orange, these would be marked with a green C.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"numeric\"\n          }), \" or \", _jsx(_components.strong, {\n            children: \"continuous\"\n          }), \" variable. 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This refers to a variable with time as value, for example date of birth or time of the event. In Orange, these would be marked with a blue T.\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-02-machine-learning-jargon/2022-02-01-file.png\",\n      width: \"744\",\n      height: \"848\",\n      src: \"/blog/2022-02-machine-learning-jargon/__optimized-images__/2022-02-01-file.png\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"meta variable\"\n          }), \" is a variable that adds additional information on a data sample, but it is not used in computation. Such variables are text variables, but they can also be of other types. 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Orange usually continuizes by default.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"discretize\"\n          }), \" is an operation that transforms numeric variables to categorical, usually by creating value ranges or bins.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"impute\"\n          }), \" is an operation that handles missing values. Certain algorithms cannot work with missing data, so the user needs to handle them beforehand. If there are few missing values, such instances would be removed. If there are many missing values, one would usually use imputing a mean or most frequent value. 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+<p>We hope you found this informative. This information should help you navigate Orange easily and talk more precisely about your data mining and machine learning tasks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-02-01","draft":false,"title":"Machine Learning Jargon","thumbImage":"2022-02-01-word-cloud.png","frontPageImage":"2022-02-01-word-cloud.png","blog":["orange","education","machine learning"],"shortExcerpt":"What do 'target variable', 'attribute', and 'learner' mean?","longExcerpt":"Data scientists have a specific language. Learn about what certain terms mean and become more confident in your data science speak!","x2images":false,"oldUrl":"/blog/2022/2022-02-01-machine-learning-jargon/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We all know each profession has its own jargon. Most of us remember from any hospital series something like: \\\"\\\"Pass me the scalpel, stat!\\\" Meaning \\\"give me the scalpel quickly\\\".\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data scientists are no different. We have our own terminology that requires some getting used to. Here's a list of common terms that we use in machine learning. Some terms are specific to Orange and might not be used elsewhere. 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In statistics they would be called \", _jsx(_components.strong, {\n            children: \"independent variables\"\n          }), \". This all refers to descriptions of data samples. If you have patients in rows, that variables are in columns and they describe these patients (i.e. with name, date of birth, cholesterol level, heart rate, and so on).\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"data sample\"\n          }), \", also known as \", _jsx(_components.strong, {\n            children: \"data instance\"\n          }), \". These are the rows in your table and represent the objects or subjects you are researching. For example, patients, passengers on Titanic, species of iris, or a children's fairy tales.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"categorical\"\n          }), \" or \", _jsx(_components.strong, {\n            children: \"discrete\"\n          }), \" variable. This refers to a variable with categories as values, for example eye color, nationality, ticket class (first, second, third), and so on. In Orange, these would be marked with a green C.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"numeric\"\n          }), \" or \", _jsx(_components.strong, {\n            children: \"continuous\"\n          }), \" variable. This refers to a variable with numbers as values, for example cholesterol level, heart rate, age, and so on. In Orange, these would be marked with a red N.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"string\"\n          }), \" or \", _jsx(_components.strong, {\n            children: \"text\"\n          }), \" variable. This refers to a variable with text as value, for example the address, the ID of a product, or the name of the person. In Orange, these would be marked with a black S. They would always be in meta variables.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"datetime\"\n          }), \" variable. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Making Predictions</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Making Predictions"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-01-22 16:01:36+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="data"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="predictive analytics"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, data, examples, predictive analytics, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Making Predictions</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 22, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>One of the cool things about being a data scientist is being able to predict. That is, predict before we know the actual outcome. I am not talking about verifying your favorite classification algorithm here, and I am not talking about cross-validation or classification accuracies or AUC or anything like that. I am talking about the good old prediction. This is where our very own Predictions widget comes to help.</p>
 <p><a href="/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface2.png" data-gallery="true"><img alt="" loading="lazy" width="638" height="573" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface2.png"/></a>
 Predictions workflow.</p>
@@ -159,4 +159,4 @@
 <p>But surely you don&#x27;t want to go through all 150 flowers to properly match the three new Irises? So instead, let&#x27;s first train a model on the existing data set. We connect the File widget to the chosen classifier (we went with Classification Tree this time) and feed the results into Predictions. Now we write down the measurements for our new flowers into <a href="https://www.youtube.com/watch?v=MHcGdQeYCMg">Google Sheets</a> (just like above), load it into Orange with a new File widget and input the fresh data into Predictions. We can observe the predicted class directly in the widget itself.</p>
 <p><a href="/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface1.png" data-gallery="true"><img alt="" loading="lazy" width="832" height="495" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface1.png"/></a>
 Predictions made by classification tree.</p>
-<p>In the left part of the visualization we have the input data set (our measurements) and in the right part the predictions made with classification tree. By default you see probabilities for all three class values and the predicted class. You can of course use other classifiers as well - it would probably make sense to first evaluate classifiers on the existing data set, find the best one for your and then use it on the new data.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-01-22 16:01:36+00:00","draft":false,"title":"Making Predictions","blog":["analysis","data","examples","predictive analytics","widget"],"oldUrl":"/blog/2016/01/22/predictive-analytics-with-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One of the cool things about being a data scientist is being able to predict. 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Since we've \", _jsx(_components.a, {\n        href: \"/blog/2015/07/24/visualizing-misclassifications/\",\n        children: \"worked so much\"\n      }), \" with it already, I'm sure you know all about this data. But now we got three new flowers in the office and of course there's no label attached to tell us what species of Iris these flowers are. [sigh....] Obviously, we will be measuring petals and sepals and contrasting the results with our data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface3-2.png\",\n        alt: \"\",\n        width: \"658\",\n        height: \"376\"\n      }), \"\\nOur new data on three flowers. 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+<p>In the left part of the visualization we have the input data set (our measurements) and in the right part the predictions made with classification tree. By default you see probabilities for all three class values and the predicted class. You can of course use other classifiers as well - it would probably make sense to first evaluate classifiers on the existing data set, find the best one for your and then use it on the new data.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-01-22 16:01:36+00:00","draft":false,"title":"Making Predictions","blog":["analysis","data","examples","predictive analytics","widget"],"oldUrl":"/blog/2016/01/22/predictive-analytics-with-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One of the cool things about being a data scientist is being able to predict. That is, predict before we know the actual outcome. I am not talking about verifying your favorite classification algorithm here, and I am not talking about cross-validation or classification accuracies or AUC or anything like that. I am talking about the good old prediction. This is where our very own Predictions widget comes to help.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface2.png\",\n        alt: \"\",\n        width: \"638\",\n        height: \"573\"\n      }), \"\\nPredictions workflow.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will be exploring the Iris data set again, but we're going to add a little twist to it. Since we've \", _jsx(_components.a, {\n        href: \"/blog/2015/07/24/visualizing-misclassifications/\",\n        children: \"worked so much\"\n      }), \" with it already, I'm sure you know all about this data. But now we got three new flowers in the office and of course there's no label attached to tell us what species of Iris these flowers are. [sigh....] Obviously, we will be measuring petals and sepals and contrasting the results with our data.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface3-2.png\",\n        alt: \"\",\n        width: \"658\",\n        height: \"376\"\n      }), \"\\nOur new data on three flowers. We have used Google Sheets to enter the data and the copied the sharable link and pasted the link to the File widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"But surely you don't want to go through all 150 flowers to properly match the three new Irises? So instead, let's first train a model on the existing data set. We connect the File widget to the chosen classifier (we went with Classification Tree this time) and feed the results into Predictions. Now we write down the measurements for our new flowers into \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=MHcGdQeYCMg\",\n        children: \"Google Sheets\"\n      }), \" (just like above), load it into Orange with a new File widget and input the fresh data into Predictions. We can observe the predicted class directly in the widget itself.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2016-01-predictive-analytics-with-orange/__optimized-images__/predictions_new_interface1.png\",\n        alt: \"\",\n        width: \"832\",\n        height: \"495\"\n      }), \"\\nPredictions made by classification tree.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the left part of the visualization we have the input data set (our measurements) and in the right part the predictions made with classification tree. By default you see probabilities for all three class values and the predicted class. You can of course use other classifiers as well - it would probably make sense to first evaluate classifiers on the existing data set, find the best one for your and then use it on the new data.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"making-predictions"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, business intelligence, gsoc2016, orange3, recommender  system</strong></p><h1 class="sc-c67a4900-0 loxVcf">Making recommendations</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>SALVACARRION</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 19, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><em>This is a guest blog from the Google Summer of Code project.</em></p>
 <p>Recommender systems are everywhere, we can find them on YouTube, Amazon, Netflix, iTunes,... This is because they are crucial component in a competitive retail services.</p>
 <p>How can I know what you may like if I have almost no information about you? The answer: taking <a href="https://en.wikipedia.org/wiki/Collaborative_filtering">Collaborative filtering (CF)</a> approaches. Basically, this means to combine all the little knowledge we have about users and/or items in order to build a grid of knowledge with which we make recommendation.</p>
@@ -288,4 +288,4 @@ <h3>SGD optimizers</h3>
 <li><a href="http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adam">Adam</a>: Similar to AdaGrad and RMSProp but with an exponentially decaying average of past gradients.</li>
 <li><a href="http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adamax">Adamax</a>: Similar to Adam, but taking the maximum between the gradient and the velocity.</li>
 </ul>
-<p>Do you want to learn more about this? Check our <a href="http://orange3-recommendation.readthedocs.io">documentation</a>!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"SALVACARRION","date":"2016-08-19 20:42:59+00:00","draft":false,"title":"Making recommendations","blog":["addons","business intelligence","gsoc2016","orange3","recommender  system"],"oldUrl":"/blog/2016/08/19/making-recommendations/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    h3: \"h3\",\n    pre: \"pre\",\n    code: \"code\",\n    strong: \"strong\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\",\n    ul: \"ul\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Recommender systems are everywhere, we can find them on YouTube, Amazon, Netflix, iTunes,... This is because they are crucial component in a competitive retail services.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"How can I know what you may like if I have almost no information about you? The answer: taking \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Collaborative_filtering\",\n        children: \"Collaborative filtering (CF)\"\n      }), \" approaches. Basically, this means to combine all the little knowledge we have about users and/or items in order to build a grid of knowledge with which we make recommendation.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To help you with that, Biolab has written \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-recommendation\",\n        children: \"Orange3-Recommendation\"\n      }), \" - an add-on for \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/download/\",\n        children: \"Orange3\"\n      }), \" to train recommendation models, cross-validate them and make predictions.\"]\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Input data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First things first. Orange3-Recommendation can read files in native tab-delimited format, or can load data from any of the major standard spreadsheet file type, like CSV and Excel. Native format starts with a header row with feature (column) names. Second header row gives the attribute type, which can be continuous, discrete, string or time. The third header line contains meta information to identify dependent features (class), irrelevant features (ignore) or meta features (meta).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are the first few lines from a data set:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        tid      user        movie       score\\n        string   discrete    discrete    continuous\\n        meta     row=1       col=1       class\\n        1        Breza       HarrySally  2\\n        2        Dana        Cvetje      5\\n        3        Cene        Prometheus  5\\n        4        Ksenija     HarrySally  4\\n        5        Albert      Matrix      4\\n        ...\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"The third row is mandatory in this kind of datasets\"\n      }), \"*, in order to know which attributes correspond to the users (row=1) and which ones to the items (col=1). For the case of big datasets, users and items must be specified as continuous attributes due to efficiency issues. (*Note: If the meta attributes \", _jsx(_components.em, {\n        children: \"row\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"col\"\n      }), \", some simple heuristics will be applied: users=\", _jsx(_components.em, {\n        children: \"column 0\"\n      }), \", items=\", _jsx(_components.em, {\n        children: \"column 1\"\n      }), \", class=\", _jsx(_components.em, {\n        children: \"last column\"\n      }), \")\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are the first few lines from a data set :\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        user            movie         score         tid\\n        continuous      continuous    continuous    time\\n        row=1           col=1         class         meta\\n        196             242           3             881250949\\n        186             302           3             891717742\\n        22              377           1             878887116\\n        244             51            2             880606923\\n        166             346           1             886397596\\n        298             474           4             884182806\\n        ...\\n\"\n      })\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Training a model\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This step is pretty simple. To train a model we have to load the data as is described above and connect it to the learner. (\", _jsx(_components.em, {\n        children: \"Don't forget to click apply\"\n      }), \")\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/data-to-brismf.png\",\n      src: \"300\",\n      width: \"570\",\n      height: \"208\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/data-to-brismf.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If the model uses side information, we only need to add an extra file.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/TrustSVD-1.png\",\n      src: \"300\",\n      width: \"686\",\n      height: \"462\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/TrustSVD-1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In addition, we can set the parameters of our model by double-clicking it:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/Screen-Shot-2016-08-22-at-15.49.56.png\",\n      src: \"400\",\n      width: \"826\",\n      height: \"1386\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Screen-Shot-2016-08-22-at-15.49.56.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"By using a fixed seed, we make random numbers predictable. Therefore, this feature is useful if we want to compare results in a deterministic way.\"\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Cross-validation\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is as simple as it seems. The only thing to point out is that side information must be connected to the model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/cv-recommendation.png\",\n      alt: \"\",\n      width: \"1544\",\n      height: \"666\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Still, cross-validation is a robust way to see how our model is performing. I consider that it's a good idea to check how our model performs with respect to the baseline. This presents a negligible overload* in our pipeline and makes our analysis more solid. \", _jsx(_components.em, {\n        children: \"(*For 1,000,000 ratings, it can take 0.027s).\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can add a baseline leaner to \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/testandscore/\",\n        children: \"Test\u0026Score\"\n      }), \" and select the model we want to apply.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/Baselines.png\",\n      src: \"300\",\n      width: \"662\",\n      height: \"774\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Baselines.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Making recommendations\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The prediction flow is exactly the same as in \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange3\"\n      }), \".\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/Recommendation-predictions.png\",\n      src: \"400\",\n      width: \"1146\",\n      height: \"564\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Recommendation-predictions.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Analyzing low-rank matrices\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/all-rank-dis.png\",\n      alt: \"\",\n      width: \"2376\",\n      height: \"1420\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once we've output the low-rank matrices, we can play around the vectors in those matrices to discover hidden relations or understand the known ones. For instance, here we plot vector 1 and 2 from the \", _jsx(_components.em, {\n        children: \"item-feature matrix\"\n      }), \" by simply connecting Data Table with selected instances to the widget \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/scatterplot/\",\n        children: \"Scatter Plot\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Visualizing-vectors.png\",\n      alt: \"\",\n      width: \"2260\",\n      height: \"1664\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using similar approaches we can discover pretty interesting things like similarity between movies or users, how movie genres relate with each other, changes in users' behavior, when the popularity of a movie has been raised due to a commercial campaign,... and many others.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, a simple pipeline to do all of the above can be something like this:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/workflow-recommendation.png\",\n      alt: \"\",\n      width: \"2946\",\n      height: \"1694\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"On the left side we connected several models to \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/testandscore/\",\n        children: \"Test\u0026Score\"\n      }), \" in order to cross-validate them. Later, we trained a \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/widgets/svdplusplus.html\",\n        children: \"SVD++\"\n      }), \" model, made some predictions, got the low-rank matrices learnt by the model and plotted some vectors of the \", _jsx(_components.em, {\n        children: \"Item-feature matrix.\"\n      })]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Analysis (Advanced users)\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here we've made a workflow (which can be downloaded \", _jsx(_components.a, {\n        href: \"/blog/2016-08-making-recommendations/Orange3-Workflow-Movielens100k.zip\",\n        children: \"here\"\n      }), \") to perform a really basic analysis on the results obtained through factoring the user and item feature matrices with \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/widgets/brismf.html\",\n        children: \"BRISMF\"\n      }), \" over the \", _jsx(_components.a, {\n        href: \"http://grouplens.org/datasets/movielens/\",\n        children: \"movielens100k\"\n      }), \" dataset. \", _jsx(_components.em, {\n        children: \"(Note: Once downloaded, set the prepared datasets in the folder 'orange'. Probably you're gonna get a couple errors. Don't worry, it's normal. To solve it, apply the scripts sequentially but don't forget previously to select all the rows in the related Table.)\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead of explaining how this pipeline works, the best thing you can do is to download it and play with it.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Complex-flow.png\",\n      alt: \"\",\n      width: \"2880\",\n      height: \"1800\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One of the analysis you can do, is to plot the most popular movies across two first vectors of the matrix descomposition. Later, you can try to find clusters, tweak it a bit and find crossed relations (e.g. \", _jsx(_components.em, {\n        children: \"male/female\"\n      }), \" Vs._ action/drama)._\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Cluster-movies.png\",\n      alt: \"\",\n      width: \"2692\",\n      height: \"1776\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Now let's focus on the scripting part.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Rating models\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In this tutorial we are going to train a \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/rating.html#brismf\",\n        children: \"BRISMF\"\n      }), \" model.\"]\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"First we \", _jsxs(_components.strong, {\n          children: [\"import \", _jsx(_components.em, {\n            children: \"Orange\"\n          }), \" and the learner\"]\n        }), \" that we want to use:\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        import Orange\\n        from orangecontrib.recommendation import BRISMFLearner\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"2\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"After that, we have to \", _jsx(_components.strong, {\n          children: \"load a dataset\"\n        }), \":\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        data = Orange.data.Table('movielens100k.tab')\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"3\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Then we set the \", _jsx(_components.strong, {\n          children: \"learner parameters\"\n        }), \", and finally we \", _jsx(_components.strong, {\n          children: \"train it\"\n        }), \" passing the dataset as an argument (the returned value will be our model trained):\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        learner = BRISMFLearner(num_factors=15, num_iter=25, learning_rate=0.07, lmbda=0.1)\\n        recommender = learner(data)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"4\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Finally, we can \", _jsx(_components.strong, {\n          children: \"make predictions\"\n        }), \" (in this case, for the first three pairs in the dataset):\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        prediction = recommender(data[:3])\\n        print(prediction)\\n        \u003e\u003e\u003e [ 3.79505151 3.75096513 1.293013 ]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Ranking models\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At this point we can try something new, let's make recommendations for a dataset in which only binary relevance is available. For this case, \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/ranking.html\",\n        children: \"CLiMF\"\n      }), \" is model that will suit our needs.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    import numpy as np\\n    from orangecontrib.recommendation import CLiMFLearner\\n    \\n    # Load data\\n    data = Orange.data.Table('epinions_train.tab')\\n    \\n    # Train recommender\\n    learner = CLiMFLearner(num_factors=10, num_iter=10, learning_rate=0.0001, lmbda=0.001)\\n    recommender = learner(data)\\n    \\n    # Make recommendations\\n    recommender(X=5)\\n    \u003e\u003e\u003e [ 494,   803,   180, ..., 25520, 25507, 30815]\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Later, we can score the model. In this case we're using the \", _jsx(_components.em, {\n        children: \"MeanReciprocalRank:\"\n      })]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    \\n    # Load test\\n    dataset testdata = Orange.data.Table('epinions_test.tab') \\n    \\n    # Sample users \\n    num_users = len(recommender.U)\\n    num_samples = min(num_users, 1000) # max. number to sample\\n    users_sampled = np.random.choice(np.arange(num_users), num_samples) \\n    \\n    # Compute Mean Reciprocal Rank (MRR) \\n    mrr, _ = recommender.compute_mrr(data=testdata, users=users_sampled) \\n    print('MRR: %.4f' % mrr) \\n    \u003e\u003e\u003e MRR: 0.3975\\n\"\n      })\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"SGD optimizers\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This add-on includes several configurations that can be used to modify the updates on the low rank matrices during the stochastic gradient descent optimization.\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#stochastic-gradient-descent\",\n          children: \"SGD\"\n        }), \": Classical SGD update.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#momentum\",\n          children: \"Momentum\"\n        }), \": SGD with inertia.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#nesterov-s-accelerated-gradient\",\n          children: \"Nesterov momentum\"\n        }), \": A Momentum that \\\"looks ahead\\\".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adagradient\",\n          children: \"AdaGrad\"\n        }), \": Optimizer that adapts its learning rating during the process.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#rmsprop\",\n          children: \"RMSProp\"\n        }), \": \\\"Leaky\\\" AdaGrad.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adadelta\",\n          children: \"AdaDelta\"\n        }), \": Extension of Adagrad that seeks to reduce its aggressive.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adam\",\n          children: \"Adam\"\n        }), \": Similar to AdaGrad and RMSProp but with an exponentially decaying average of past gradients.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adamax\",\n          children: \"Adamax\"\n        }), \": Similar to Adam, but taking the maximum between the gradient and the velocity.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Do you want to learn more about this? Check our \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io\",\n        children: \"documentation\"\n      }), \"!\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"making-recommendations"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Do you want to learn more about this? Check our <a href="http://orange3-recommendation.readthedocs.io">documentation</a>!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"SALVACARRION","date":"2016-08-19 20:42:59+00:00","draft":false,"title":"Making recommendations","blog":["addons","business intelligence","gsoc2016","orange3","recommender  system"],"oldUrl":"/blog/2016/08/19/making-recommendations/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    h3: \"h3\",\n    pre: \"pre\",\n    code: \"code\",\n    strong: \"strong\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\",\n    ul: \"ul\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Recommender systems are everywhere, we can find them on YouTube, Amazon, Netflix, iTunes,... This is because they are crucial component in a competitive retail services.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"How can I know what you may like if I have almost no information about you? The answer: taking \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Collaborative_filtering\",\n        children: \"Collaborative filtering (CF)\"\n      }), \" approaches. Basically, this means to combine all the little knowledge we have about users and/or items in order to build a grid of knowledge with which we make recommendation.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To help you with that, Biolab has written \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-recommendation\",\n        children: \"Orange3-Recommendation\"\n      }), \" - an add-on for \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/download/\",\n        children: \"Orange3\"\n      }), \" to train recommendation models, cross-validate them and make predictions.\"]\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Input data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First things first. Orange3-Recommendation can read files in native tab-delimited format, or can load data from any of the major standard spreadsheet file type, like CSV and Excel. Native format starts with a header row with feature (column) names. Second header row gives the attribute type, which can be continuous, discrete, string or time. The third header line contains meta information to identify dependent features (class), irrelevant features (ignore) or meta features (meta).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are the first few lines from a data set:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        tid      user        movie       score\\n        string   discrete    discrete    continuous\\n        meta     row=1       col=1       class\\n        1        Breza       HarrySally  2\\n        2        Dana        Cvetje      5\\n        3        Cene        Prometheus  5\\n        4        Ksenija     HarrySally  4\\n        5        Albert      Matrix      4\\n        ...\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"The third row is mandatory in this kind of datasets\"\n      }), \"*, in order to know which attributes correspond to the users (row=1) and which ones to the items (col=1). For the case of big datasets, users and items must be specified as continuous attributes due to efficiency issues. (*Note: If the meta attributes \", _jsx(_components.em, {\n        children: \"row\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"col\"\n      }), \", some simple heuristics will be applied: users=\", _jsx(_components.em, {\n        children: \"column 0\"\n      }), \", items=\", _jsx(_components.em, {\n        children: \"column 1\"\n      }), \", class=\", _jsx(_components.em, {\n        children: \"last column\"\n      }), \")\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are the first few lines from a data set :\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        user            movie         score         tid\\n        continuous      continuous    continuous    time\\n        row=1           col=1         class         meta\\n        196             242           3             881250949\\n        186             302           3             891717742\\n        22              377           1             878887116\\n        244             51            2             880606923\\n        166             346           1             886397596\\n        298             474           4             884182806\\n        ...\\n\"\n      })\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Training a model\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This step is pretty simple. To train a model we have to load the data as is described above and connect it to the learner. (\", _jsx(_components.em, {\n        children: \"Don't forget to click apply\"\n      }), \")\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/data-to-brismf.png\",\n      src: \"300\",\n      width: \"570\",\n      height: \"208\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/data-to-brismf.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If the model uses side information, we only need to add an extra file.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/TrustSVD-1.png\",\n      src: \"300\",\n      width: \"686\",\n      height: \"462\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/TrustSVD-1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In addition, we can set the parameters of our model by double-clicking it:\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/Screen-Shot-2016-08-22-at-15.49.56.png\",\n      src: \"400\",\n      width: \"826\",\n      height: \"1386\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Screen-Shot-2016-08-22-at-15.49.56.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"By using a fixed seed, we make random numbers predictable. Therefore, this feature is useful if we want to compare results in a deterministic way.\"\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Cross-validation\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is as simple as it seems. The only thing to point out is that side information must be connected to the model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/cv-recommendation.png\",\n      alt: \"\",\n      width: \"1544\",\n      height: \"666\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Still, cross-validation is a robust way to see how our model is performing. I consider that it's a good idea to check how our model performs with respect to the baseline. This presents a negligible overload* in our pipeline and makes our analysis more solid. \", _jsx(_components.em, {\n        children: \"(*For 1,000,000 ratings, it can take 0.027s).\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can add a baseline leaner to \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/testandscore/\",\n        children: \"Test\u0026Score\"\n      }), \" and select the model we want to apply.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/Baselines.png\",\n      src: \"300\",\n      width: \"662\",\n      height: \"774\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Baselines.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Making recommendations\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The prediction flow is exactly the same as in \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange3\"\n      }), \".\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2016-08-making-recommendations/Recommendation-predictions.png\",\n      src: \"400\",\n      width: \"1146\",\n      height: \"564\",\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Recommendation-predictions.png\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Analyzing low-rank matrices\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/all-rank-dis.png\",\n      alt: \"\",\n      width: \"2376\",\n      height: \"1420\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once we've output the low-rank matrices, we can play around the vectors in those matrices to discover hidden relations or understand the known ones. For instance, here we plot vector 1 and 2 from the \", _jsx(_components.em, {\n        children: \"item-feature matrix\"\n      }), \" by simply connecting Data Table with selected instances to the widget \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/scatterplot/\",\n        children: \"Scatter Plot\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Visualizing-vectors.png\",\n      alt: \"\",\n      width: \"2260\",\n      height: \"1664\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using similar approaches we can discover pretty interesting things like similarity between movies or users, how movie genres relate with each other, changes in users' behavior, when the popularity of a movie has been raised due to a commercial campaign,... and many others.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, a simple pipeline to do all of the above can be something like this:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/workflow-recommendation.png\",\n      alt: \"\",\n      width: \"2946\",\n      height: \"1694\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"On the left side we connected several models to \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/testandscore/\",\n        children: \"Test\u0026Score\"\n      }), \" in order to cross-validate them. Later, we trained a \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/widgets/svdplusplus.html\",\n        children: \"SVD++\"\n      }), \" model, made some predictions, got the low-rank matrices learnt by the model and plotted some vectors of the \", _jsx(_components.em, {\n        children: \"Item-feature matrix.\"\n      })]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Analysis (Advanced users)\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here we've made a workflow (which can be downloaded \", _jsx(_components.a, {\n        href: \"/blog/2016-08-making-recommendations/Orange3-Workflow-Movielens100k.zip\",\n        children: \"here\"\n      }), \") to perform a really basic analysis on the results obtained through factoring the user and item feature matrices with \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/widgets/brismf.html\",\n        children: \"BRISMF\"\n      }), \" over the \", _jsx(_components.a, {\n        href: \"http://grouplens.org/datasets/movielens/\",\n        children: \"movielens100k\"\n      }), \" dataset. \", _jsx(_components.em, {\n        children: \"(Note: Once downloaded, set the prepared datasets in the folder 'orange'. Probably you're gonna get a couple errors. Don't worry, it's normal. To solve it, apply the scripts sequentially but don't forget previously to select all the rows in the related Table.)\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead of explaining how this pipeline works, the best thing you can do is to download it and play with it.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Complex-flow.png\",\n      alt: \"\",\n      width: \"2880\",\n      height: \"1800\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One of the analysis you can do, is to plot the most popular movies across two first vectors of the matrix descomposition. Later, you can try to find clusters, tweak it a bit and find crossed relations (e.g. \", _jsx(_components.em, {\n        children: \"male/female\"\n      }), \" Vs._ action/drama)._\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-making-recommendations/__optimized-images__/Cluster-movies.png\",\n      alt: \"\",\n      width: \"2692\",\n      height: \"1776\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Now let's focus on the scripting part.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Rating models\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In this tutorial we are going to train a \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/rating.html#brismf\",\n        children: \"BRISMF\"\n      }), \" model.\"]\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"First we \", _jsxs(_components.strong, {\n          children: [\"import \", _jsx(_components.em, {\n            children: \"Orange\"\n          }), \" and the learner\"]\n        }), \" that we want to use:\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        import Orange\\n        from orangecontrib.recommendation import BRISMFLearner\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"2\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"After that, we have to \", _jsx(_components.strong, {\n          children: \"load a dataset\"\n        }), \":\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        data = Orange.data.Table('movielens100k.tab')\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"3\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Then we set the \", _jsx(_components.strong, {\n          children: \"learner parameters\"\n        }), \", and finally we \", _jsx(_components.strong, {\n          children: \"train it\"\n        }), \" passing the dataset as an argument (the returned value will be our model trained):\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        learner = BRISMFLearner(num_factors=15, num_iter=25, learning_rate=0.07, lmbda=0.1)\\n        recommender = learner(data)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"4\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Finally, we can \", _jsx(_components.strong, {\n          children: \"make predictions\"\n        }), \" (in this case, for the first three pairs in the dataset):\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"        prediction = recommender(data[:3])\\n        print(prediction)\\n        \u003e\u003e\u003e [ 3.79505151 3.75096513 1.293013 ]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"Ranking models\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At this point we can try something new, let's make recommendations for a dataset in which only binary relevance is available. For this case, \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/ranking.html\",\n        children: \"CLiMF\"\n      }), \" is model that will suit our needs.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    import numpy as np\\n    from orangecontrib.recommendation import CLiMFLearner\\n    \\n    # Load data\\n    data = Orange.data.Table('epinions_train.tab')\\n    \\n    # Train recommender\\n    learner = CLiMFLearner(num_factors=10, num_iter=10, learning_rate=0.0001, lmbda=0.001)\\n    recommender = learner(data)\\n    \\n    # Make recommendations\\n    recommender(X=5)\\n    \u003e\u003e\u003e [ 494,   803,   180, ..., 25520, 25507, 30815]\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Later, we can score the model. In this case we're using the \", _jsx(_components.em, {\n        children: \"MeanReciprocalRank:\"\n      })]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import Orange\\n    \\n    # Load test\\n    dataset testdata = Orange.data.Table('epinions_test.tab') \\n    \\n    # Sample users \\n    num_users = len(recommender.U)\\n    num_samples = min(num_users, 1000) # max. number to sample\\n    users_sampled = np.random.choice(np.arange(num_users), num_samples) \\n    \\n    # Compute Mean Reciprocal Rank (MRR) \\n    mrr, _ = recommender.compute_mrr(data=testdata, users=users_sampled) \\n    print('MRR: %.4f' % mrr) \\n    \u003e\u003e\u003e MRR: 0.3975\\n\"\n      })\n    }), \"\\n\", _jsx(_components.h3, {}), \"\\n\", _jsx(_components.h3, {\n      children: \"SGD optimizers\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This add-on includes several configurations that can be used to modify the updates on the low rank matrices during the stochastic gradient descent optimization.\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#stochastic-gradient-descent\",\n          children: \"SGD\"\n        }), \": Classical SGD update.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#momentum\",\n          children: \"Momentum\"\n        }), \": SGD with inertia.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#nesterov-s-accelerated-gradient\",\n          children: \"Nesterov momentum\"\n        }), \": A Momentum that \\\"looks ahead\\\".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adagradient\",\n          children: \"AdaGrad\"\n        }), \": Optimizer that adapts its learning rating during the process.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#rmsprop\",\n          children: \"RMSProp\"\n        }), \": \\\"Leaky\\\" AdaGrad.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adadelta\",\n          children: \"AdaDelta\"\n        }), \": Extension of Adagrad that seeks to reduce its aggressive.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adam\",\n          children: \"Adam\"\n        }), \": Similar to AdaGrad and RMSProp but with an exponentially decaying average of past gradients.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"http://orange3-recommendation.readthedocs.io/en/latest/scripting/optimizers.html#adamax\",\n          children: \"Adamax\"\n        }), \": Similar to Adam, but taking the maximum between the gradient and the velocity.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Do you want to learn more about this? Check our \", _jsx(_components.a, {\n        href: \"http://orange3-recommendation.readthedocs.io\",\n        children: \"documentation\"\n      }), \"!\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"making-recommendations"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>edit, domain, data</strong></p><h1 class="sc-c67a4900-0 loxVcf">Managing Data with Edit Domain</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 19, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Importing data into Orange is easy. File, import, and voila, your data is here. But what about if you want to rename a variable, change it&#x27;s type or edit labels? Edit Domain to the rescue!</p>
 <p>First of all, what is &#x27;domain&#x27;. Domain is like a metadata of your data - it describes column names, column types (categorical, numeric, string, datetime), and values for categorical variables. You will come across domain everywhere in Orange, because Orange&#x27;s table (Orange.data.Table for programmers) is nothing without it.</p>
 <p>Edit Domain helps you organize the domain of your data. Let us use Datasets widget and load HDI, a dataset of human development index for most countries in the world. We have 188 rows (countries) and 66 features (index variables).</p>
@@ -166,4 +166,4 @@
 <a href="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-rename-label-sort.png" data-gallery="true"><img alt="" loading="lazy" width="713" height="536" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-rename-label-sort.png"/></a>
 <a href="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-line-plot.png" data-gallery="true"><img alt="" loading="lazy" width="857" height="578" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-line-plot.png"/></a>
 <p>Edit Domain is a great widget to organize your data. See documentation for other great widgets, such as Create Class or Feature Statistics.</p>
-<a href="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="318" height="193" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-06-19","draft":false,"title":"Managing Data with Edit Domain","thumbImage":"2020-06-19-edit-domain-small.png","frontPageImage":"2020-06-19-edit-domain-small.png","blog":["edit","domain","data"],"shortExcerpt":"Handle your data with Edit Domain.","longExcerpt":"How to handle your data with Edit Domain - rename, change type, merge, sort...","x2images":false,"oldUrl":"/blog/2020/2020-06-19-edit-domain/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Importing data into Orange is easy. File, import, and voila, your data is here. But what about if you want to rename a variable, change it's type or edit labels? Edit Domain to the rescue!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First of all, what is 'domain'. Domain is like a metadata of your data - it describes column names, column types (categorical, numeric, string, datetime), and values for categorical variables. You will come across domain everywhere in Orange, because Orange's table (Orange.data.Table for programmers) is nothing without it.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Edit Domain helps you organize the domain of your data. Let us use Datasets widget and load HDI, a dataset of human development index for most countries in the world. We have 188 rows (countries) and 66 features (index variables).\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-data-table.png\",\n      width: \"770\",\n      height: \"490\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, let us check the domain in Edit Domain. First of all, our variable names are long, so we can make them shorter with Edit Domain. Select, say, \", _jsx(_components.em, {\n        children: \"Gross National Income (GNI) per capita\"\n      }), \" and rename it to GNI. Simple! If you are unhappy with the change, simply press \", _jsx(_components.em, {\n        children: \"Reset Selected\"\n      }), \" at the bottom of the widget.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-rename.png\",\n      width: \"631\",\n      height: \"505\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-rename.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Scroll down and check the rest of the variables. We have two categorical variables in the data, child labour and maternity leave. Let us check \", _jsx(_components.em, {\n        children: \"Child labour  (% ages 5-14) 2009-2015\"\n      }), \" and rename it first to \", _jsx(_components.em, {\n        children: \"Child labour\"\n      }), \". You can see the variable has many values, from 1 to 47. How about we merge the less frequent values into one?\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-merge-infrequent1.png\",\n      width: \"540\",\n      height: \"455\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-merge-infrequent1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Press \", _jsx(_components.em, {\n        children: \"M\"\n      }), \" on the right side of the widget and a new window will pop up. There are several ways to group less frequent variables, but let's go with merging all but top 10 most frequent values. Let us also change the label of these values from \", _jsx(_components.em, {\n        children: \"other\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"infrequent\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-merge-infrequent2.png\",\n      width: \"549\",\n      height: \"631\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-merge-infrequent2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, we will select \", _jsx(_components.em, {\n        children: \"Country\"\n      }), \" from the list and set it as a categorical variable. Click on Type drop down and select Categorical. This will reinterpret the variable according to the selected type. Now you can also see the values of the reinterpreted variable. 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If you connect \", _jsx(_components.strong, {\n        children: \"Line Plot\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Edit Domain\"\n      }), \" and set Group by to \", _jsx(_components.em, {\n        children: \"Country\"\n      }), \", Antigua and Barbuda will have a long name and will be placed at the top.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-rename-label-sort.png\",\n      width: \"713\",\n      height: \"536\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-rename-label-sort.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-line-plot.png\",\n      width: \"857\",\n      height: \"578\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-line-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Edit Domain is a great widget to organize your data. 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+<a href="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="318" height="193" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-06-19","draft":false,"title":"Managing Data with Edit Domain","thumbImage":"2020-06-19-edit-domain-small.png","frontPageImage":"2020-06-19-edit-domain-small.png","blog":["edit","domain","data"],"shortExcerpt":"Handle your data with Edit Domain.","longExcerpt":"How to handle your data with Edit Domain - rename, change type, merge, sort...","x2images":false,"oldUrl":"/blog/2020/2020-06-19-edit-domain/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot, WorkflowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Importing data into Orange is easy. 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We have 188 rows (countries) and 66 features (index variables).\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-data-table.png\",\n      width: \"770\",\n      height: \"490\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now, let us check the domain in Edit Domain. First of all, our variable names are long, so we can make them shorter with Edit Domain. Select, say, \", _jsx(_components.em, {\n        children: \"Gross National Income (GNI) per capita\"\n      }), \" and rename it to GNI. Simple! If you are unhappy with the change, simply press \", _jsx(_components.em, {\n        children: \"Reset Selected\"\n      }), \" at the bottom of the widget.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-rename.png\",\n      width: \"631\",\n      height: \"505\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-rename.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Scroll down and check the rest of the variables. We have two categorical variables in the data, child labour and maternity leave. Let us check \", _jsx(_components.em, {\n        children: \"Child labour  (% ages 5-14) 2009-2015\"\n      }), \" and rename it first to \", _jsx(_components.em, {\n        children: \"Child labour\"\n      }), \". You can see the variable has many values, from 1 to 47. How about we merge the less frequent values into one?\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-merge-infrequent1.png\",\n      width: \"540\",\n      height: \"455\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-merge-infrequent1.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Press \", _jsx(_components.em, {\n        children: \"M\"\n      }), \" on the right side of the widget and a new window will pop up. There are several ways to group less frequent variables, but let's go with merging all but top 10 most frequent values. Let us also change the label of these values from \", _jsx(_components.em, {\n        children: \"other\"\n      }), \" to \", _jsx(_components.em, {\n        children: \"infrequent\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-merge-infrequent2.png\",\n      width: \"549\",\n      height: \"631\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-merge-infrequent2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, we will select \", _jsx(_components.em, {\n        children: \"Country\"\n      }), \" from the list and set it as a categorical variable. Click on Type drop down and select Categorical. This will reinterpret the variable according to the selected type. Now you can also see the values of the reinterpreted variable. Double-click \", _jsx(_components.em, {\n        children: \"Antigua and Barb.\"\n      }), \" and rename it to \", _jsx(_components.em, {\n        children: \"Antigua and Barbuda\"\n      }), \". You can use up and down arrows to change the order of the variable. Let us push Antigua and Barbuda to the top. The order will be evident in, say, visualizations and their legends. If you connect \", _jsx(_components.strong, {\n        children: \"Line Plot\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Edit Domain\"\n      }), \" and set Group by to \", _jsx(_components.em, {\n        children: \"Country\"\n      }), \", Antigua and Barbuda will have a long name and will be placed at the top.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-rename-label-sort.png\",\n      width: \"713\",\n      height: \"536\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-rename-label-sort.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2020-06-edit-domain/2020-06-19-line-plot.png\",\n      width: \"857\",\n      height: \"578\",\n      src: \"/blog/2020-06-edit-domain/__optimized-images__/2020-06-19-line-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Edit Domain is a great widget to organize your data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>development</strong></p><h1 class="sc-c67a4900-0 loxVcf">Meet Trubar, a friend of Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 17, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange has been translated to Slovenian language (no official release yet: rough corners are very much being polished). This will pave the way for translations into other languages.</p>
 <a href="/blog/2023-01-trubar/__optimized-images__/2023-01-17-table.png" data-gallery="true"><img alt="" loading="lazy" width="1096" height="1016" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-01-trubar/__optimized-images__/2023-01-17-table.png"/></a>
 <p>What do we use? Gettext, right? Wrong. Orange is written in modern Python and uses f-strings for string interpolation. They are great, but don&#x27;t play well with gettext.</p>
@@ -206,4 +206,4 @@ <h2>What about plural forms?</h2>
 <p>Developed by native speaker of this beautifully complicated Slavic language, Trubar handles it with excellence. The beauty of f-strings is that they make implementation of plural forms much simpler because they allow using arbitrary functions. We simply inserted the appropriate function call and that&#x27;s it. For instances, besides having four plural forms, the translation of proposition &quot;with&quot; before a number depends upon the number, and this is handled simply by using the function that returns the proper former for the given number. Which all happens without any help in the original source.</p>
 <p>More about localization in <a href="http://janezd.github.io/trubar/localization/">documentation</a>.</p>
 <h2>Ready to use?</h2>
-<p>Sure, try it out. Trubar is <a href="https://pypi.org/project/trubar/">pip-installable</a>, <a href="http://janezd.github.io/trubar/">documentation</a> is decent enough and although it is still early in development, we don&#x27;t plan any huge changes. The least we can promise is that its compatibility-breaking changes will give you less headache that those by pyqtgraph. (Though ... this may not be much of a promise. :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2023-01-17","draft":false,"title":"Meet Trubar, a friend of Orange","thumbImage":"2023-01-20-trubar-brez.png","frontPageImage":"2023-01-20-trubar-brez.png","blog":["development"],"shortExcerpt":"A new, general utility for localization of Python software","longExcerpt":"A new, general utility for localization of Python software, developed for Orange","oldUrl":"/blog/2023/2023-01-17-trubar/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\",\n    code: \"code\",\n    pre: \"pre\",\n    span: \"span\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange has been translated to Slovenian language (no official release yet: rough corners are very much being polished). This will pave the way for translations into other languages.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-01-trubar/2023-01-17-table.png\",\n      width: \"1096\",\n      height: \"1016\",\n      src: \"/blog/2023-01-trubar/__optimized-images__/2023-01-17-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What do we use? Gettext, right? Wrong. Orange is written in modern Python and uses f-strings for string interpolation. They are great, but don't play well with gettext.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why gettext doesn't cut it\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If you've never heard about gettext: \", _jsx(_components.a, {\n        href: \"https://www.gnu.org/software/gettext/\",\n        children: \"gettext\"\n      }), \" is a popular framework for software localization. Developers enclose every string that needs to be translated into a function call that translates it. The function would be called something short, like \", _jsx(_components.code, {\n        children: \"_\"\n      }), \" or \", _jsx(_components.code, {\n        children: \"tr\"\n      }), \". One of gettext's utilities extracts all such strings into messages files. Translators provide translations. Messages are compiled into binary form and imported into the program. When function \", _jsx(_components.code, {\n        children: \"_\"\n      }), \" (or \", _jsx(_components.code, {\n        children: \"tr\"\n      }), \" or whatever) gets a string, it searches for a translation and returns it.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"So in Python, a programmer would write \", _jsx(_components.code, {\n        children: \"print(tr(\\\"Data contains {n} instances\\\").format(n)))\"\n      }), \". Translator would see the message \", _jsx(_components.code, {\n        children: \"\\\"Data contains {n} instances\\\"\"\n      }), \" and provide a translation, for instance \", _jsx(_components.code, {\n        children: \"\\\"Podatki vsebujejo {n} primerov.\\\"\"\n      }), \". After messages are compiled and in their place, the function \", _jsx(_components.code, {\n        children: \"tr\"\n      }), \" would receive the English message and return the Slovenian translation.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The above example uses the old-style interpolation with \", _jsx(_components.code, {\n        children: \"format\"\n      }), \". is does not work for Python's f-strings. With f-strings, we would have \", _jsx(_components.code, {\n        children: \"print(tr(f\\\"Data contains {n} instances\\\")\"\n      }), \". 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To our knowledge, there is currently no tool to support translation of f-strings.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Luckily, we are programmers.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Enters Trubar\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We developed Trubar. Named after Primož Trubar (the author of the first Slovenian printed books, Catechismus and Abecedarium, and the first translator of part of the Bible in Slovenian language), Trubar collects all strings from sources and places them into a yaml-like file. There, they can be translated - or marked as strings that must not be translated. 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{\n          className: \"hljs-literal\",\n          children: \"false\"\n        }), \"\\n            \", _jsx(_components.span, {\n          className: \"hljs-attr\",\n          children: \"Color by instance classes:\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"Obarvaj\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"primere\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"glede\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"na\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"razred\"\n        }), \"\\n            \", _jsx(_components.span, {\n          className: \"hljs-attr\",\n          children: \"Selection:\"\n        }), \" \", _jsx(_components.span, {\n          className: 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\"hljs-string\",\n          children: \"Izvirni\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"vrstni\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"red\"\n        }), \"\\n        \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"`set_dataset`:\"\n        }), \"\\n            \", _jsx(_components.span, {\n          className: \"hljs-attr\",\n          children: \"name:\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"false\"\n        }), \"\\n            \", _jsx(_components.span, {\n          className: \"hljs-attr\",\n          children: \"Data:\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"Podatki\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If it looks simple, it's because \", _jsxs(_components.a, {\n        href: \"http://janezd.github.io/trubar/getting-started/\",\n        children: [\"it \", _jsx(_components.em, {\n          children: \"is\"\n        }), \" simple\"]\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Besides working with f-strings, the advantage of Trubar's approach is that - unlike gettext - it does not pollute the sources with calls and parentheses. There's enough of them already. Instead of marking strings for translation within the source code, they are marked (for non-translation) in the message file. This must be done at some point, but it's better to do it in a dedicated place, not in the code.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This of course comes at a cost: unlike with gettext where the user can switch between languages, software translated with Trubar requires a separate distribution for each language. Or packing separate sources for multiple languages into one distribution. Whether this hurts or not, depends upon circumstances.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"What about plural forms?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You ask that seriously? So you don't speak Slovenian, I guess.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"See the text \\\"Ni ciljne spremenljivke\\\" in the screenshot? It means \\\"No target variable\\\". Slovenian language counts like this:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"Ni ciljne spremenljivke\\nEna ciljna spremenljivka\\nDve ciljni spremenljivki\\nTri ciljne spremenljivke\\nŠtiri ciljne spremenljivke\\nPet ciljnih spremenljivk\\n...\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"and then the same form is used until 101, where we go back to singular, dual, plural, plural and genitive (which is what you see above).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Developed by native speaker of this beautifully complicated Slavic language, Trubar handles it with excellence. The beauty of f-strings is that they make implementation of plural forms much simpler because they allow using arbitrary functions. We simply inserted the appropriate function call and that's it. 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\ No newline at end of file
+<p>Sure, try it out. Trubar is <a href="https://pypi.org/project/trubar/">pip-installable</a>, <a href="http://janezd.github.io/trubar/">documentation</a> is decent enough and although it is still early in development, we don&#x27;t plan any huge changes. The least we can promise is that its compatibility-breaking changes will give you less headache that those by pyqtgraph. (Though ... this may not be much of a promise. :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2023-01-17","draft":false,"title":"Meet Trubar, a friend of Orange","thumbImage":"2023-01-20-trubar-brez.png","frontPageImage":"2023-01-20-trubar-brez.png","blog":["development"],"shortExcerpt":"A new, general utility for localization of Python software","longExcerpt":"A new, general utility for localization of Python software, developed for Orange","oldUrl":"/blog/2023/2023-01-17-trubar/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\",\n    code: \"code\",\n    pre: \"pre\",\n    span: \"span\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange has been translated to Slovenian language (no official release yet: rough corners are very much being polished). 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@@ -154,4 +154,4 @@
 <p>For a long time, the idea was to utilize the friendly nature of Miniconda to install Orange dependencies, which often misbehaved on some platforms. Miniconda provides Orange with Python 3.6 and conda installer, which is then used to handle everything Orange needs for proper functioning. So sssssss-mooth!</p>
 <p><a href="https://orange.biolab.si/download/files/Orange3-3.4.4-Miniconda-x86_64.exe">Miniconda Installer</a></p>
 <p>Please know that our Miniconda installer is in a beta state, but we are inviting adventurous testers to try it and report any bugs they find to our <a href="https://github.com/biolab/orange3/issues/new?title=Problem%20with%20miniconda%20installer">issue tracker</a> [there won&#x27;t be any of course! ;) ].</p>
-<p>Happy testing! 🐍|🍊</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-07-14 13:30:47+00:00","draft":false,"title":"Miniconda Installer","blog":["distribution","download","orange3"],"oldUrl":"/blog/2017/07/14/miniconda-installer/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange has a new friend! It's Miniconda, Anaconda's little sister.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For a long time, the idea was to utilize the friendly nature of Miniconda to install Orange dependencies, which often misbehaved on some platforms. Miniconda provides Orange with Python 3.6 and conda installer, which is then used to handle everything Orange needs for proper functioning. So sssssss-mooth!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download/files/Orange3-3.4.4-Miniconda-x86_64.exe\",\n        children: \"Miniconda Installer\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Please know that our Miniconda installer is in a beta state, but we are inviting adventurous testers to try it and report any bugs they find to our \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/issues/new?title=Problem%20with%20miniconda%20installer\",\n        children: \"issue tracker\"\n      }), \" [there won't be any of course! ;) ].\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy testing! 🐍|🍊\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"miniconda-installer"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Happy testing! 🐍|🍊</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-07-14 13:30:47+00:00","draft":false,"title":"Miniconda Installer","blog":["distribution","download","orange3"],"oldUrl":"/blog/2017/07/14/miniconda-installer/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange has a new friend! It's Miniconda, Anaconda's little sister.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For a long time, the idea was to utilize the friendly nature of Miniconda to install Orange dependencies, which often misbehaved on some platforms. Miniconda provides Orange with Python 3.6 and conda installer, which is then used to handle everything Orange needs for proper functioning. So sssssss-mooth!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.a, {\n        href: \"https://orange.biolab.si/download/files/Orange3-3.4.4-Miniconda-x86_64.exe\",\n        children: \"Miniconda Installer\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Please know that our Miniconda installer is in a beta state, but we are inviting adventurous testers to try it and report any bugs they find to our \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/issues/new?title=Problem%20with%20miniconda%20installer\",\n        children: \"issue tracker\"\n      }), \" [there won't be any of course! ;) ].\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy testing! 🐍|🍊\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"miniconda-installer"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Mining our own data</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Mining our own data"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-11-27 08:37:03+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="data"/><meta property="article:tag" content="distribution"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, data, distribution, orange3, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Mining our own data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 27, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Recently we&#x27;ve made a short survey that was, upon Orange download, asking people how they found out about Orange, what was their data mining level and where do they work. The main purpose of this is to get a better insight into our user base and to figure out what is the profile of people interested in trying Orange.</p>
 <p>Here we have some preliminary results that we&#x27;ve managed to gather in the past three weeks or so. Obviously we will use Orange to help us make sense of the data.</p>
 <p>We&#x27;ve downloaded our data from Typeform and appended some background information such as OS and browser. Let&#x27;s see what we&#x27;ve got in the Data Table widget.</p>
@@ -175,4 +175,4 @@
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 <p>Obviously, this is a very simple analysis. But even such simple tasks are never boring with good visualization tools such as Distributions and Mosaic Display. You could also use Venn Diagram to find common features of selected subsets or perhaps Sieve Diagram for probabilities.</p>
 <p>We are very happy to get these data and we would like to thank everyone who completed the survey. If you wish to help us further, please fill out a <a href="https://orange-software.typeform.com/to/c73R43">longer survey</a> that won&#x27;t actually take you more than 3 minutes of your time (we timed it!).</p>
-<p>Happy Friday everyone!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-11-27 08:37:03+00:00","draft":false,"title":"Mining our own data","blog":["analysis","data","distribution","orange3","visualization"],"oldUrl":"/blog/2015/11/27/mining-our-own-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently we've made a short survey that was, upon Orange download, asking people how they found out about Orange, what was their data mining level and where do they work. The main purpose of this is to get a better insight into our user base and to figure out what is the profile of people interested in trying Orange.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here we have some preliminary results that we've managed to gather in the past three weeks or so. Obviously we will use Orange to help us make sense of the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We've downloaded our data from Typeform and appended some background information such as OS and browser. Let's see what we've got in the Data Table widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results7.png\",\n      alt: \"\",\n      width: \"1446\",\n      height: \"539\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, this is our entire data table. Here we also have the data on people who completed the survey and who didn't. First, let's organize the data properly. We'll do this with Select Columns widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results.png\",\n      alt: \"\",\n      width: \"597\",\n      height: \"430\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We removed all the meta attributes as they are not very relevant for our analysis. Next we moved the 'completed' attribute into target variable, thus making it our class variable.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results21.png\",\n      alt: \"\",\n      width: \"974\",\n      height: \"809\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we would like to see some basic distributions from our data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results3.png\",\n      alt: \"\",\n      width: \"856\",\n      height: \"515\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interesting. Most of our users are working on Windows, a few on Mac and very few on Linux.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's investigate further. Now we want to know more about those people who actually completed the survey. Let's use Select Columns again, this time removing os_type, os_name, agent_name and completed from our data and keeping just the answers. We made \\\"Where do you work?\\\" our class variable, but we could use either one of the three. Another trick is to set in directly in Distributions widget under 'Group by'.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results4.png\",\n      alt: \"\",\n      width: \"974\",\n      height: \"809\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, let's again use Distributions - this is such a simple way to get a good sense of your data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results5.png\",\n      alt: \"\",\n      width: \"739\",\n      height: \"515\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Obviously out of those who found out about Orange in college, most are students, but what's interesting here is that there are so many. We can also see that out of those who found us on the web, most come from the private sector, followed by academia and researchers. Good. How about the other question?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results6.png\",\n      alt: \"\",\n      width: \"739\",\n      height: \"515\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Again, results are not particularly shocking, but it's great to confirm your hypothesis with real data. Out of beginner level data miners, most are students, while most intermediate users come from the industry.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A quick look at the Mosaic Display will give us a good overview:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results81.png\",\n      alt: \"\",\n      width: \"820\",\n      height: \"589\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Yup, this sums it up quite nicely. We have lots of beginner levels users and not many expert ones (height of the box). Also most people found out about Orange on the web or in college (width of the box). A thin line on the left shows apriori distribution, thus making it easier to compare expected and actual number of instances. For example, there should be at least some people who are students and have found out about Orange at a conference. But there aren't - a contrast between how much red there should be in the box (line on the left) and how much there actually is (bigger part of the box) is quite telling. We can even select all the beginner level users who found out about Orange in college and further inspect the data, but be it enough for now.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our final workflow:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results121.png\",\n      alt: \"\",\n      width: \"796\",\n      height: \"549\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Obviously, this is a very simple analysis. But even such simple tasks are never boring with good visualization tools such as Distributions and Mosaic Display. 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If you wish to help us further, please fill out a \", _jsx(_components.a, {\n        href: \"https://orange-software.typeform.com/to/c73R43\",\n        children: \"longer survey\"\n      }), \" that won't actually take you more than 3 minutes of your time (we timed it!).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy Friday everyone!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"mining-our-own-data"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Happy Friday everyone!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-11-27 08:37:03+00:00","draft":false,"title":"Mining our own data","blog":["analysis","data","distribution","orange3","visualization"],"oldUrl":"/blog/2015/11/27/mining-our-own-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently we've made a short survey that was, upon Orange download, asking people how they found out about Orange, what was their data mining level and where do they work. The main purpose of this is to get a better insight into our user base and to figure out what is the profile of people interested in trying Orange.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here we have some preliminary results that we've managed to gather in the past three weeks or so. Obviously we will use Orange to help us make sense of the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We've downloaded our data from Typeform and appended some background information such as OS and browser. Let's see what we've got in the Data Table widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results7.png\",\n      alt: \"\",\n      width: \"1446\",\n      height: \"539\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, this is our entire data table. Here we also have the data on people who completed the survey and who didn't. First, let's organize the data properly. We'll do this with Select Columns widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results.png\",\n      alt: \"\",\n      width: \"597\",\n      height: \"430\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We removed all the meta attributes as they are not very relevant for our analysis. Next we moved the 'completed' attribute into target variable, thus making it our class variable.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results21.png\",\n      alt: \"\",\n      width: \"974\",\n      height: \"809\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we would like to see some basic distributions from our data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results3.png\",\n      alt: \"\",\n      width: \"856\",\n      height: \"515\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interesting. Most of our users are working on Windows, a few on Mac and very few on Linux.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's investigate further. Now we want to know more about those people who actually completed the survey. Let's use Select Columns again, this time removing os_type, os_name, agent_name and completed from our data and keeping just the answers. We made \\\"Where do you work?\\\" our class variable, but we could use either one of the three. Another trick is to set in directly in Distributions widget under 'Group by'.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results4.png\",\n      alt: \"\",\n      width: \"974\",\n      height: \"809\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, let's again use Distributions - this is such a simple way to get a good sense of your data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results5.png\",\n      alt: \"\",\n      width: \"739\",\n      height: \"515\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Obviously out of those who found out about Orange in college, most are students, but what's interesting here is that there are so many. We can also see that out of those who found us on the web, most come from the private sector, followed by academia and researchers. Good. How about the other question?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results6.png\",\n      alt: \"\",\n      width: \"739\",\n      height: \"515\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Again, results are not particularly shocking, but it's great to confirm your hypothesis with real data. Out of beginner level data miners, most are students, while most intermediate users come from the industry.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A quick look at the Mosaic Display will give us a good overview:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results81.png\",\n      alt: \"\",\n      width: \"820\",\n      height: \"589\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Yup, this sums it up quite nicely. We have lots of beginner levels users and not many expert ones (height of the box). Also most people found out about Orange on the web or in college (width of the box). A thin line on the left shows apriori distribution, thus making it easier to compare expected and actual number of instances. For example, there should be at least some people who are students and have found out about Orange at a conference. But there aren't - a contrast between how much red there should be in the box (line on the left) and how much there actually is (bigger part of the box) is quite telling. We can even select all the beginner level users who found out about Orange in college and further inspect the data, but be it enough for now.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our final workflow:\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-11-mining-our-own-data/__optimized-images__/blog-results121.png\",\n      alt: \"\",\n      width: \"796\",\n      height: \"549\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Obviously, this is a very simple analysis. But even such simple tasks are never boring with good visualization tools such as Distributions and Mosaic Display. You could also use Venn Diagram to find common features of selected subsets or perhaps Sieve Diagram for probabilities.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We are very happy to get these data and we would like to thank everyone who completed the survey. If you wish to help us further, please fill out a \", _jsx(_components.a, {\n        href: \"https://orange-software.typeform.com/to/c73R43\",\n        children: \"longer survey\"\n      }), \" that won't actually take you more than 3 minutes of your time (we timed it!).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy Friday everyone!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"mining-our-own-data"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
diff --git a/blog/model-based-feature-scoring/index.html b/blog/model-based-feature-scoring/index.html
index c09b3aea4..d1f0242c0 100644
--- a/blog/model-based-feature-scoring/index.html
+++ b/blog/model-based-feature-scoring/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, classification, features, regression, scoring</strong></p><h1 class="sc-c67a4900-0 loxVcf">Model-Based Feature Scoring</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 19, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Feature scoring and ranking can help in understanding the data in supervised settings. Orange includes a number of standard feature scoring procedures one can access in the Rank widget. Moreover, a number of modeling techniques, like linear or logistic regression, can rank features explicitly through assignment of weights. Trained models like random forests have their own methods for feature scoring. Models inferred by these modeling techniques depend on their parameters, like type and level of regularization for logistic regression. Same holds for feature weight: any change of parameters of the modeling techniques would change the resulting feature scores.</p>
 <p>It would thus be great if we could observe these changes and compare feature ranking provided by various machine learning methods. For this purpose, the Rank widget recently got a new input channel called scorer. We can attach any learner that can provide feature scores to the input of Rank, and then observe the ranking in the Rank table.</p>
 <a href="/blog/2015-12-model-based-feature-scoring/__optimized-images__/model-scoring-lr.png" data-gallery="true"><img alt="" loading="lazy" width="284" height="244" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-12-model-based-feature-scoring/__optimized-images__/model-scoring-lr.png"/></a>
 <p>Say, for the famous voting data set (File widget, Browse documentation data sets), the last two feature score columns were obtained by random forest and logistic regression with L1 regularization (C=0.1). Try changing the regularization parameter and type to see changes in feature scores.</p>
 <a href="/blog/2015-12-model-based-feature-scoring/__optimized-images__/rank-voting-lr.png" data-gallery="true"><img alt="" loading="lazy" width="853" height="442" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-12-model-based-feature-scoring/__optimized-images__/rank-voting-lr.png"/></a>
 <p>Feature weights for logistic and linear regression correspond to the absolute value of coefficients of their linear models. To observe their untransformed values in the table, these widgets now also output a data table with feature weights. (At the time of the writing of this blog, this feature has been implemented for linear regression; other classifiers and regressors that can estimate feature weights will be updated soon).</p>
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+<a href="/blog/2015-12-model-based-feature-scoring/__optimized-images__/lr-coefficients.png" data-gallery="true"><img alt="" loading="lazy" width="711" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-12-model-based-feature-scoring/__optimized-images__/lr-coefficients.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2015-12-19 19:27:51+00:00","draft":false,"title":"Model-Based Feature Scoring","blog":["analysis","classification","features","regression","scoring"],"oldUrl":"/blog/2015/12/19/model-based-feature-scoring/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Feature scoring and ranking can help in understanding the data in supervised settings. 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 <p>Orange 3.4.0 introduced a new widget category, <strong>Model</strong>, which now contains all supervised learning algorithms in one place and replaces the separate Classify and Regression categories.</p>
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+<a href="/blog/2017-04-model-replaces-classify-and-regression/__optimized-images__/Screen-Shot-2017-04-07-at-15.45.21.png" data-gallery="true"><img alt="" loading="lazy" width="672" height="652" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-04-model-replaces-classify-and-regression/__optimized-images__/Screen-Shot-2017-04-07-at-15.45.21.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-04-07 14:27:44+00:00","draft":false,"title":"Model replaces Classify and Regression","blog":["classification","features","interface","orange3","prediction","predictive  analytics","regression","toolbox","update"],"oldUrl":"/blog/2017/04/07/model-replaces-classify-and-regression/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Did you recently wonder where did Classification Tree go? 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We wanted to simplify the interface for new users and make finding an appropriate learning algorithm easier. Moreover, now you can reuse some workflows on different data sets, say \", _jsx(_components.em, {\n        children: \"housing.tab\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"iris.tab\"\n      }), \"!\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Leading up to this change, many algorithms were refactored so that regression and classification versions of the same method were merged into a single widget (and class in the underlying python API). For example, Classification Tree and Regression Tree have become simply Tree, which is capable of modelling categorical or numeric target variables. And similarly for SVM, kNN, Random Forest, …\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Have you ever searched for a widget by typing its name and were confused by multiple options appearing in the search box? 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, gsoc, mlc, multilabel</strong></p><h1 class="sc-c67a4900-0 loxVcf">Multi-label classification (and Multi-target prediction) in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 09, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The last summer, student Wencan Luo participated in <a href="https://code.google.com/soc/">Google Summer of Code</a> to implement <a href="/blog/2011/09/02/gsoc-review-multi-label-classification-implementation/">Multi-label Classification in Orange</a>. He provided a framework, implemented a few algorithms and some prototype widgets. His work has been &quot;hidden&quot; in our repositories for too long; finally, we have merged part of his code into Orange (widgets are not there yet ...) and added a more general support for multi-target prediction.</p>
 <p>You can load multi-label tab-delimited data (e.g. <a href="http://orange.biolab.si/trac/intertrac/export%3Atrunk/orange/doc/datasets/emotions.tab">emotions.tab</a>) just like any other <a href="/doc/reference/Orange.data.formats/#tab-delimited-format">tab-delimited data</a>:</p>
 <pre><code>    &gt;&gt;&gt; zoo = Orange.data.Table(&#x27;zoo&#x27;)            # single-target
@@ -186,4 +186,4 @@
     &gt;&gt;&gt; Orange.evaluation.scoring.mlc_hamming_loss(test)
     [0.2228780213603148]
 </code></pre>
-<p>In one of the following blog posts, a multi-target regression method PLS that is in the process of implementation will be described.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-01-09 12:41:00+00:00","draft":false,"title":"Multi-label classification (and Multi-target prediction) in Orange","blog":["classification","gsoc","mlc","multilabel"],"oldUrl":"/blog/2012/01/09/multi-label-classification-and-multi-target-prediction-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"The last summer, student Wencan Luo participated in \", _jsx(_components.a, {\n        href: \"https://code.google.com/soc/\",\n        children: \"Google Summer of Code\"\n      }), \" to implement \", _jsx(_components.a, {\n        href: \"/blog/2011/09/02/gsoc-review-multi-label-classification-implementation/\",\n        children: \"Multi-label Classification in Orange\"\n      }), \". He provided a framework, implemented a few algorithms and some prototype widgets. His work has been \\\"hidden\\\" in our repositories for too long; finally, we have merged part of his code into Orange (widgets are not there yet ...) and added a more general support for multi-target prediction.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can load multi-label tab-delimited data (e.g. \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/trac/intertrac/export%3Atrunk/orange/doc/datasets/emotions.tab\",\n        children: \"emotions.tab\"\n      }), \") just like any other \", _jsx(_components.a, {\n        href: \"/doc/reference/Orange.data.formats/#tab-delimited-format\",\n        children: \"tab-delimited data\"\n      }), \":\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e zoo = Orange.data.Table('zoo')            # single-target\\n    \u003e\u003e\u003e emotions = Orange.data.Table('emotions')  # multi-label\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The difference is that now zoo's domain has a non-empty \", _jsx(_components.strong, {\n        children: \"class_var\"\n      }), \" field, while a list of \", _jsx(_components.strong, {\n        children: \"emotions\"\n      }), \"' labels can be obtained through it's domain's \", _jsx(_components.strong, {\n        children: \"class_vars\"\n      }), \":\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e zoo.domain.class_var\\n    EnumVariable 'type'\\n    \u003e\u003e\u003e emotions.domain.class_vars\\n    \u003cEnumVariable 'amazed-suprised',\\n     EnumVariable 'happy-pleased',\\n     EnumVariable 'relaxing-calm',\\n     EnumVariable 'quiet-still',\\n     EnumVariable 'sad-lonely',\\n     EnumVariable 'angry-aggresive'\u003e\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A simple example of a \", _jsx(_components.a, {\n        href: \"/doc/reference/Orange.multilabel/\",\n        children: \"multi-label classification\"\n      }), \" learner is a \\\"binary relevance\\\" learner. Let's try it out.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e learner = Orange.multilabel.BinaryRelevanceLearner()\\n    \u003e\u003e\u003e classifier = learner(emotions)\\n    \u003e\u003e\u003e classifier(emotions[0])\\n    [\u003corange.Value 'amazed-suprised'='0'\u003e,\\n     \u003corange.Value 'happy-pleased'='0'\u003e,\\n     \u003corange.Value 'relaxing-calm'='1'\u003e,\\n     \u003corange.Value 'quiet-still'='1'\u003e,\\n     \u003corange.Value 'sad-lonely'='1'\u003e,\\n     \u003corange.Value 'angry-aggresive'='0'\u003e]\\n    \u003e\u003e\u003e classifier(emotions[0], Orange.classification.Classifier.GetProbabilities)\\n    [\u003c1.000, 0.000\u003e, \u003c0.881, 0.119\u003e, \u003c0.000, 1.000\u003e,\\n     \u003c0.046, 0.954\u003e, \u003c0.000, 1.000\u003e, \u003c1.000, 0.000\u003e]\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Real values of label variables of \", _jsx(_components.strong, {\n        children: \"emotions[0]\"\n      }), \" instance can be obtained by calling \", _jsx(_components.strong, {\n        children: \"emotions[0].get_classes()\"\n      }), \", which is analogous to the \", _jsx(_components.strong, {\n        children: \"get_class\"\n      }), \" method in the single-target case.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For multi-label classification, we can also perform testing like usual, however, \", _jsx(_components.a, {\n        href: \"/doc/reference/Orange.evaluation.scoring/#scoring-for-multilabel-classification\",\n        children: \"specialised evaluation measures\"\n      }), \" have to be used:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e test = Orange.evaluation.testing.cross_validation([learner], emotions)\\n    \u003e\u003e\u003e Orange.evaluation.scoring.mlc_hamming_loss(test)\\n    [0.2228780213603148]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In one of the following blog posts, a multi-target regression method PLS that is in the process of implementation will be described.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"multi-label-classification-and-multi-target-prediction-in-orange"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>In one of the following blog posts, a multi-target regression method PLS that is in the process of implementation will be described.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-01-09 12:41:00+00:00","draft":false,"title":"Multi-label classification (and Multi-target prediction) in Orange","blog":["classification","gsoc","mlc","multilabel"],"oldUrl":"/blog/2012/01/09/multi-label-classification-and-multi-target-prediction-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"The last summer, student Wencan Luo participated in \", _jsx(_components.a, {\n        href: \"https://code.google.com/soc/\",\n        children: \"Google Summer of Code\"\n      }), \" to implement \", _jsx(_components.a, {\n        href: \"/blog/2011/09/02/gsoc-review-multi-label-classification-implementation/\",\n        children: \"Multi-label Classification in Orange\"\n      }), \". 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Let's try it out.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e learner = Orange.multilabel.BinaryRelevanceLearner()\\n    \u003e\u003e\u003e classifier = learner(emotions)\\n    \u003e\u003e\u003e classifier(emotions[0])\\n    [\u003corange.Value 'amazed-suprised'='0'\u003e,\\n     \u003corange.Value 'happy-pleased'='0'\u003e,\\n     \u003corange.Value 'relaxing-calm'='1'\u003e,\\n     \u003corange.Value 'quiet-still'='1'\u003e,\\n     \u003corange.Value 'sad-lonely'='1'\u003e,\\n     \u003corange.Value 'angry-aggresive'='0'\u003e]\\n    \u003e\u003e\u003e classifier(emotions[0], Orange.classification.Classifier.GetProbabilities)\\n    [\u003c1.000, 0.000\u003e, \u003c0.881, 0.119\u003e, \u003c0.000, 1.000\u003e,\\n     \u003c0.046, 0.954\u003e, \u003c0.000, 1.000\u003e, \u003c1.000, 0.000\u003e]\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Real values of label variables of \", _jsx(_components.strong, {\n        children: \"emotions[0]\"\n      }), \" instance can be obtained by calling \", _jsx(_components.strong, {\n        children: \"emotions[0].get_classes()\"\n      }), \", which is analogous to the \", _jsx(_components.strong, {\n        children: \"get_class\"\n      }), \" method in the single-target case.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For multi-label classification, we can also perform testing like usual, however, \", _jsx(_components.a, {\n        href: \"/doc/reference/Orange.evaluation.scoring/#scoring-for-multilabel-classification\",\n        children: \"specialised evaluation measures\"\n      }), \" have to be used:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003e\u003e\u003e test = Orange.evaluation.testing.cross_validation([learner], emotions)\\n    \u003e\u003e\u003e Orange.evaluation.scoring.mlc_hamming_loss(test)\\n    [0.2228780213603148]\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In one of the following blog posts, a multi-target regression method PLS that is in the process of implementation will be described.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"multi-label-classification-and-multi-target-prediction-in-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - My First Orange Widget</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - My First Orange Widget"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-02-23 10:11:16+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="programming"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - My First Orange Widget</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - My First Orange Widget"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-02-23 10:11:16+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="programming"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>examples, orange3, programming, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">My First Orange Widget</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 23, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Recently, I took on a daunting task - programming my first widget. I&#x27;m not a programmer or a computer science grad, but I&#x27;ve been looking at Orange code for almost two years now and I thought I could handle it.</p>
 <p>I set to create a simple <a href="https://www.nottingham.ac.uk/alzsh3/acvocab/concordances.htm">Concordance</a> widget that displays word contexts in a corpus (the widget will be available in the future release). The widget turned out to be a little more complicated than I originally anticipated, but it was a great exercise in programming.</p>
 <p>Today, I&#x27;ll explain how I got started with my widget development. We will create a very basic Word Finder widget, that just goes through the corpus (data) and tells you whether a word occurs in a corpus or not. This particular widget is meant to be a part of the <a href="https://github.com/biolab/orange3-text">Orange3-Text add-on</a> (so you need the add-on installed to try it), but the basic structure is the same for all widgets.</p>
@@ -207,4 +207,4 @@
 <p>This is it. This is our widget. Good job. Creating a new widget can indeed be lot of fun. You can go from a quite basic widget to very intricate, depending on your sense of adventure.</p>
 <p>Finally, you can get the <a href="https://gist.github.com/ajdapretnar/e66e1dbecef3bb59abd4137bf8c2ab77">entire widget code in gist</a>.</p>
 <a href="/blog/2017-02-my-first-orange-widget/__optimized-images__/Screen-Shot-2017-02-23-at-10.41.27.png" data-gallery="true"><img alt="" loading="lazy" width="1354" height="1032" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-02-my-first-orange-widget/__optimized-images__/Screen-Shot-2017-02-23-at-10.41.27.png"/></a>
-<p>Happy programming, everyone! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-02-23 10:11:16+00:00","draft":false,"title":"My First Orange Widget","blog":["examples","orange3","programming","widget"],"oldUrl":"/blog/2017/02/23/my-first-orange-widget/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently, I took on a daunting task - programming my first widget. I'm not a programmer or a computer science grad, but I've been looking at Orange code for almost two years now and I thought I could handle it.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I set to create a simple \", _jsx(_components.a, {\n        href: \"https://www.nottingham.ac.uk/alzsh3/acvocab/concordances.htm\",\n        children: \"Concordance\"\n      }), \" widget that displays word contexts in a corpus (the widget will be available in the future release). The widget turned out to be a little more complicated than I originally anticipated, but it was a great exercise in programming.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Today, I'll explain how I got started with my widget development. We will create a very basic Word Finder widget, that just goes through the corpus (data) and tells you whether a word occurs in a corpus or not. This particular widget is meant to be a part of the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-text\",\n        children: \"Orange3-Text add-on\"\n      }), \" (so you need the add-on installed to try it), but the basic structure is the same for all widgets.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, I have to set the basic widget class.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"class\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title class_\",\n          children: \"OWWordFinder\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-title class_ inherited__\",\n          children: \"OWWidget\"\n        }), \"):\\n    name = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Word Finder\\\"\"\n        }), \"\\n    description = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Display whether a word is in a text or not.\\\"\"\n        }), \"\\n    icon = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"icons/WordFinder.svg\\\"\"\n        }), \"\\n    priority = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"\\n\\n    inputs = [(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Corpus'\"\n        }), \", Table, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'set_data'\"\n        }), \")]\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# This widget will have no output, but in case you want one, you define it as below.\"\n        }), \"\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# outputs = [('Output Name', output_type, 'output_method')]\"\n        }), \"\\n\\n    want_control_area = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"False\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This sets up the description of the widget, icon, inputs and so on. \", _jsx(_components.code, {\n        children: \"want_control_area\"\n      }), \" is where we say we only want the main window. Both are on by default in Orange and this simply hides the empty control area on the widget's left side. If your widget has any parameters and controls, leave the control area on and place the buttons there.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-my-first-orange-widget/__optimized-images__/example-area.jpg\",\n      alt: \"\",\n      width: \"1234\",\n      height: \"978\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.code, {\n        children: \"__init__\"\n      }), \" we define widget properties (such as data and queried word) and set the view. I decided to go with a very simple design - I just put everything in the \", _jsx(_components.code, {\n        children: \"mainArea\"\n      }), \". For such a basic widget this might be ok, but otherwise you might want to dig deeper into models and use \", _jsx(_components.code, {\n        children: \"QTableView\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QGraphicsScene\"\n      }), \" or something similar. Here we will build just the bare bones of a functioning widget.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"__init__\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-params\",\n          children: \"self\"\n        }), \"):\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"super\"\n        }), \"().__init__()\\n\\n    self.corpus = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"None\"\n        }), \"    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# input data\"\n        }), \"\\n    self.word = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"\\\"\"\n        }), \"        \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# queried word\"\n        }), \"\\n\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# setting the gui\"\n        }), \"\\n    gui.widgetBox(self.mainArea, orientation=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"vertical\\\"\"\n        }), \")\\n    self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \" = gui.lineEdit(self.mainArea, self, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"''\"\n        }), \",\\n                                orientation=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"horizontal\\\"\"\n        }), \",\\n                                label=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Query:'\"\n        }), \")\\n    self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \".setFocus()\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# run method self.search on every text change\"\n        }), \"\\n    self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \".textChanged.connect(self.search)\\n\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# place a text label in the mainArea\"\n        }), \"\\n    self.view = QLabel()\\n    self.mainArea.layout().addWidget(self.view)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Ok, this now sets the \", _jsx(_components.code, {\n        children: \"__init__\"\n      }), \": what the widget remembers and how it looks like. With our buttons in place, the widget needs some methods, too.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first method will update the self.corpus attribute, when the widget receives an input.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"set_data\"\n        }), \"(\", _jsxs(_components.span, {\n          className: \"hljs-params\",\n          children: [\"self, data=\", _jsx(_components.span, {\n            className: \"hljs-literal\",\n            children: \"None\"\n          })]\n        }), \"):\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"is\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"not\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"None\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"and\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"not\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"isinstance\"\n        }), \"(data, Corpus):\\n        self.corpus = Corpus.from_table(data.domain, data)\\n    self.corpus = data\\n    self.search()\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At the end we called \", _jsx(_components.code, {\n        children: \"self.search()\"\n      }), \" method, which we already met in \", _jsx(_components.code, {\n        children: \"__init__\"\n      }), \" above. This method is key to our widget, as it will run the search every time the word changes. Moreover, it will run the method on the same query word when the widget is provided with a new data set, which is why we set it also in \", _jsx(_components.code, {\n        children: \"set_data()\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, let's finally write this method.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"search\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-params\",\n          children: \"self\"\n        }), \"):\\n    self.word = self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \".text()\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# self.corpus.tokens will run a default tokenizer, if no tokens are provided on the input\"\n        }), \"\\n    result = \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"any\"\n        }), \"(self.word \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" doc \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" doc \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" self.corpus.tokens)\\n    self.view.setText(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"str\"\n        }), \"(result))\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is it. This is our widget. Good job. Creating a new widget can indeed be lot of fun. You can go from a quite basic widget to very intricate, depending on your sense of adventure.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, you can get the \", _jsx(_components.a, {\n        href: \"https://gist.github.com/ajdapretnar/e66e1dbecef3bb59abd4137bf8c2ab77\",\n        children: \"entire widget code in gist\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-my-first-orange-widget/__optimized-images__/Screen-Shot-2017-02-23-at-10.41.27.png\",\n      alt: \"\",\n      width: \"1354\",\n      height: \"1032\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy programming, everyone! :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"my-first-orange-widget"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Happy programming, everyone! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-02-23 10:11:16+00:00","draft":false,"title":"My First Orange Widget","blog":["examples","orange3","programming","widget"],"oldUrl":"/blog/2017/02/23/my-first-orange-widget/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Recently, I took on a daunting task - programming my first widget. I'm not a programmer or a computer science grad, but I've been looking at Orange code for almost two years now and I thought I could handle it.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I set to create a simple \", _jsx(_components.a, {\n        href: \"https://www.nottingham.ac.uk/alzsh3/acvocab/concordances.htm\",\n        children: \"Concordance\"\n      }), \" widget that displays word contexts in a corpus (the widget will be available in the future release). The widget turned out to be a little more complicated than I originally anticipated, but it was a great exercise in programming.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Today, I'll explain how I got started with my widget development. We will create a very basic Word Finder widget, that just goes through the corpus (data) and tells you whether a word occurs in a corpus or not. This particular widget is meant to be a part of the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-text\",\n        children: \"Orange3-Text add-on\"\n      }), \" (so you need the add-on installed to try it), but the basic structure is the same for all widgets.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, I have to set the basic widget class.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"class\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title class_\",\n          children: \"OWWordFinder\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-title class_ inherited__\",\n          children: \"OWWidget\"\n        }), \"):\\n    name = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Word Finder\\\"\"\n        }), \"\\n    description = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Display whether a word is in a text or not.\\\"\"\n        }), \"\\n    icon = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"icons/WordFinder.svg\\\"\"\n        }), \"\\n    priority = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"\\n\\n    inputs = [(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Corpus'\"\n        }), \", Table, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'set_data'\"\n        }), \")]\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# This widget will have no output, but in case you want one, you define it as below.\"\n        }), \"\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# outputs = [('Output Name', output_type, 'output_method')]\"\n        }), \"\\n\\n    want_control_area = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"False\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This sets up the description of the widget, icon, inputs and so on. \", _jsx(_components.code, {\n        children: \"want_control_area\"\n      }), \" is where we say we only want the main window. Both are on by default in Orange and this simply hides the empty control area on the widget's left side. If your widget has any parameters and controls, leave the control area on and place the buttons there.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-my-first-orange-widget/__optimized-images__/example-area.jpg\",\n      alt: \"\",\n      width: \"1234\",\n      height: \"978\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.code, {\n        children: \"__init__\"\n      }), \" we define widget properties (such as data and queried word) and set the view. I decided to go with a very simple design - I just put everything in the \", _jsx(_components.code, {\n        children: \"mainArea\"\n      }), \". For such a basic widget this might be ok, but otherwise you might want to dig deeper into models and use \", _jsx(_components.code, {\n        children: \"QTableView\"\n      }), \", \", _jsx(_components.code, {\n        children: \"QGraphicsScene\"\n      }), \" or something similar. Here we will build just the bare bones of a functioning widget.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"__init__\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-params\",\n          children: \"self\"\n        }), \"):\\n    \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"super\"\n        }), \"().__init__()\\n\\n    self.corpus = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"None\"\n        }), \"    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# input data\"\n        }), \"\\n    self.word = \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"\\\"\"\n        }), \"        \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# queried word\"\n        }), \"\\n\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# setting the gui\"\n        }), \"\\n    gui.widgetBox(self.mainArea, orientation=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"vertical\\\"\"\n        }), \")\\n    self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \" = gui.lineEdit(self.mainArea, self, \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"''\"\n        }), \",\\n                                orientation=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"horizontal\\\"\"\n        }), \",\\n                                label=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'Query:'\"\n        }), \")\\n    self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \".setFocus()\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# run method self.search on every text change\"\n        }), \"\\n    self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \".textChanged.connect(self.search)\\n\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# place a text label in the mainArea\"\n        }), \"\\n    self.view = QLabel()\\n    self.mainArea.layout().addWidget(self.view)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Ok, this now sets the \", _jsx(_components.code, {\n        children: \"__init__\"\n      }), \": what the widget remembers and how it looks like. With our buttons in place, the widget needs some methods, too.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first method will update the self.corpus attribute, when the widget receives an input.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"set_data\"\n        }), \"(\", _jsxs(_components.span, {\n          className: \"hljs-params\",\n          children: [\"self, data=\", _jsx(_components.span, {\n            className: \"hljs-literal\",\n            children: \"None\"\n          })]\n        }), \"):\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" data \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"is\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"not\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"None\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"and\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"not\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"isinstance\"\n        }), \"(data, Corpus):\\n        self.corpus = Corpus.from_table(data.domain, data)\\n    self.corpus = data\\n    self.search()\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At the end we called \", _jsx(_components.code, {\n        children: \"self.search()\"\n      }), \" method, which we already met in \", _jsx(_components.code, {\n        children: \"__init__\"\n      }), \" above. This method is key to our widget, as it will run the search every time the word changes. Moreover, it will run the method on the same query word when the widget is provided with a new data set, which is why we set it also in \", _jsx(_components.code, {\n        children: \"set_data()\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, let's finally write this method.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"def\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-title function_\",\n          children: \"search\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-params\",\n          children: \"self\"\n        }), \"):\\n    self.word = self.\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"input\"\n        }), \".text()\\n    \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# self.corpus.tokens will run a default tokenizer, if no tokens are provided on the input\"\n        }), \"\\n    result = \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"any\"\n        }), \"(self.word \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" doc \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" doc \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" self.corpus.tokens)\\n    self.view.setText(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"str\"\n        }), \"(result))\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is it. This is our widget. Good job. Creating a new widget can indeed be lot of fun. You can go from a quite basic widget to very intricate, depending on your sense of adventure.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, you can get the \", _jsx(_components.a, {\n        href: \"https://gist.github.com/ajdapretnar/e66e1dbecef3bb59abd4137bf8c2ab77\",\n        children: \"entire widget code in gist\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-02-my-first-orange-widget/__optimized-images__/Screen-Shot-2017-02-23-at-10.41.27.png\",\n      alt: \"\",\n      width: \"1354\",\n      height: \"1032\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Happy programming, everyone! :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"my-first-orange-widget"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, network</strong></p><h1 class="sc-c67a4900-0 loxVcf">Network Add-on Published in JSS</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 03, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>NetExplorer, a widget for network exploration, was in orange for over 5 years. Several network analysis widgets were added to Orange since, and we decided to move the entire network functionality to an <a href="https://pypi.python.org/pypi/Orange-Network">Orange Network add-on</a>.</p>
 <p>We recently published a paper <a href="http://www.jstatsoft.org/v53/i06">Interactive Network Exploration with Orange</a> in the Journal of Statistical Software. We invite you to read the tutorial on network exploration. It is aimed for beginners in this topic, and includes detailed explanation with images.</p>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Network Analysis with Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Network Analysis with Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-07-18 13:42:34+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="network"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -167,4 +167,4 @@
 <p>We&#x27;ve colored the nodes by type and set the size of the nodes to the number of authors per conference/paper. Finally, we&#x27;ve set the node label to &#x27;name&#x27;. Seems like International Conference on AI and Law and AI and Law journal are connected through the number of shared authors. Same goes for AI in Medicine in Europe conference and AI and Medicine journal. Connections indeed make sense.</p>
 <a href="/blog/2016-07-network-analysis-with-orange/__optimized-images__/conference1-1.png" data-gallery="true"><img alt="" loading="lazy" width="1282" height="1001" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-07-network-analysis-with-orange/__optimized-images__/conference1-1.png"/></a>
 <p>The entire workflow.</p>
-<p>There are many other things you can do with the Networks add-on in Orange. You can color nodes by predictions, highlight misclassifications or output only nodes with certain network parameters. But for today, let this be it.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-07-18 13:42:34+00:00","draft":false,"title":"Network Analysis with Orange","blog":["addons","analysis","examples","network","orange3","visualization"],"oldUrl":"/blog/2016/07/18/network-analysis-with-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Visualizing relations between data instances can tell us a lot about our data. 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+<p>There are many other things you can do with the Networks add-on in Orange. You can color nodes by predictions, highlight misclassifications or output only nodes with certain network parameters. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, network, networkx, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">NetworkX in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 29, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://networkx.lanl.gov/">NetworkX</a> – a popular open-source python library for network analysis has finally found its way into Orange. It is now used as a base class for network representation in all Orange modules and widgets. By that, we offered to the widespread network community a fruitful and fun way to visualize and explore networks, using their existing NetworkX scripts. It has never been easier to combine network analysis and visualization with existing machine learning and data discovery methods.</p>
 <p>Complete documentation is available in the <a href="/doc/orange25/Orange.network.html">Orange network headquarters</a>. For a brief overview, take a look at the following example. Let us suppose we would like to analyse the data about patients, having one of two types of leukemia. So, we have a <a href="http://blog.biolab.si/wp-content/uploads/2011/07/29/leukemiagsea.tab">data set</a> with 72 patient, 4600+ gene expressions and a class variable. We also have a vast <a href="/blog/2011-07-networkx-in-orange/genes_biofuncttar.gz">network of human genes</a>, connected if they share a biological function. What we would like to examine is a sub-network with only several hundred most expressed genes from the data set. To show off a bit, we will also use the <a href="/addons/">Orange Bioinformatics add-on</a>. Here is how we do it:</p>
 <pre><code class="hljs language-python"><span class="hljs-keyword">import</span> Orange
@@ -200,4 +200,4 @@
 <span class="hljs-comment"># set the network</span>
 ow.set_graph(G_sub)
 app.exec_()
-</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-07-29 18:56:00+00:00","draft":false,"title":"NetworkX in Orange","blog":["analysis","network","networkx","visualization"],"oldUrl":"/blog/2011/07/29/networkx-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://networkx.lanl.gov/\",\n        children: \"NetworkX\"\n      }), \" – a popular open-source python library for network analysis has finally found its way into Orange. It is now used as a base class for network representation in all Orange modules and widgets. By that, we offered to the widespread network community a fruitful and fun way to visualize and explore networks, using their existing NetworkX scripts. It has never been easier to combine network analysis and visualization with existing machine learning and data discovery methods.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Complete documentation is available in the \", _jsx(_components.a, {\n        href: \"/doc/orange25/Orange.network.html\",\n        children: \"Orange network headquarters\"\n      }), \". For a brief overview, take a look at the following example. Let us suppose we would like to analyse the data about patients, having one of two types of leukemia. So, we have a \", _jsx(_components.a, {\n        href: \"http://blog.biolab.si/wp-content/uploads/2011/07/29/leukemiagsea.tab\",\n        children: \"data set\"\n      }), \" with 72 patient, 4600+ gene expressions and a class variable. We also have a vast \", _jsx(_components.a, {\n        href: \"/blog/2011-07-networkx-in-orange/genes_biofuncttar.gz\",\n        children: \"network of human genes\"\n      }), \", connected if they share a biological function. What we would like to examine is a sub-network with only several hundred most expressed genes from the data set. To show off a bit, we will also use the \", _jsx(_components.a, {\n        href: \"/addons/\",\n        children: \"Orange Bioinformatics add-on\"\n      }), \". Here is how we do it:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" obiExpression\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# load leukemia data set\"\n        }), \"\\ntable = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/media/Ox/Projects_Archive/res/BIO/leukemia/leukemiaGSEA.tab\\\"\"\n        }), \")\\n\\nuseAttributeLabels = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"False\"\n        }), \"\\nttest = obiExpression.ExpressionSignificance_TTest(table, useAttributeLabels)\\n\\ntarget = [table.domain.classVar(\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"), table.domain.classVar(\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \")]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# test for significantly expressed genes\"\n        }), \"\\nscore = ttest(target = target)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# each gene is scored (t-test, p-value)\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" score[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"(FloatVariable \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'HIST1H4C'\"\n        }), \", (\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1.8377179790830149\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0.07034778767062116\"\n        }), \"))\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# sort by p-value\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" operator \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" itemgetter\\nscore.sort(key=\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"lambda\"\n        }), \" s: s[\", _jsx(_components.span, {\n          className: 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\"hljs-string\",\n          children: \"'genes_biofunct.gpickle'\"\n        }), \")\\n\\nitems = G.items().filter_bool({\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'gene'\"\n        }), \": important_genes})\\nindices = [i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i, present \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"enumerate\"\n        }), \"(items) \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" present]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# build a subraph of 200 most expressed genes\"\n        }), \"\\nG_sub = G.subgraph(indices)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In addition to the power of scripting environment, we also get the benefits of visual data exploration with Orange widgets. However, network widgets are currently under heavy development, so expect some bugs if you dare to try them. Coding should be finished in a month or two, check the blog for progress updates. Here is how to open the network in Nx Explorer widget:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" sys\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" PyQt4\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# must have OWNxExplorer in python path!\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" OWNxExplorer\\n\\napp=PyQt4.QtGui.QApplication(sys.argv)\\now=OWNxExplorer.OWNxExplorer()\\now.show()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# set the network\"\n        }), \"\\now.set_graph(G_sub)\\napp.exec_()\\n\"]\n      })\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"networkx-in-orange"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-07-29 18:56:00+00:00","draft":false,"title":"NetworkX in Orange","blog":["analysis","network","networkx","visualization"],"oldUrl":"/blog/2011/07/29/networkx-in-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://networkx.lanl.gov/\",\n        children: \"NetworkX\"\n      }), \" – a popular open-source python library for network analysis has finally found its way into Orange. It is now used as a base class for network representation in all Orange modules and widgets. By that, we offered to the widespread network community a fruitful and fun way to visualize and explore networks, using their existing NetworkX scripts. It has never been easier to combine network analysis and visualization with existing machine learning and data discovery methods.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Complete documentation is available in the \", _jsx(_components.a, {\n        href: \"/doc/orange25/Orange.network.html\",\n        children: \"Orange network headquarters\"\n      }), \". For a brief overview, take a look at the following example. Let us suppose we would like to analyse the data about patients, having one of two types of leukemia. So, we have a \", _jsx(_components.a, {\n        href: \"http://blog.biolab.si/wp-content/uploads/2011/07/29/leukemiagsea.tab\",\n        children: \"data set\"\n      }), \" with 72 patient, 4600+ gene expressions and a class variable. We also have a vast \", _jsx(_components.a, {\n        href: \"/blog/2011-07-networkx-in-orange/genes_biofuncttar.gz\",\n        children: \"network of human genes\"\n      }), \", connected if they share a biological function. What we would like to examine is a sub-network with only several hundred most expressed genes from the data set. To show off a bit, we will also use the \", _jsx(_components.a, {\n        href: \"/addons/\",\n        children: \"Orange Bioinformatics add-on\"\n      }), \". Here is how we do it:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" obiExpression\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# load leukemia data set\"\n        }), \"\\ntable = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"/media/Ox/Projects_Archive/res/BIO/leukemia/leukemiaGSEA.tab\\\"\"\n        }), \")\\n\\nuseAttributeLabels = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"False\"\n        }), \"\\nttest = obiExpression.ExpressionSignificance_TTest(table, useAttributeLabels)\\n\\ntarget = [table.domain.classVar(\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"), table.domain.classVar(\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \")]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# test for significantly expressed genes\"\n        }), \"\\nscore = ttest(target = target)\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# each gene is scored (t-test, p-value)\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \" score[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"(FloatVariable \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'HIST1H4C'\"\n        }), \", (\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1.8377179790830149\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0.07034778767062116\"\n        }), \"))\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# sort by p-value\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" operator \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" itemgetter\\nscore.sort(key=\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"lambda\"\n        }), \" s: s[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"][\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# select 200 genes with the lowest p-value\"\n        }), \"\\nimportant_genes = [gene_var.name \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" gene_var, s \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" score[:\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"200\"\n        }), \"]]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# read the gene network (5000+ genes, dense network)\"\n        }), \"\\nG = Orange.network.readwrite.read(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'genes_biofunct.gpickle'\"\n        }), \")\\n\\nitems = G.items().filter_bool({\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'gene'\"\n        }), \": important_genes})\\nindices = [i \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" i, present \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"enumerate\"\n        }), \"(items) \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"if\"\n        }), \" present]\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# build a subraph of 200 most expressed genes\"\n        }), \"\\nG_sub = G.subgraph(indices)\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In addition to the power of scripting environment, we also get the benefits of visual data exploration with Orange widgets. However, network widgets are currently under heavy development, so expect some bugs if you dare to try them. Coding should be finished in a month or two, check the blog for progress updates. Here is how to open the network in Nx Explorer widget:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" sys\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" PyQt4\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# must have OWNxExplorer in python path!\"\n        }), \"\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" OWNxExplorer\\n\\napp=PyQt4.QtGui.QApplication(sys.argv)\\now=OWNxExplorer.OWNxExplorer()\\now.show()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# set the network\"\n        }), \"\\now.set_graph(G_sub)\\napp.exec_()\\n\"]\n      })\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"networkx-in-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Neural Network is Back!</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Neural Network is Back!"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-11-03 12:40:06+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="neuralnetwork"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="regression"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, neuralnetwork, orange3, regression, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Neural Network is Back!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 03, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We know you&#x27;ve missed it. We&#x27;ve been getting many requests to bring back Neural Network widget, but we also had many reservations about it.</p>
 <p>Neural networks are powerful and great, but to do them right is not straight-forward. And to do them right in the context of a GUI-based visual programming tool like Orange is a twisted double helix of a roller coaster.</p>
 <p>Do we make each layer a widget and then stack them? Do we use parallel processing or try to do something server-side? Theano or Keras? Tensorflow perhaps?</p>
@@ -157,4 +157,4 @@
 <p>Then one day a silly novice programmer (a.k.a. me) had enough and just threw scikit-learn&#x27;s <a href="http://scikit-learn.org/stable/modules/neural_networks_supervised.html">Multi-layer Perceptron model</a> into a widget and called it a day. There you go. A Neural Network widget just like it was in Orange2 - a wrapper for a scikit&#x27;s function that works out-of-the-box. Nothing fancy, nothing powerful, but it does its job. It models things and it predicts things.</p>
 <p>Just like that:</p>
 <a href="/blog/2017-11-neural-network-is-back/__optimized-images__/Screen-Shot-2017-11-03-at-13.32.28.png" data-gallery="true"><img alt="" loading="lazy" width="562" height="280" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-11-neural-network-is-back/__optimized-images__/Screen-Shot-2017-11-03-at-13.32.28.png"/></a>
-<p>Have fun with the new widget!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-11-03 12:40:06+00:00","draft":false,"title":"Neural Network is Back!","blog":["classification","neuralnetwork","orange3","regression","widget"],"oldUrl":"/blog/2017/11/03/neural-network-is-back/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We know you've missed it. We've been getting many requests to bring back Neural Network widget, but we also had many reservations about it.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Neural networks are powerful and great, but to do them right is not straight-forward. And to do them right in the context of a GUI-based visual programming tool like Orange is a twisted double helix of a roller coaster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Do we make each layer a widget and then stack them? Do we use parallel processing or try to do something server-side? Theano or Keras? 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+<p>Have fun with the new widget!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-11-03 12:40:06+00:00","draft":false,"title":"Neural Network is Back!","blog":["classification","neuralnetwork","orange3","regression","widget"],"oldUrl":"/blog/2017/11/03/neural-network-is-back/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We know you've missed it. We've been getting many requests to bring back Neural Network widget, but we also had many reservations about it.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Neural networks are powerful and great, but to do them right is not straight-forward. And to do them right in the context of a GUI-based visual programming tool like Orange is a twisted double helix of a roller coaster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Do we make each layer a widget and then stack them? Do we use parallel processing or try to do something server-side? Theano or Keras? 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>canvas, features</strong></p><h1 class="sc-c67a4900-0 loxVcf">New canvas</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 14, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange Canvas, a visual programming environment for Orange, has been around for a while. Integrating new and new features degraded the quality of code to a point where further development proved to be a daunting task. With ever increasing number of widgets, the existing widget toolbar is becoming harder and harder to use, but improving it is really hard. For that reason, we decided Orange needs a new Canvas, a rewrite, that would keep all of the feature of the existing one, but introduce the needed structure and modularity to the source code.</p>
 <p>The project started about a year ago, and more than 20 thousand lines of code later, we have something to show you. As of yesterday, the new canvas was merged to the main Orange repository, where it lives alongside the old one. At the moment, it still lacks a lot of testing, some features are not completely implemented, but the main functionality, i.e. visual programming with widgets and links, should work.</p>
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@@ -165,4 +165,4 @@
 </code></pre>
 <p>with the python that has Orange installed.</p>
 <p>What to expect?</p>
-<p>Nothing will explode, but short of that, anything might happen. If you stumble upon issues or have helpful suggestions, please post them on our <a href="http://orange.biolab.si/trac/newticket?component=new-canvas">issue tracker</a>. There are some known <a href="http://orange.biolab.si/trac/query?status=accepted&amp;status=assigned&amp;status=new&amp;status=reopened&amp;component=new-canvas&amp;col=id&amp;col=summary&amp;col=component&amp;col=type&amp;col=status&amp;col=milestone&amp;order=priority">problems we are aware of</a>; you do not need to report those :).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-02-14 14:37:00+00:00","draft":false,"title":"New canvas","blog":["canvas","features"],"oldUrl":"/blog/2013/02/14/new-canvas/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange Canvas, a visual programming environment for Orange, has been around for a while. 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At the moment, it still lacks a lot of testing, some features are not completely implemented, but the main functionality, i.e. visual programming with widgets and links, should work.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2013-02-new-canvas/__optimized-images__/screen_shot_2013-02-14_at_144527.png__616x616_q95_upscale.png\",\n      alt: \"\",\n      width: \"616\",\n      height: \"425\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you are feeling adventurous, you can try it out yourself. 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+<p>Nothing will explode, but short of that, anything might happen. If you stumble upon issues or have helpful suggestions, please post them on our <a href="http://orange.biolab.si/trac/newticket?component=new-canvas">issue tracker</a>. There are some known <a href="http://orange.biolab.si/trac/query?status=accepted&amp;status=assigned&amp;status=new&amp;status=reopened&amp;component=new-canvas&amp;col=id&amp;col=summary&amp;col=component&amp;col=type&amp;col=status&amp;col=milestone&amp;order=priority">problems we are aware of</a>; you do not need to report those :).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-02-14 14:37:00+00:00","draft":false,"title":"New canvas","blog":["canvas","features"],"oldUrl":"/blog/2013/02/14/new-canvas/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange Canvas, a visual programming environment for Orange, has been around for a while. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - New in Orange: Partial least squares regression</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - New in Orange: Partial least squares regression"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2012-02-02 16:21:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="multitarget"/><meta property="article:tag" content="pls"/><meta property="article:tag" content="regression"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>multitarget, pls, regression</strong></p><h1 class="sc-c67a4900-0 loxVcf">New in Orange: Partial least squares regression</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 02, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://en.wikipedia.org/wiki/Partial_least_squares_regression">Partial least squares regression</a> is a regression technique which supports multiple response variables. PLS regression is very popular in areas such as bioinformatics, chemometrics etc. where the number of observations is usually less than the number of measured variables and where there exists multicollinearity among the predictor variables. In such situations, standard regression techniques would usually fail. The PLS regression is now available in Orange (see <a href="/doc/reference/Orange.regression.pls">documentation</a>)!</p>
 <p>You can use PLS regression model on single-target or <a href="/blog/2012/01/09/multi-label-classification-and-multi-target-prediction-in-orange/">multi-target</a> data sets. Simply load the data set <a href="/doc/reference/_downloads/multitarget-synthetic.tab">multitarget-synthetic.tab</a> and see that it contains three predictor variables and four responses using this code.</p>
 <pre><code>    data = Orange.data.Table(&quot;multitarget-synthetic.tab&quot;)
@@ -188,4 +188,4 @@
      Linear (single-target) 0.5769 0.3128 0.2703 0.2529 0.2493 0.2446 0.2436 0.2442
      PLS (multi-target) 0.3663 0.2955 0.2623 0.2517 0.2487 0.2447 0.2441 0.2448
 </code></pre>
-<p>We can see that PLS regression outperforms linear regression when the number of training instances is low. Such situations (low number of instances compared to high number of variables) are quite common when analyzing data sets in bioinformatics. However, with increasing number of training instances, the advantages of PLS regression diminish.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-02-02 16:21:00+00:00","draft":false,"title":"New in Orange: Partial least squares regression","blog":["multitarget","pls","regression"],"oldUrl":"/blog/2012/02/02/new-in-orange-partial-least-squares-regression/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Partial_least_squares_regression\",\n        children: \"Partial least squares regression\"\n      }), \" is a regression technique which supports multiple response variables. PLS regression is very popular in areas such as bioinformatics, chemometrics etc. where the number of observations is usually less than the number of measured variables and where there exists multicollinearity among the predictor variables. In such situations, standard regression techniques would usually fail. The PLS regression is now available in Orange (see \", _jsx(_components.a, {\n        href: \"/doc/reference/Orange.regression.pls\",\n        children: \"documentation\"\n      }), \")!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can use PLS regression model on single-target or \", _jsx(_components.a, {\n        href: \"/blog/2012/01/09/multi-label-classification-and-multi-target-prediction-in-orange/\",\n        children: \"multi-target\"\n      }), \" data sets. Simply load the data set \", _jsx(_components.a, {\n        href: \"/doc/reference/_downloads/multitarget-synthetic.tab\",\n        children: \"multitarget-synthetic.tab\"\n      }), \" and see that it contains three predictor variables and four responses using this code.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    data = Orange.data.Table(\\\"multitarget-synthetic.tab\\\")\\n    print \\\"Input variables:\\\"\\n    print data.domain.features\\n    print \\\"Response variables:\\\"\\n    print data.domain.class_vars\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Input variables:\\n    \u003cFloatVariable 'X1', FloatVariable 'X2', FloatVariable 'X3'\u003e\\n    Response variables:\\n    \u003cFloatVariable 'Y1', FloatVariable 'Y2', FloatVariable 'Y3', FloatVariable 'Y4'\u003e\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As you can see, all variables in this data set are continuous. The PLS regression is intended forsuch situations although it can be used for discrete input variables as well (using 0-1 continuation). Currently, discrete response variables are not yet supported.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's try to fit the PLS regression model on our data set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"learner = Orange.multitarget.pls.PLSRegressionLearner()\\nclassifier = learner(data)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The classifier can be now used to predict values of the four responses based onthree predictors. Let's see how it manages this task on the first data instance.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    actual = data[0].get_classes()\\n    predicted = classifier(data[0]) \\n\\n    print \\\"Actual\\\", \\\"Predicted\\\"\\n    for a, p in zip(actual, predicted):\\n        print \\\"%6.3f %6.3f\\\" % (a,p)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Actual Predicted\\n     0.490  0.613\\n     1.237  0.826\\n     1.808  1.084\\n     0.422  0.534\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To test the usefulness of PLS as a multi-target method let's compare it to a single-target method - linear regression. We did this by comparing \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Mean_squared_error\",\n        children: \"Root mean squared error\"\n      }), \" (RMSE) of predicted values for a single response variable. We constructed synthetic data sets and performed the RMSE analysis using \", _jsx(_components.a, {\n        href: \"/blog/2012-02-new-in-orange-partial-least-squares-regression/pls_vs_linear.py\",\n        children: \"this script\"\n      }), \". The results can be seen in the following output:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"     Training set sizes      5     10     20     50    100    200    500   1000\\n     Linear (single-target) 0.5769 0.3128 0.2703 0.2529 0.2493 0.2446 0.2436 0.2442\\n     PLS (multi-target) 0.3663 0.2955 0.2623 0.2517 0.2487 0.2447 0.2441 0.2448\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that PLS regression outperforms linear regression when the number of training instances is low. Such situations (low number of instances compared to high number of variables) are quite common when analyzing data sets in bioinformatics. However, with increasing number of training instances, the advantages of PLS regression diminish.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"new-in-orange-partial-least-squares-regression"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>We can see that PLS regression outperforms linear regression when the number of training instances is low. Such situations (low number of instances compared to high number of variables) are quite common when analyzing data sets in bioinformatics. However, with increasing number of training instances, the advantages of PLS regression diminish.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-02-02 16:21:00+00:00","draft":false,"title":"New in Orange: Partial least squares regression","blog":["multitarget","pls","regression"],"oldUrl":"/blog/2012/02/02/new-in-orange-partial-least-squares-regression/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Partial_least_squares_regression\",\n        children: \"Partial least squares regression\"\n      }), \" is a regression technique which supports multiple response variables. PLS regression is very popular in areas such as bioinformatics, chemometrics etc. where the number of observations is usually less than the number of measured variables and where there exists multicollinearity among the predictor variables. In such situations, standard regression techniques would usually fail. The PLS regression is now available in Orange (see \", _jsx(_components.a, {\n        href: \"/doc/reference/Orange.regression.pls\",\n        children: \"documentation\"\n      }), \")!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can use PLS regression model on single-target or \", _jsx(_components.a, {\n        href: \"/blog/2012/01/09/multi-label-classification-and-multi-target-prediction-in-orange/\",\n        children: \"multi-target\"\n      }), \" data sets. Simply load the data set \", _jsx(_components.a, {\n        href: \"/doc/reference/_downloads/multitarget-synthetic.tab\",\n        children: \"multitarget-synthetic.tab\"\n      }), \" and see that it contains three predictor variables and four responses using this code.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    data = Orange.data.Table(\\\"multitarget-synthetic.tab\\\")\\n    print \\\"Input variables:\\\"\\n    print data.domain.features\\n    print \\\"Response variables:\\\"\\n    print data.domain.class_vars\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Input variables:\\n    \u003cFloatVariable 'X1', FloatVariable 'X2', FloatVariable 'X3'\u003e\\n    Response variables:\\n    \u003cFloatVariable 'Y1', FloatVariable 'Y2', FloatVariable 'Y3', FloatVariable 'Y4'\u003e\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As you can see, all variables in this data set are continuous. The PLS regression is intended forsuch situations although it can be used for discrete input variables as well (using 0-1 continuation). Currently, discrete response variables are not yet supported.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let's try to fit the PLS regression model on our data set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"learner = Orange.multitarget.pls.PLSRegressionLearner()\\nclassifier = learner(data)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The classifier can be now used to predict values of the four responses based onthree predictors. Let's see how it manages this task on the first data instance.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    actual = data[0].get_classes()\\n    predicted = classifier(data[0]) \\n\\n    print \\\"Actual\\\", \\\"Predicted\\\"\\n    for a, p in zip(actual, predicted):\\n        print \\\"%6.3f %6.3f\\\" % (a,p)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    Actual Predicted\\n     0.490  0.613\\n     1.237  0.826\\n     1.808  1.084\\n     0.422  0.534\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To test the usefulness of PLS as a multi-target method let's compare it to a single-target method - linear regression. We did this by comparing \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Mean_squared_error\",\n        children: \"Root mean squared error\"\n      }), \" (RMSE) of predicted values for a single response variable. We constructed synthetic data sets and performed the RMSE analysis using \", _jsx(_components.a, {\n        href: \"/blog/2012-02-new-in-orange-partial-least-squares-regression/pls_vs_linear.py\",\n        children: \"this script\"\n      }), \". The results can be seen in the following output:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"     Training set sizes      5     10     20     50    100    200    500   1000\\n     Linear (single-target) 0.5769 0.3128 0.2703 0.2529 0.2493 0.2446 0.2436 0.2442\\n     PLS (multi-target) 0.3663 0.2955 0.2623 0.2517 0.2487 0.2447 0.2441 0.2448\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that PLS regression outperforms linear regression when the number of training instances is low. Such situations (low number of instances compared to high number of variables) are quite common when analyzing data sets in bioinformatics. However, with increasing number of training instances, the advantages of PLS regression diminish.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"new-in-orange-partial-least-squares-regression"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/new-in-orange-support-for-conll-u-files/index.html b/blog/new-in-orange-support-for-conll-u-files/index.html
index 4fe945ea3..29a32787b 100644
--- a/blog/new-in-orange-support-for-conll-u-files/index.html
+++ b/blog/new-in-orange-support-for-conll-u-files/index.html
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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - New in Orange: Support for CONLL-U files</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - New in Orange: Support for CONLL-U files"/><meta property="og:description" content="Orange can now work with CONLL-U files!"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2021-09-15"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="conllu"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="corpus"/><meta property="article:tag" content="lemma"/><meta property="og:image" content="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-conllu.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - New in Orange: Support for CONLL-U files</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - New in Orange: Support for CONLL-U files"/><meta property="og:description" content="Orange can now work with CONLL-U files!"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2021-09-15"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="conllu"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="corpus"/><meta property="article:tag" content="lemma"/><meta property="og:image" content="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-conllu.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>conllu, text mining, corpus, lemma</strong></p><h1 class="sc-c67a4900-0 loxVcf">New in Orange: Support for CONLL-U files</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 15, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="https://universaldependencies.org/format.html">CONLL-U</a> files are ubiquitous in text mining and natural language processing. They can hold a great deal of linguistic data, specifically sentence boundaries, word lemmas, universal POS tags, language specific POS tag, morphological features, dependency relations, named entities, and so on. This is how a typical CONLL-U file looks like.</p>
 <pre><code>    # sent_id = ParlaMint-GB_2021-01-05-lords.seg2.2
     # text = The Hybrid Sitting of the House will now begin.
@@ -174,4 +174,4 @@
 <a href="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-preprocess-text.png" data-gallery="true"><img alt="" loading="lazy" width="1150" height="520" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-preprocess-text.png"/></a>
 <p>Looking at the Word Cloud, we can see that indeed only verbs and nouns were kept after preprocessing. And not only that! As we have selected to import lemmas, our words will already be normalized. Most of the preprocessing work is done for us! Now you can play with downstream analysis - for example, try to determine which words are significant for which MP using Word Enrichment!</p>
 <a href="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-word-cloud.png" data-gallery="true"><img alt="" loading="lazy" width="1188" height="697" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-word-cloud.png"/></a>
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+<a href="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="500" height="124" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-09-connlu-files/__optimized-images__/2021-09-15-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-09-15","draft":false,"title":"New in Orange: Support for CONLL-U files","thumbImage":"2021-09-15-conllu.png","frontPageImage":"2021-09-15-conllu.png","blog":["conllu","text mining","corpus","lemma"],"shortExcerpt":"Orange can now work with CONLL-U files!","longExcerpt":"Orange can now work with CONLL-U files, including its lemmas, POS tags, and named entities.","x2images":true,"oldUrl":"/blog/2021/2021-09-15-connlu-files/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://universaldependencies.org/format.html\",\n        children: \"CONLL-U\"\n      }), \" files are ubiquitous in text mining and natural language processing. 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diff --git a/blog/new-orange-icons/index.html b/blog/new-orange-icons/index.html
index ccde68269..a126d7037 100644
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>icons</strong></p><h1 class="sc-c67a4900-0 loxVcf">New Orange icons</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 06, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>As new and new widgets with new features are added to Orange, icons for them have to be drawn. Most of the time those are just some quick sketches or even missing altogether. But now we are starting to redraw and unify them. A few of them have already been made.</p>
 <a href="/blog/2012-01-new-orange-icons/__optimized-images__/featureconstructor_60.png__60x60_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2012-01-new-orange-icons/__optimized-images__/featureconstructor_60.png__60x60_q95_crop_upscale.png"/></a>
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+<a href="/blog/2012-01-new-orange-icons/__optimized-images__/basicsvm_60.png__60x60_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2012-01-new-orange-icons/__optimized-images__/basicsvm_60.png__60x60_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-01-06 14:54:00+00:00","draft":false,"title":"New Orange icons","blog":["icons"],"oldUrl":"/blog/2012/01/06/new-orange-icons/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"As new and new widgets with new features are added to Orange, icons for them have to be drawn. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>documentation, examples, tutorial</strong></p><h1 class="sc-c67a4900-0 loxVcf">New scripting tutorial</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 06, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange just got a new, completely rewritten <a href="http://docs.biolab.si/orange/2/tutorial/rst/index.html">scripting tutorial</a>. The tutorial uses Orange class hierarchy as introduced for version 2.5. The tutorial is supposed to be a gentle introduction in Orange scripting. It includes many examples, from really simple ones to those more complex. To give you a hint about the later, here is the code for learner with feature subset selection from:</p>
 <pre><code>    class SmallLearner(Orange.classification.PyLearner):
         def __init__(self, base_learner=Orange.classification.bayes.NaiveLearner,
@@ -185,4 +185,4 @@
 <pre><code>    print Counter(str(d.get_class()) for d in data)
 </code></pre>
 <p>Ok. Pretty print is missing, but that, if not in the same line, could be done in another one.</p>
-<p>For now, the tutorial focuses on data input and output, classification and regression. We plan to use other sections, but you can also give us a hint if there are any you would wish to be included.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2013-01-06 19:30:00+00:00","draft":false,"title":"New scripting tutorial","blog":["documentation","examples","tutorial"],"oldUrl":"/blog/2013/01/06/new-scripting-tutorial/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange just got a new, completely rewritten \", _jsx(_components.a, {\n        href: \"http://docs.biolab.si/orange/2/tutorial/rst/index.html\",\n        children: \"scripting tutorial\"\n      }), \". 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+<p>For now, the tutorial focuses on data input and output, classification and regression. We plan to use other sections, but you can also give us a hint if there are any you would wish to be included.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2013-01-06 19:30:00+00:00","draft":false,"title":"New scripting tutorial","blog":["documentation","examples","tutorial"],"oldUrl":"/blog/2013/01/06/new-scripting-tutorial/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange just got a new, completely rewritten \", _jsx(_components.a, {\n        href: \"http://docs.biolab.si/orange/2/tutorial/rst/index.html\",\n        children: \"scripting tutorial\"\n      }), \". 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - New Video Tutorials on Text Mining</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - New Video Tutorials on Text Mining"/><meta property="og:description" content="New video tutorials on text mining available on our YouTube channel."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2020-09-28"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="tutorial"/><meta property="article:tag" content="video"/><meta property="article:tag" content="twitter"/><meta property="article:tag" content="sentiment analysis"/><meta property="article:tag" content="embedding"/><meta property="og:image" content="/blog/2020-09-text-tutorials/__optimized-images__/2020-09-28-text-tutorials.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>text mining, tutorial, video, twitter, sentiment analysis, embedding</strong></p><h1 class="sc-c67a4900-0 loxVcf">New Video Tutorials on Text Mining</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 28, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In July, we were pleasantly surprised to be awarded a <a href="https://numfocus.org/">NumFocus</a> <a href="https://numfocus.org/programs/sustainability">Small Development Grant</a>, which is intended to support small tasks in open source projects they sponsor. We decided to extend our text mining tutorials with four new videos, which cover the recent additions to the Text Mining add-on. Our YouTube channel already has a playlist for getting started with Orange and several specialized playlists for learning spectroscopy, single-cell analysis, text mining and image analytics with Orange.</p>
 <p>Related: <a href="blog/2016/02/26/getting-started-series-pt2/" target="_blank" rel="noreferrer">Getting Started Series Part 2</a></p>
 <p>While <a href="https://orange.biolab.si/widget-catalog/text-mining/twitter-widget/">Twitter</a> widget is not a new addition to the Text add-on, it has been missing a tutorial all this time. In the video, we describe how to use the widget and how to perform topic modelling on tweets.</p>
@@ -160,4 +160,4 @@
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 <p>Finally, we show how to compute a network from Twitter mentions. This tutorials also shows how to mix-and-match Orange components from different add-ons.</p>
 <iframe width="560" height="315" src="https://www.youtube.com/embed/XE1_tPgfxoA" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
-<p>Don&#x27;t forget to <a href="https://www.youtube.com/c/OrangeDataMining">subscribe</a> to our channel for more videos! And give us a thumbs up if you enjoyed the tutorials. :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-09-28","draft":false,"title":"New Video Tutorials on Text Mining","thumbImage":"2020-09-28-text-tutorials.png","frontPageImage":"2020-09-28-text-tutorials.png","blog":["text mining","tutorial","video","twitter","sentiment analysis","embedding"],"shortExcerpt":"New video tutorials on text mining available on our YouTube channel.","longExcerpt":"New video tutorials on text mining available on our YouTube channel.","x2images":false,"oldUrl":"/blog/2020/2020-09-28-text-tutorials/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In July, we were pleasantly surprised to be awarded a \", _jsx(_components.a, {\n        href: \"https://numfocus.org/\",\n        children: \"NumFocus\"\n      }), \" \", _jsx(_components.a, {\n        href: \"https://numfocus.org/programs/sustainability\",\n        children: \"Small Development Grant\"\n      }), \", which is intended to support small tasks in open source projects they sponsor. 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+<p>Don&#x27;t forget to <a href="https://www.youtube.com/c/OrangeDataMining">subscribe</a> to our channel for more videos! And give us a thumbs up if you enjoyed the tutorials. :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-09-28","draft":false,"title":"New Video Tutorials on Text Mining","thumbImage":"2020-09-28-text-tutorials.png","frontPageImage":"2020-09-28-text-tutorials.png","blog":["text mining","tutorial","video","twitter","sentiment analysis","embedding"],"shortExcerpt":"New video tutorials on text mining available on our YouTube channel.","longExcerpt":"New video tutorials on text mining available on our YouTube channel.","x2images":false,"oldUrl":"/blog/2020/2020-09-28-text-tutorials/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew} = _components;\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In July, we were pleasantly surprised to be awarded a \", _jsx(_components.a, {\n        href: \"https://numfocus.org/\",\n        children: \"NumFocus\"\n      }), \" \", _jsx(_components.a, {\n        href: \"https://numfocus.org/programs/sustainability\",\n        children: \"Small Development Grant\"\n      }), \", which is intended to support small tasks in open source projects they sponsor. 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@@ -160,4 +160,4 @@
 <p>The same goes for all the other attributes - you can interactively explore how much a certain value contributes to the probability of a selected target class.</p>
 <p>But it gets even better! Instead of dragging the blue dots in the nomogram, you can feed it the data. In the workflow below, we pass the data through the Data Table widget and then feed the selected data instance to the Nomogram. The Nomogram would then show what is the probability of the target class for this particular instance, and it would &quot;explain&quot; what are the magnitudes of contributions of individual attribute values.</p>
 <a href="/blog/2017-06-nomogram/__optimized-images__/Screen-Shot-2017-04-19-at-14.38.43.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="739" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-06-nomogram/__optimized-images__/Screen-Shot-2017-04-19-at-14.38.43.png"/></a>
-<p>This makes Nomogram a great widget for understanding the model and for interactive data exploration.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-06-05 11:39:12+00:00","draft":false,"title":"Nomogram","blog":["analysis","classification","features","interactive data visualization","visualization"],"oldUrl":"/blog/2017/06/05/nomogram/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"One more exciting visualization has been introduced to Orange - a Nomogram. 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+<p>This makes Nomogram a great widget for understanding the model and for interactive data exploration.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-06-05 11:39:12+00:00","draft":false,"title":"Nomogram","blog":["analysis","classification","features","interactive data visualization","visualization"],"oldUrl":"/blog/2017/06/05/nomogram/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"One more exciting visualization has been introduced to Orange - a Nomogram. In general, \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Nomogram\",\n        children: \"nomograms\"\n      }), \" are graphical devices that can approximate the calculation of some function. A Nomogram widget in Orange visualizes Logistic Regression and Naive Bayes classification models, and compute the class probabilities given a set of attributes values. In the nomogram, we can check how changing of the attribute values affect the class probabilities, and since the widget (like widgets in Orange) is interactive, we can do this on the fly.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, how does it work? First, feed the Nomogram a classification model, say, Logistic Regression. 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The second box shows the most important attribute, sex, and its contribution to the probability of the target class (more for male, almost 0 for female). The final box shows the total probability of the target class for the selected values of attributes (blue dots).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The most important attribute, however, seems to be 'sex', where the chance for survival (target class = no) is lower for males than it is for females. How do I know? Grab the blue dot over the attribute and drag it from 'male' to 'female'. The total probability for dying on Titanic (survived=no) drops from 89% to 43%.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The same goes for all the other attributes - you can interactively explore how much a certain value contributes to the probability of a selected target class.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But it gets even better! Instead of dragging the blue dots in the nomogram, you can feed it the data. In the workflow below, we pass the data through the Data Table widget and then feed the selected data instance to the Nomogram. The Nomogram would then show what is the probability of the target class for this particular instance, and it would \\\"explain\\\" what are the magnitudes of contributions of individual attribute values.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-06-nomogram/__optimized-images__/Screen-Shot-2017-04-19-at-14.38.43.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"739\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This makes Nomogram a great widget for understanding the model and for interactive data exploration.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"nomogram"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Oasys: Orange Canvas applied to Optical Physics</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Oasys: Orange Canvas applied to Optical Physics"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-05-20 08:05:21+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="elettra"/><meta property="article:tag" content="esrf"/><meta property="article:tag" content="oasys"/><meta property="article:tag" content="orangecanvas"/><meta property="article:tag" content="physics"/><meta property="article:tag" content="synchrotron"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -158,4 +158,4 @@
 <p>Widgets in this case become parts of the simulation pipeline. Each widget has an input and output beam of light, just like real life devices, and the parameters within the widget are physical properties of a particular experimental object. Thus scientists can model the experiment in advance and do it much quicker and easier than before.</p>
 <a href="/blog/2016-05-oasys-orange-canvas-applied-to-optical-physics/__optimized-images__/unnamed.png" data-gallery="true"><img alt="" loading="lazy" width="1432" height="834" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-05-oasys-orange-canvas-applied-to-optical-physics/__optimized-images__/unnamed.png"/></a>
 <p>Furthermore, Orange and OASYS provide a user-friendly GUI that domain experts can quickly get used to. There are anecdotal evidences of renowned physicists, who preferred to do their analysis with outdated simulation tools. However, after using OASYS for just a few days, they were already completely comfortable and could reproduce previously calculated results in a software without any problem. Moreover, they did it within several days instead of weeks as before.</p>
-<p>This is the power of visual programming - providing a user-friendly interface for automating complicated calculations and quick prototyping.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-05-20 08:05:21+00:00","draft":false,"title":"Oasys: Orange Canvas applied to Optical Physics","blog":["elettra","esrf","oasys","orangecanvas","physics","synchrotron"],"oldUrl":"/blog/2016/05/20/oasys-orange-canvas-applied-to-optical-physics/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This week we’re hosting experts in optical physics from \", _jsx(_components.a, {\n        href: \"https://www.elettra.eu/\",\n        children: \"Elettra Sincrotrone Trieste\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"http://www.esrf.eu/\",\n        children: \"European Synchrotron Radiation Facility\"\n      }), \" in our laboratory. 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+<p>This is the power of visual programming - providing a user-friendly interface for automating complicated calculations and quick prototyping.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-05-20 08:05:21+00:00","draft":false,"title":"Oasys: Orange Canvas applied to Optical Physics","blog":["elettra","esrf","oasys","orangecanvas","physics","synchrotron"],"oldUrl":"/blog/2016/05/20/oasys-orange-canvas-applied-to-optical-physics/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This week we’re hosting experts in optical physics from \", _jsx(_components.a, {\n        href: \"https://www.elettra.eu/\",\n        children: \"Elettra Sincrotrone Trieste\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"http://www.esrf.eu/\",\n        children: \"European Synchrotron Radiation Facility\"\n      }), \" in our laboratory. For a long time they have been interested in developing a user interface that integrates different simulation tools and data analysis software within one environment. It all came true with Orange Canvas and the \", _jsx(_components.a, {\n        href: \"http://www.elettra.eu/oasys.html\",\n        children: \"OASYS system\"\n      }), \". We’ve already \", _jsx(_components.a, {\n        href: \"/blog/2014/08/26/orange-canvas-applied-to-x-ray-optics/\",\n        children: \"written about this\"\n      }), \" two years ago, when the idea first came up. Now the actual software is ready and is being used by researchers for everyday analysis and prototyping.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"OASYS is basically pure Orange Canvas (Orange but no data mining widgets) that is reconfigured for the needs of optical physicists. 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@@ -162,4 +162,4 @@
 <p>If we zoom in, we can see that &quot;the&quot;, &quot;and&quot;, &quot;of&quot;, &quot;to&quot;, &quot;a&quot; are by far the most frequent words. This calls for some preprocessing, particularly stopword removal. We place <a href="https://orangedatamining.com/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a> between Corpus and Word Cloud and use default preprocessing. Our bar plot has changed. Now, the most frequent word is &quot;said&quot;, so perhaps another round of stopword removal is necessary.</p>
 <a href="/blog/2021-01-word-distribution/__optimized-images__/2021-01-27-word-distribution.png" data-gallery="true"><img alt="" loading="lazy" width="1136" height="737" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-01-word-distribution/__optimized-images__/2021-01-27-word-distribution.png"/></a>
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-<p>Orange widgets are intended to be as general as possible, which it easy to stack them into custom workflows. Don&#x27;t forget to explore all the outputs different widgets offer, for example the <a href="https://orangedatamining.com/widget-catalog/text-mining/topicmodelling-widget/">All Topics</a> output from Topic Modelling, <a href="https://orangedatamining.com/widget-catalog/text-mining/concordance/">Concordances</a> from Concordance, or <a href="https://orangedatamining.com/blog/2019/2019-08-27-pivot-table/">Grouped Data</a> from Pivot Table.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-01-27","draft":false,"title":"Observing Word Distribution","thumbImage":"2021-01-27-word-distribution-small.png","frontPageImage":"2021-01-27-word-distribution-small.png","blog":["text mining","word distribution","bar plot","word cloud"],"shortExcerpt":"How to inspect word distribution in a corpus with in Orange.","longExcerpt":"How to inspect word distribution in a corpus with a clever combination of widgets in Orange.","x2images":true,"oldUrl":"/blog/2021/2021-01-27-word-distribution/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In text mining, one of key tasks is understanding and inspecting the corpus. It makes it easier to determine the preprocessing techniques and downstream analysis (the selection of document frequency weights, topic modelling technique, lemmatization and so on).\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Even though Orange sometimes doesn't have a widget for a specific task, the said task can be achieved with a combination of widgets and their outputs. Let us look at an example of word distribution. There is no such widget in Orange, but word distributions are generally available in \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/text-mining/wordcloud/\",\n        children: \"Word Cloud\"\n      }), \". Word Cloud shows a list of most frequent words and their frequencies on the left and a cloud visualization on the right.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-01-word-distribution/2021-01-27-word-cloud.png\",\n      width: \"1080\",\n      height: \"626\",\n      src: \"/blog/2021-01-word-distribution/__optimized-images__/2021-01-27-word-cloud.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a great start, but Word Cloud only shows the 100 words and the visualization doesn't directly correspond to the word frequency (words are scaled so that very frequent words don't overwhelm less frequent ones). Yet Word Cloud has an output called \", _jsx(_components.strong, {\n        children: \"Word Counts\"\n      }), \", which outputs a table with words and their frequencies in columns. Just what we would like to see!\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-01-word-distribution/2021-01-27-data-table.png\",\n      width: \"379\",\n      height: \"380\",\n      src: \"/blog/2021-01-word-distribution/__optimized-images__/2021-01-27-data-table.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Can we see these frequencies as distributions? Yes, we can. A general widget showing numeric values (such as word counts) is \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/visualize/barplot/\",\n        children: \"Bar Plot\"\n      }), \". We pass the Word Counts output of Word Cloud to Bar Plot. 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+<p>Orange widgets are intended to be as general as possible, which it easy to stack them into custom workflows. Don&#x27;t forget to explore all the outputs different widgets offer, for example the <a href="https://orangedatamining.com/widget-catalog/text-mining/topicmodelling-widget/">All Topics</a> output from Topic Modelling, <a href="https://orangedatamining.com/widget-catalog/text-mining/concordance/">Concordances</a> from Concordance, or <a href="https://orangedatamining.com/blog/2019/2019-08-27-pivot-table/">Grouped Data</a> from Pivot Table.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-01-27","draft":false,"title":"Observing Word Distribution","thumbImage":"2021-01-27-word-distribution-small.png","frontPageImage":"2021-01-27-word-distribution-small.png","blog":["text mining","word distribution","bar plot","word cloud"],"shortExcerpt":"How to inspect word distribution in a corpus with in Orange.","longExcerpt":"How to inspect word distribution in a corpus with a clever combination of widgets in Orange.","x2images":true,"oldUrl":"/blog/2021/2021-01-27-word-distribution/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In text mining, one of key tasks is understanding and inspecting the corpus. It makes it easier to determine the preprocessing techniques and downstream analysis (the selection of document frequency weights, topic modelling technique, lemmatization and so on).\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Even though Orange sometimes doesn't have a widget for a specific task, the said task can be achieved with a combination of widgets and their outputs. Let us look at an example of word distribution. There is no such widget in Orange, but word distributions are generally available in \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/text-mining/wordcloud/\",\n        children: \"Word Cloud\"\n      }), \". Word Cloud shows a list of most frequent words and their frequencies on the left and a cloud visualization on the right.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-01-word-distribution/2021-01-27-word-cloud.png\",\n      width: \"1080\",\n      height: \"626\",\n      src: \"/blog/2021-01-word-distribution/__optimized-images__/2021-01-27-word-cloud.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a great start, but Word Cloud only shows the 100 words and the visualization doesn't directly correspond to the word frequency (words are scaled so that very frequent words don't overwhelm less frequent ones). Yet Word Cloud has an output called \", _jsx(_components.strong, {\n        children: \"Word Counts\"\n      }), \", which outputs a table with words and their frequencies in columns. 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We can also label each bar by setting Annotations to Word.\"]\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-01-word-distribution/2021-01-27-bar-plot.png\",\n      width: \"1057\",\n      height: \"421\",\n      src: \"/blog/2021-01-word-distribution/__optimized-images__/2021-01-27-bar-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If we zoom in, we can see that \\\"the\\\", \\\"and\\\", \\\"of\\\", \\\"to\\\", \\\"a\\\" are by far the most frequent words. This calls for some preprocessing, particularly stopword removal. We place \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/text-mining/preprocesstext/\",\n        children: \"Preprocess Text\"\n      }), \" between Corpus and Word Cloud and use default preprocessing. Our bar plot has changed. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>statistical significance, hypothesis testing, p-value, multiple hypothesis testing</strong></p><h1 class="sc-c67a4900-0 loxVcf">Of Carrots and Horses and the Fear of Heights</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 29, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>A word of warning. When teaching in the US, I am careful enough to not say things like &quot;<em>consider a binary variable, for instance, gender</em>&quot;. But I&#x27;m writing this from Russia where considering gender as a binary variable is not only an acceptable but rather a desired, official position. In Russia you can even get away with some mild swearing. While I never use strong curse words, neither in the classroom nor in private, I occasionally say &quot;shit&quot;, which is, as I have been warned, not acceptable in US classrooms (while on corridors you can f... all you want). This blog entry targets Russian audience.</p>
 <center>* * *</center>
 <p>A(n imaginary) friend of mine has this hobby of searching for all kinds of weird relations. Basically to annoy people, and me in particular. Like &quot;<em>you know, I discovered that people who wear glasses tend to like horses</em>&quot;. Yes, sure they do. &quot;<em>No, seriously, I collected some data. One hundred people, actually. See? It&#x27;s totally scientific.</em>&quot; Like hell it is. &quot;<em>Prove me wrong then!</em>&quot; he said. Pathetic.</p>
@@ -192,4 +192,4 @@
 <p>Now connect Sieve to Random data. Open it and click Score Combinations. It will order the pairs according to their significance. For every pair we select, Sieve will also show its chi-square and the corresponding p-value (at the bottom left). Predictably, the first 50 pairs have p-values below 0.05. By using Score Combinations and picking the top ones, we are making the same mistake as my imaginary friend. (Sieve does not compute Pearson coefficients but chi squares, yet in this context the two statistics are completely related. You can check that Correlations and VizRank show the same order of pairs.)</p>
 <p>So using Score Combinations (or equivalent buttons in other widgets) and then claiming that you found and proved some relation, is exactly what my friend was doing.</p>
 <p>Does this mean that we shouldn&#x27;t use Score Combinations? Why does Orange have such buttons then? They are safe to use for as long as we do not consider relation that we found in this way as &quot;proven&quot;. Automated tools for forming hypotheses can, well, form hypotheses. To prove them, you need to check them on another data (and still risk a 5 % chance of being successful by pure luck, if you use statistical tests and require p &lt; 0.05) or, preferably, you should find the underlying reason for the correlation.</p>
-<p>Eating a lot of carrots decreases the chances of missing a bus because being more like a rabbit helps you run faster.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2019-09-29","draft":false,"title":"Of Carrots and Horses and the Fear of Heights","thumbImage":"2019-09-29-horses.jpg","frontPageImage":"2019-09-29-horses.jpg","blog":["statistical significance","hypothesis testing","p-value","multiple hypothesis testing"],"shortExcerpt":"A cautionary tale of imaginary friend who made too many hypotheses to test - and how Orange is no different","longExcerpt":"A cautionary tale of imaginary friend who made too many hypotheses to test - and how Orange is no different","oldUrl":"/blog/2019/2019-09-29-of-carrots-horses-and-fear-of-heights/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"A word of warning. When teaching in the US, I am careful enough to not say things like \\\"\", _jsx(_components.em, {\n        children: \"consider a binary variable, for instance, gender\"\n      }), \"\\\". But I'm writing this from Russia where considering gender as a binary variable is not only an acceptable but rather a desired, official position. In Russia you can even get away with some mild swearing. While I never use strong curse words, neither in the classroom nor in private, I occasionally say \\\"shit\\\", which is, as I have been warned, not acceptable in US classrooms (while on corridors you can f... all you want). This blog entry targets Russian audience.\"]\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A(n imaginary) friend of mine has this hobby of searching for all kinds of weird relations. Basically to annoy people, and me in particular. Like \\\"\", _jsx(_components.em, {\n        children: \"you know, I discovered that people who wear glasses tend to like horses\"\n      }), \"\\\". Yes, sure they do. \\\"\", _jsx(_components.em, {\n        children: \"No, seriously, I collected some data. One hundred people, actually. See? It's totally scientific.\"\n      }), \"\\\" Like hell it is. \\\"\", _jsx(_components.em, {\n        children: \"Prove me wrong then!\"\n      }), \"\\\" he said. Pathetic.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One day I've had enough and built a BS-detector, short for \\\"bullshit detector\\\". I invented a formula that computes the similarity between two columns of 0's and 1's. It's normalized so that it gives me a 1 if the columns are the same, a -1 if they are exactly opposite, and 0 if one tells us nothing about the other, that is, they are the same in exactly half of cases. The score can also be any other number in between, of course - the more different from 0, the stronger the relation. At first I called the formula \\\"Demšar's bullshit score\\\", but the next day it sounded too much like being about \\\"Demšar's bullshit\\\" rather than \\\"bullshit score\\\". So I made up a better name: \\\"Pearson correlation coefficient\\\". After watching all that Star Trek series featuring all kinds of \\\"rotating field harmonics\\\", I became rather proficient at inventing fancy names myself. \", _jsx(_components.em, {\n        children: \"Pearson correlation coefficient\"\n      }), \" sounds really convincing.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When the imaginary friend (children need them for company and professors need them for making up stories) brought me his next relation, I computed the so-called \\\"Pearson correlation coefficient\\\", got a result of 0.012 and explained what it meant. In his concrete example it meant that drinking apple juice has nothing to do with fear of heights.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course he complained that 0.012 is not zero, but I was prepared for this. I already realized that the numbers I'll get for his relations won't be neither 0 nor 1. So I needed to get some intuition into what kind of numbers to expect from his random (to my belief) relations. I produced some random data myself. I pretended to have a sample of one hundred people for which I recorded two properties. Both binary. One may be a tendency to scratch head, and the other binary variable may be, for instance, gender. Except that I of course had no actual data and just made up two random columns of zeros and ones. And computed my formula.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To do so, I opened Orange and created the following workflow. Widget Random Data from add-on \\\"Orange3-Educational\\\" provides random data drawn from different distributions. I set it up to prepare two variables drawn from Bernoulli distribution, which is a fancy name for a random column of 0's and 1's. The two variables are discrete, so I attached its output to Continuize to make them numeric. Widget Correlations computes the Demšar's ... I mean \\\"Pearson correlation coefficient\\\".\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-random-continuize-correlations.png\",\n      width: \"341\",\n      height: \"86\",\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-random-continuize-correlations.png\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-random.png\",\n      src: \"50%\",\n      width: \"471\",\n      height: \"356\",\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-random.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I checked the coefficient for my pair of random variables. It gave me 0.084, which is more than the score of his -- to his claim related -- pair, 0.012. I went back to Random Data, generated a different sample, and checked the correlation. And repeated it again. And again. Always larger than his 0.012. I told him that I can run this for 1000 times, but then I saw it's more practical to just construct 45 variables instead of 2 by changing parameter Variables in Random data to 45.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This gave me 990 pairs. A large majority of them was larger than 0.012 that he presented. Actually, I told him (after scrolling way down through the widget) that apple juice and fear of heights were so unrelated that 914 out of (almost) 1000 random relations were stronger than this.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I opened a beer about two minutes later, after closing the doors behind him. I earned it.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next day he came again. And the next. And the following day, too. He just wouldn't give up. And I kept opening beers until his fifth day's relation indeed had a BS score of 0.22 (and luckily made some sense -- I indeed believe that women can distinguish between more colors than men, who recognize about six or seven). I argued that 0.22 is still low, but he forced me to look into my correlations and, damn, only 32 out of 1000 \", _jsx(_components.em, {\n        children: \"random\"\n      }), \" relations were so strongly correlated. I had to yield this time: I confessed that random, uncorrelated variables were unlikely to give such strong BS scores.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We agreed that even some of 1000 random (non-)relations are bound to be related by chance, so I couldn't request that he beats all my random pairs. We decided that we will consider a relation significant, if its Pearson correlation coefficient is so large that only 5 % of random relations exceed it. With his samples of size 100, my experiments showed that this value was 0.204. So 5 % it was. I thought it safe enough, he thought it fair enough.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We now had a perfect test. One that we would both trust.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nothing happened for a few days. I believed I essentially won. He knew that we had a method for detecting his bullshit so he won't bother me again (so soon and so often).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Or so I thought. Several days later some devil brought him again, with some more relations to check. Total rubbish, but some were really strong. Then he confessed he made up the data for those. It made no sense why he would do this -- and then even confess. But when I looked into the data I realized he was trying to guess my secret formula! By the looks of it, he actually succeeded. I made a stand: I made him swear he would never ever cheated by falsifed data. Ever. Again. Or it was the last time I talk with him.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A day passed. He came grinning so I knew something was up. Eating carrots helps against missing a bus. It has beaten all except the top 2% of my random relations.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So I opened a beer -- with him. After the second one he opened up. He actually thanked me for the formula! He said it makes his life so much easier. I realized that I haven't won but shot myself in the foot. He now uses my formula methodically. Yes, he actually calls it a method. He even gave it a name, he calls it \\\"null-hypothesis significance testing\\\". I didn't know he was a fellow Star Trek fan.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"He collected a huge number of his stupid measurements over the years. All on the same group of one hundred individuals. He still believes that some variables are related. And I gave him a method for checking them. We agreed that any pair that beats at least 95% of my random pairs (for which it needs a score of 0.204) is truly correlated. So his method now is to simply compute my formula (which he knows under the name Pearson correlation coefficient) on all pairs of features. He picked one out of many pairs with correlation above 0.204 and brought it to me for testing. Hence the grin.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The worst thing was that he wasn't doing this (only) to tease me. He sincerely believed in the method. We agreed that this would be the way to test his relations and he was serious about it. If my formula gives the number above 0.204 for some relation, the relation is true.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"He thanked for the beer and promised to come again tomorrow. And the day after, too. Now that we are collaborators.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Чёрт! (Another confession: I'm writing this at Patriarch ponds, sitting on a bench with my face towards the pond and my back towards Malaya bronnaya.)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I had some serious thinking to do.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next day he came as promised. He told me about the latest relation he found. I forgot what it was; I didn't pay attention. Neither did I bother to check it -- because I knew he has. I instead asked him how many variables he has. Like, for every person he knows whether she or he smokes, wears socks at work, speaks any Hungarian, reads Russian grotesque novels ... how many different things? He said 154.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"\\\"\", _jsx(_components.em, {\n        children: \"Oh, this is quite a collection. Good work\"\n      }), \",\\\" I kept chatting while typing something into a calculator. \\\"\", _jsx(_components.em, {\n        children: \"So you must have identified around 590 'statistically significant' relations in your list, haven't you?\"\n      }), \"\\\" He was significantly less happy after hearing this. \\\"\", _jsx(_components.em, {\n        children: \"Actually 592,\"\n      }), \"\\\" he said. \\\"\", _jsx(_components.em, {\n        children: \"But -- how did you know?\"\n      }), \"\\\" \\\"\", _jsx(_components.em, {\n        children: \"People with strange beards like mine tend to have telepathic abilities,\"\n      }), \"\\\" I said seriosly. \\\"\", _jsx(_components.em, {\n        children: \"What?\"\n      }), \"\\\" \\\"\", _jsx(_components.em, {\n        children: \"Не шалю, никого не трогаю, починяю примус.\"\n      }), \"\\\"\"]\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I hate spoilers and told him nothing. But I'll tell you. It's easy. We can rather safely assume that all his correlations are random. So we can replicate his data without actually collecting it. Open the Random data widget and generate 154 variables instead of 45, so that you simulate \", _jsx(_components.em, {\n        children: \"his\"\n      }), \" data. Go to Correlations and check how many pairs of \", _jsx(_components.em, {\n        children: \"random\"\n      }), \" variables have a correlation above 0.204. You'll see there are around 590. Quite close to the number he got.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But I wrote that I typed the numbers into a calculator. Sure. I don't need Orange for this. With 154 variables, there are 154 * 153 / 2 = 11781 pairs. We agreed that he can show me any relation that beats all but top 5 % of random relations. His relations are random so there are 11781 * 0.05 = 590 relations in the top 5 %.\"\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The story of course follows the dogma of hypothesis testing. We test a relation by computing some statistics, like Pearson correlation coefficient, and compare it with the value of this statistics that we would get on random data of this kind. This can be done like here. We generate a great number of random cases and compute the statistics for each. If the tested hypothesis beats all except 3 % of random ones, we say p = 0.03, meaning that \\\"there is only 3% probability that a randomly generated hypothesis would reach such a high score\\\".\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There is of course a faster way to test the significance of the Pearson correlation coefficient. Pearson was a smart guy (and my calling his correlation coefficient a \\\"bullshit score\\\" shouldn't be taken out of the context of the story). Distribution of Pearson correlations for random data is known, so critical values (like 0.204 in our case) can be computed analytically, without simulation. We nevertheless used the simulation in this story to make it more evident what significance testing actually does.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The story is not without flaws. The 11891 hypotheses are not unrelated. More importantly, when I wrote that the guy went home unhappy (though grudging), I implied that I proved the lack of correlation. With the common null-hypotheses testing you cannot prove the null-hypothesis (in my case: the hypothesis that there is no correlation). Applied properly, he wins if my test proves the correlation, or we call it a draw if it doesn't. I can't win.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The moral of the story is that statistical tests should not be used in the way we use them here. They are not a magic bullet for testing the correctness of hypotheses. There is not just one but two problems here.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If the test requires beating 95 % of my random relations, it will incorrectly recognize 5 % of relations as significant. This is pretty obvious: the top 5 % of my relations beat the remaining 95 %. His random relations will be equally successful, by pure chance. I knew the test was not failsafe. But he cheated: he checked all his relations and brought me those that passed the test. This is the skill of the Texas sharpshooter: shooting first, and drawing targets around holes next. He just found the relations that were successful by chance. For this reason, statistical hypothesis testing requires that you form the hypothesis first, and only then test it on the data - instead of forming them form the data, like he did.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But even if he wouldn't cheat and he would pick pairs at random, with 5 % threshold, 1 of 20 hypothesis would still be successful by chance, so he would be happy once every full moon, assuming he'd take weekends off. In statistics, this is called multiple hypotheses testing. You have to account for randomly \\\"successful\\\" hypotheses by using various corrections. Let us mention just the Bonferroni correction as the simplest example.\"\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This being an Orange blog, let us conclude by showing a problem with (careless use of) Orange. Connect Distributions widget to Random data. A dialog will pop-up so you can decide which Correlation's output to use. Connect output \\\"Correlations\\\" to input \\\"Data\\\". Correlations now shows the distribution of Pearson correlation coefficients on this data. Correlation coefficient is significant if it's in the 5 % tail of this distribution. You can fit some curve to this if you wish.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(Figure, {\n        src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-distribution.png\",\n        caption: \"\"\n      }), \" \\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now connect Sieve to Random data. Open it and click Score Combinations. It will order the pairs according to their significance. For every pair we select, Sieve will also show its chi-square and the corresponding p-value (at the bottom left). Predictably, the first 50 pairs have p-values below 0.05. By using Score Combinations and picking the top ones, we are making the same mistake as my imaginary friend. (Sieve does not compute Pearson coefficients but chi squares, yet in this context the two statistics are completely related. You can check that Correlations and VizRank show the same order of pairs.)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So using Score Combinations (or equivalent buttons in other widgets) and then claiming that you found and proved some relation, is exactly what my friend was doing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Does this mean that we shouldn't use Score Combinations? Why does Orange have such buttons then? They are safe to use for as long as we do not consider relation that we found in this way as \\\"proven\\\". Automated tools for forming hypotheses can, well, form hypotheses. To prove them, you need to check them on another data (and still risk a 5 % chance of being successful by pure luck, if you use statistical tests and require p \u003c 0.05) or, preferably, you should find the underlying reason for the correlation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Eating a lot of carrots decreases the chances of missing a bus because being more like a rabbit helps you run faster.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":970,"height":646,"src":"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-horses.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"of-carrots-and-horses-and-the-fear-of-heights"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Eating a lot of carrots decreases the chances of missing a bus because being more like a rabbit helps you run faster.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2019-09-29","draft":false,"title":"Of Carrots and Horses and the Fear of Heights","thumbImage":"2019-09-29-horses.jpg","frontPageImage":"2019-09-29-horses.jpg","blog":["statistical significance","hypothesis testing","p-value","multiple hypothesis testing"],"shortExcerpt":"A cautionary tale of imaginary friend who made too many hypotheses to test - and how Orange is no different","longExcerpt":"A cautionary tale of imaginary friend who made too many hypotheses to test - and how Orange is no different","oldUrl":"/blog/2019/2019-09-29-of-carrots-horses-and-fear-of-heights/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"A word of warning. When teaching in the US, I am careful enough to not say things like \\\"\", _jsx(_components.em, {\n        children: \"consider a binary variable, for instance, gender\"\n      }), \"\\\". But I'm writing this from Russia where considering gender as a binary variable is not only an acceptable but rather a desired, official position. In Russia you can even get away with some mild swearing. While I never use strong curse words, neither in the classroom nor in private, I occasionally say \\\"shit\\\", which is, as I have been warned, not acceptable in US classrooms (while on corridors you can f... all you want). This blog entry targets Russian audience.\"]\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A(n imaginary) friend of mine has this hobby of searching for all kinds of weird relations. Basically to annoy people, and me in particular. Like \\\"\", _jsx(_components.em, {\n        children: \"you know, I discovered that people who wear glasses tend to like horses\"\n      }), \"\\\". Yes, sure they do. \\\"\", _jsx(_components.em, {\n        children: \"No, seriously, I collected some data. One hundred people, actually. See? It's totally scientific.\"\n      }), \"\\\" Like hell it is. \\\"\", _jsx(_components.em, {\n        children: \"Prove me wrong then!\"\n      }), \"\\\" he said. Pathetic.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One day I've had enough and built a BS-detector, short for \\\"bullshit detector\\\". I invented a formula that computes the similarity between two columns of 0's and 1's. It's normalized so that it gives me a 1 if the columns are the same, a -1 if they are exactly opposite, and 0 if one tells us nothing about the other, that is, they are the same in exactly half of cases. The score can also be any other number in between, of course - the more different from 0, the stronger the relation. At first I called the formula \\\"Demšar's bullshit score\\\", but the next day it sounded too much like being about \\\"Demšar's bullshit\\\" rather than \\\"bullshit score\\\". So I made up a better name: \\\"Pearson correlation coefficient\\\". After watching all that Star Trek series featuring all kinds of \\\"rotating field harmonics\\\", I became rather proficient at inventing fancy names myself. \", _jsx(_components.em, {\n        children: \"Pearson correlation coefficient\"\n      }), \" sounds really convincing.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When the imaginary friend (children need them for company and professors need them for making up stories) brought me his next relation, I computed the so-called \\\"Pearson correlation coefficient\\\", got a result of 0.012 and explained what it meant. In his concrete example it meant that drinking apple juice has nothing to do with fear of heights.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course he complained that 0.012 is not zero, but I was prepared for this. I already realized that the numbers I'll get for his relations won't be neither 0 nor 1. So I needed to get some intuition into what kind of numbers to expect from his random (to my belief) relations. I produced some random data myself. I pretended to have a sample of one hundred people for which I recorded two properties. Both binary. One may be a tendency to scratch head, and the other binary variable may be, for instance, gender. Except that I of course had no actual data and just made up two random columns of zeros and ones. And computed my formula.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To do so, I opened Orange and created the following workflow. Widget Random Data from add-on \\\"Orange3-Educational\\\" provides random data drawn from different distributions. I set it up to prepare two variables drawn from Bernoulli distribution, which is a fancy name for a random column of 0's and 1's. The two variables are discrete, so I attached its output to Continuize to make them numeric. Widget Correlations computes the Demšar's ... I mean \\\"Pearson correlation coefficient\\\".\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-random-continuize-correlations.png\",\n      width: \"341\",\n      height: \"86\",\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-random-continuize-correlations.png\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-random.png\",\n      src: \"50%\",\n      width: \"471\",\n      height: \"356\",\n      src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-random.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I checked the coefficient for my pair of random variables. It gave me 0.084, which is more than the score of his -- to his claim related -- pair, 0.012. I went back to Random Data, generated a different sample, and checked the correlation. And repeated it again. And again. Always larger than his 0.012. I told him that I can run this for 1000 times, but then I saw it's more practical to just construct 45 variables instead of 2 by changing parameter Variables in Random data to 45.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This gave me 990 pairs. A large majority of them was larger than 0.012 that he presented. Actually, I told him (after scrolling way down through the widget) that apple juice and fear of heights were so unrelated that 914 out of (almost) 1000 random relations were stronger than this.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I opened a beer about two minutes later, after closing the doors behind him. I earned it.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next day he came again. And the next. And the following day, too. He just wouldn't give up. And I kept opening beers until his fifth day's relation indeed had a BS score of 0.22 (and luckily made some sense -- I indeed believe that women can distinguish between more colors than men, who recognize about six or seven). I argued that 0.22 is still low, but he forced me to look into my correlations and, damn, only 32 out of 1000 \", _jsx(_components.em, {\n        children: \"random\"\n      }), \" relations were so strongly correlated. I had to yield this time: I confessed that random, uncorrelated variables were unlikely to give such strong BS scores.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We agreed that even some of 1000 random (non-)relations are bound to be related by chance, so I couldn't request that he beats all my random pairs. We decided that we will consider a relation significant, if its Pearson correlation coefficient is so large that only 5 % of random relations exceed it. With his samples of size 100, my experiments showed that this value was 0.204. So 5 % it was. I thought it safe enough, he thought it fair enough.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We now had a perfect test. One that we would both trust.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nothing happened for a few days. I believed I essentially won. He knew that we had a method for detecting his bullshit so he won't bother me again (so soon and so often).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Or so I thought. Several days later some devil brought him again, with some more relations to check. Total rubbish, but some were really strong. Then he confessed he made up the data for those. It made no sense why he would do this -- and then even confess. But when I looked into the data I realized he was trying to guess my secret formula! By the looks of it, he actually succeeded. I made a stand: I made him swear he would never ever cheated by falsifed data. Ever. Again. Or it was the last time I talk with him.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A day passed. He came grinning so I knew something was up. Eating carrots helps against missing a bus. It has beaten all except the top 2% of my random relations.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So I opened a beer -- with him. After the second one he opened up. He actually thanked me for the formula! He said it makes his life so much easier. I realized that I haven't won but shot myself in the foot. He now uses my formula methodically. Yes, he actually calls it a method. He even gave it a name, he calls it \\\"null-hypothesis significance testing\\\". I didn't know he was a fellow Star Trek fan.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"He collected a huge number of his stupid measurements over the years. All on the same group of one hundred individuals. He still believes that some variables are related. And I gave him a method for checking them. We agreed that any pair that beats at least 95% of my random pairs (for which it needs a score of 0.204) is truly correlated. So his method now is to simply compute my formula (which he knows under the name Pearson correlation coefficient) on all pairs of features. He picked one out of many pairs with correlation above 0.204 and brought it to me for testing. Hence the grin.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The worst thing was that he wasn't doing this (only) to tease me. He sincerely believed in the method. We agreed that this would be the way to test his relations and he was serious about it. If my formula gives the number above 0.204 for some relation, the relation is true.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"He thanked for the beer and promised to come again tomorrow. And the day after, too. Now that we are collaborators.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Чёрт! (Another confession: I'm writing this at Patriarch ponds, sitting on a bench with my face towards the pond and my back towards Malaya bronnaya.)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I had some serious thinking to do.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next day he came as promised. He told me about the latest relation he found. I forgot what it was; I didn't pay attention. Neither did I bother to check it -- because I knew he has. I instead asked him how many variables he has. Like, for every person he knows whether she or he smokes, wears socks at work, speaks any Hungarian, reads Russian grotesque novels ... how many different things? He said 154.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"\\\"\", _jsx(_components.em, {\n        children: \"Oh, this is quite a collection. Good work\"\n      }), \",\\\" I kept chatting while typing something into a calculator. \\\"\", _jsx(_components.em, {\n        children: \"So you must have identified around 590 'statistically significant' relations in your list, haven't you?\"\n      }), \"\\\" He was significantly less happy after hearing this. \\\"\", _jsx(_components.em, {\n        children: \"Actually 592,\"\n      }), \"\\\" he said. \\\"\", _jsx(_components.em, {\n        children: \"But -- how did you know?\"\n      }), \"\\\" \\\"\", _jsx(_components.em, {\n        children: \"People with strange beards like mine tend to have telepathic abilities,\"\n      }), \"\\\" I said seriosly. \\\"\", _jsx(_components.em, {\n        children: \"What?\"\n      }), \"\\\" \\\"\", _jsx(_components.em, {\n        children: \"Не шалю, никого не трогаю, починяю примус.\"\n      }), \"\\\"\"]\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I hate spoilers and told him nothing. But I'll tell you. It's easy. We can rather safely assume that all his correlations are random. So we can replicate his data without actually collecting it. Open the Random data widget and generate 154 variables instead of 45, so that you simulate \", _jsx(_components.em, {\n        children: \"his\"\n      }), \" data. Go to Correlations and check how many pairs of \", _jsx(_components.em, {\n        children: \"random\"\n      }), \" variables have a correlation above 0.204. You'll see there are around 590. Quite close to the number he got.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But I wrote that I typed the numbers into a calculator. Sure. I don't need Orange for this. With 154 variables, there are 154 * 153 / 2 = 11781 pairs. We agreed that he can show me any relation that beats all but top 5 % of random relations. His relations are random so there are 11781 * 0.05 = 590 relations in the top 5 %.\"\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The story of course follows the dogma of hypothesis testing. We test a relation by computing some statistics, like Pearson correlation coefficient, and compare it with the value of this statistics that we would get on random data of this kind. This can be done like here. We generate a great number of random cases and compute the statistics for each. If the tested hypothesis beats all except 3 % of random ones, we say p = 0.03, meaning that \\\"there is only 3% probability that a randomly generated hypothesis would reach such a high score\\\".\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There is of course a faster way to test the significance of the Pearson correlation coefficient. Pearson was a smart guy (and my calling his correlation coefficient a \\\"bullshit score\\\" shouldn't be taken out of the context of the story). Distribution of Pearson correlations for random data is known, so critical values (like 0.204 in our case) can be computed analytically, without simulation. We nevertheless used the simulation in this story to make it more evident what significance testing actually does.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The story is not without flaws. The 11891 hypotheses are not unrelated. More importantly, when I wrote that the guy went home unhappy (though grudging), I implied that I proved the lack of correlation. With the common null-hypotheses testing you cannot prove the null-hypothesis (in my case: the hypothesis that there is no correlation). Applied properly, he wins if my test proves the correlation, or we call it a draw if it doesn't. I can't win.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The moral of the story is that statistical tests should not be used in the way we use them here. They are not a magic bullet for testing the correctness of hypotheses. There is not just one but two problems here.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If the test requires beating 95 % of my random relations, it will incorrectly recognize 5 % of relations as significant. This is pretty obvious: the top 5 % of my relations beat the remaining 95 %. His random relations will be equally successful, by pure chance. I knew the test was not failsafe. But he cheated: he checked all his relations and brought me those that passed the test. This is the skill of the Texas sharpshooter: shooting first, and drawing targets around holes next. He just found the relations that were successful by chance. For this reason, statistical hypothesis testing requires that you form the hypothesis first, and only then test it on the data - instead of forming them form the data, like he did.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But even if he wouldn't cheat and he would pick pairs at random, with 5 % threshold, 1 of 20 hypothesis would still be successful by chance, so he would be happy once every full moon, assuming he'd take weekends off. In statistics, this is called multiple hypotheses testing. You have to account for randomly \\\"successful\\\" hypotheses by using various corrections. Let us mention just the Bonferroni correction as the simplest example.\"\n    }), \"\\n\", _jsx(\"center\", {\n      children: \"* * *\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This being an Orange blog, let us conclude by showing a problem with (careless use of) Orange. Connect Distributions widget to Random data. A dialog will pop-up so you can decide which Correlation's output to use. Connect output \\\"Correlations\\\" to input \\\"Data\\\". Correlations now shows the distribution of Pearson correlation coefficients on this data. Correlation coefficient is significant if it's in the 5 % tail of this distribution. You can fit some curve to this if you wish.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(Figure, {\n        src: \"/blog/2019-09-of-carrots-horses-and-fear-of-heights/2019-09-29-distribution.png\",\n        caption: \"\"\n      }), \" \\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now connect Sieve to Random data. Open it and click Score Combinations. It will order the pairs according to their significance. For every pair we select, Sieve will also show its chi-square and the corresponding p-value (at the bottom left). Predictably, the first 50 pairs have p-values below 0.05. By using Score Combinations and picking the top ones, we are making the same mistake as my imaginary friend. (Sieve does not compute Pearson coefficients but chi squares, yet in this context the two statistics are completely related. You can check that Correlations and VizRank show the same order of pairs.)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So using Score Combinations (or equivalent buttons in other widgets) and then claiming that you found and proved some relation, is exactly what my friend was doing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Does this mean that we shouldn't use Score Combinations? Why does Orange have such buttons then? They are safe to use for as long as we do not consider relation that we found in this way as \\\"proven\\\". Automated tools for forming hypotheses can, well, form hypotheses. To prove them, you need to check them on another data (and still risk a 5 % chance of being successful by pure luck, if you use statistical tests and require p \u003c 0.05) or, preferably, you should find the underlying reason for the correlation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Eating a lot of carrots decreases the chances of missing a bus because being more like a rabbit helps you run faster.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":970,"height":646,"src":"/blog/2019-09-of-carrots-horses-and-fear-of-heights/__optimized-images__/2019-09-29-horses.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"of-carrots-and-horses-and-the-fear-of-heights"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>model performance, confusion matrix, education</strong></p><h1 class="sc-c67a4900-0 loxVcf">On Expected Vomiting Time</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 28, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We just finished a lecture and a student came with a question. The lecture was, in short, about how predictive models compute probabilities; to use a model for making decisions, we must find a threshold that will minimize the cost, maximize the profit, strike a good balance between sensitivity and specificity, or increase the general human well-being in some other way. So this student came with a question about something I didn&#x27;t explain well, and she gave me one last chance.</p>
 <p>I had some data that looked like this.</p>
 <table><thead><tr><th align="right">ID</th><th align="center">   predicted prob.   </th><th align="center">аctual class</th></tr></thead><tbody><tr><td align="right">1</td><td align="center">0.984</td><td align="center">p</td></tr><tr><td align="right">2</td><td align="center">0.907</td><td align="center">p</td></tr><tr><td align="right">3</td><td align="center">0.881</td><td align="center">n</td></tr><tr><td align="right">4</td><td align="center">0.865</td><td align="center">n</td></tr><tr><td align="right">5</td><td align="center">0.815</td><td align="center">p</td></tr><tr><td align="right">6</td><td align="center">0.741</td><td align="center">p</td></tr><tr><td align="right">7</td><td align="center">0.735</td><td align="center">p</td></tr><tr><td align="right">8</td><td align="center">0.635</td><td align="center">n</td></tr><tr><td align="right">9</td><td align="center">0.582</td><td align="center">n</td></tr><tr><td align="right">10</td><td align="center">0.480</td><td align="center">p</td></tr><tr><td align="right">11</td><td align="center">0.413</td><td align="center">n</td></tr><tr><td align="right">12</td><td align="center">0.317</td><td align="center">n</td></tr><tr><td align="right">13</td><td align="center">0.287</td><td align="center">n</td></tr><tr><td align="right">14</td><td align="center">0.225</td><td align="center">n</td></tr><tr><td align="right">15</td><td align="center">0.216</td><td align="center">p</td></tr><tr><td align="right">16</td><td align="center">0.183</td><td align="center">n</td></tr></tbody></table>
@@ -184,4 +184,4 @@ <h2>Optimal threshold for the model</h2>
 <h3>Pedagogical Moral</h3>
 <p>The nice thing about the story is that we didn&#x27;t care about classification accuracy. Oh, yes, after fixing the threshold to 0.48, we can compute it (it&#x27;s 11/16 -- just count the p&#x27;s above and n&#x27;s below the threshold). We also see that the probability of administering a drug to a healthy person (and making him needlessly hug the toilet for seven days) is 40 %, because in our sample, we had 4 such cases out of 10 whom we gave the drug. We call such poor victims <em>false positives</em>, and 40 % is the <em>false discovery rate</em>. The miserables whom we don&#x27;t give the drug though we should, are <em>false negatives</em>. The <em>false omission rate</em> (being sick if you&#x27;re not given a drug) is 1/6. The probability of not being given the drug if you&#x27;re sick (<em>miss rate</em> or <em>false negative rate</em>) is 1/7. I&#x27;m of course just copying this from Wikipedia. Nobody knows the entire list of names.</p>
 <p>As a lecturer, I believe that emphasizing this list of names too much may do more harm than good. I usually show them the list just to say that all these things have names, but then try to compute something meaningful and not fancy-named. Try forcing them to learn the list and then give them a task like above. They&#x27;ll likely spend the entire exam guessing whether you want them to compute <em>specificity</em> or <em>critical success index</em> -- instead of simply computing the <em>expected vomiting time</em>. Which is not even on Wikipedia.</p>
-<p>Yet.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2019-09-28","draft":false,"title":"On Expected Vomiting Time","thumbImage":"2019-09-28-vomiting-pumpkin.jpg","frontPageImage":"2019-09-28-vomiting-pumpkin.jpg","blog":["model performance","confusion matrix","education"],"shortExcerpt":"Computing meaningful performance scores of models should be creative","longExcerpt":"A report of an interesting ending to a lecture about setting probability thresholds for predictive models","oldUrl":"/blog/2019/2019-09-28-on-expected-vomiting-time/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    table: \"table\",\n    thead: \"thead\",\n    tr: \"tr\",\n    th: \"th\",\n    tbody: \"tbody\",\n    td: \"td\",\n    em: \"em\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    h3: \"h3\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We just finished a lecture and a student came with a question. The lecture was, in short, about how predictive models compute probabilities; to use a model for making decisions, we must find a threshold that will minimize the cost, maximize the profit, strike a good balance between sensitivity and specificity, or increase the general human well-being in some other way. So this student came with a question about something I didn't explain well, and she gave me one last chance.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I had some data that looked like this.\"\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            align: \"right\",\n            children: \"ID\"\n          }), _jsx(_components.th, {\n            align: \"center\",\n            children: \"   predicted prob.   \"\n          }), _jsx(_components.th, {\n            align: \"center\",\n            children: \"аctual class\"\n          })]\n        })\n      }), _jsxs(_components.tbody, {\n        children: [_jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"1\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.984\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"2\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.907\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"3\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.881\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"4\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.865\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"5\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.815\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"6\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.741\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"7\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.735\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"8\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.635\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"9\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.582\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"10\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.480\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"11\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.413\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"12\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.317\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"13\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.287\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"14\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.225\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"15\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.216\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"16\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.183\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        })]\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The data is made up, so I had to make up its meaning to form an example. In the rush of a moment, I succumbed to classics: \\\"\", _jsx(_components.em, {\n        children: \"For each of those 16 people you predicted the probability for being sick. Whom are you going to give the cure?\"\n      }), \"\\\" Her answer was in line with the best manners of modern medical practice (who cares about bacteria becoming resistant to antibiotics?): \\\"\", _jsx(_components.em, {\n        children: \"To all!\"\n      }), \"\\\" I didn't expect this, but anyway planned to continue with emphasizing that this medicine is not harmless: \\\"\", _jsx(_components.em, {\n        children: \"People who take it will vomit for one week.\"\n      }), \"\\\" She turned by 180 degrees: \\\"\", _jsx(_components.em, {\n        children: \"Oh, no. To nobody!\"\n      }), \"\\\" I didn't expect this either, but it still led nicely to my next condition: \\\"\", _jsx(_components.em, {\n        children: \"But if a person is indeed sick and you don't give him the drug, he'll vomit for four weeks.\"\n      }), \"\\\"\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Decision making becomes much more interesting when both options have consequences.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, to restate:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"if you give the medicine to anybody, sick or healthy, he vomits for a week;\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"if you don't give the cure to a sick person, he vomits for four weeks -- and you can no longer help him after he starts.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Optimal threshold\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To solve the problem, we had to compute the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \". This being a fairly new medical term, we need to define it. For instance, what would be the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \" for a person who is sick with a probability of 0.865? If we have 1000 people with such probability of being sick, 865 will vomit (for four weeks) and 135 won't.\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"If we don't give these 1000 people the medicine, 865 will vomit for 4 weeks, hence the total vomiting time will be 4 x 865 = 3460 weeks, or 3.460 weeks per person, where the average includes those who are not vomiting. This is of course the same as if we just multiplied 4 x 0.865, without multiplying and then dividing by 1000.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"If we give these 1000 people the medicine, all 1000 will vomit for a week, thus totaling 1000 weeks of vomiting or, obviously, 1 week per person.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The latter option is better. Administering the medicine will reduce the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \" (per person) from 3.460 weeks to 1 week. Well done.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What about people with 0.216 probability of being sick? Expected vomiting time of such persons is 4 x 0.216 = 0.864, which is less than a week. No drug for them. (The poor bloke with the 0.216 probability is actually sick. But we don't know this yet. S..t happens. I mean, vomit happens.)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Obviously, the optimal threshold here is 0.250, which gives the expected vomiting time of 1 week, with or without the drug. We should administer the drug to all except the last three people in the list.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We could have computed this threshold even before having the model. It is a property of the disease and the drug, not the model.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For an extra task, compute the expected vomiting time with a threshold of 0.25. Note that you can't assume that 25 % of untreated people will vomit for 4 weeks: some of those below the 0.25 threshold may also have just 12 % or 3 % chance of being sick. Assume a uniform probability distribution.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Optimal threshold for the model\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have so far used just probabilities and ignored the information whether somebody is sick (p) or not (n), which is given in the third column. We also assumed that predicted probabilities were correct because we had nothing else to go by.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now let us take the true status into account. It will allow us to compute the actual vomiting times when using this concrete model at particular thresholds.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If we indeed used the threshold of 0.25 and administered the drug to the first 13 people, we'd have 13 people vomiting for 1 week, and the poor guy #15 suffering for 4, which gives us (well, which gives \", _jsx(_components.em, {\n        children: \"them\"\n      }), \") the total vomiting time of 13 + 4 = 17 weeks, or 17/16 = 1.0625 per person.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we instead used a threshold of 0.2, 15 people would vomit for 1 week and nobody for 4 weeks. This reduces total vomiting time to 15 weeks.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"(The actual story we developed with the student was slightly different, but roughly at this point, a student who was doing something else and has just started listening to our debate about expected and total vomiting times, interrupted us with: \\\"\", _jsx(_components.em, {\n        children: \"Who are all these people and why are they vomiting?\"\n      }), \"\\\" We had to explain that nobody is vomiting and that we (that is: I) just have a \", _jsx(_components.em, {\n        children: \"sick\"\n      }), \" sense for examples.)\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But there's an even better threshold. If we give the drug only to people whose probability is at least 0.48, 10 people will vomit for one week and the poor guy #15 for four weeks, giving us a total of 14 weeks of vomiting. Or 14/16 = 0.875 weeks per person.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We shouldn't go any higher. Every sick person whom we don't give a drug needs to be counterbalanced by at least three healthy people being freed from a one-week ordeal, and there are not enough healthy persons left above 0.48.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this way, we simulated what would happen if we used this model on the population represented by this sample of people. So if we use it in practice and our goal is to reduce the total (or average) vomiting time, we know to administer the drug to everybody with p ≥ 0.48.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Why the discrepancy? Why isn't the threshold that we computed above, 0.250, also the optimal threshold? 0.250 would be the optimal threshold if predicted probabilities were true probabilities. Model's may be badly calibrated. While there exist methods for fixing their calibration (though not with so little data), we here took a more direct approach and computed the threshold for probabilities as predicted by our model.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Pedagogical Moral\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The nice thing about the story is that we didn't care about classification accuracy. Oh, yes, after fixing the threshold to 0.48, we can compute it (it's 11/16 -- just count the p's above and n's below the threshold). We also see that the probability of administering a drug to a healthy person (and making him needlessly hug the toilet for seven days) is 40 %, because in our sample, we had 4 such cases out of 10 whom we gave the drug. We call such poor victims \", _jsx(_components.em, {\n        children: \"false positives\"\n      }), \", and 40 % is the \", _jsx(_components.em, {\n        children: \"false discovery rate\"\n      }), \". The miserables whom we don't give the drug though we should, are \", _jsx(_components.em, {\n        children: \"false negatives\"\n      }), \". The \", _jsx(_components.em, {\n        children: \"false omission rate\"\n      }), \" (being sick if you're not given a drug) is 1/6. The probability of not being given the drug if you're sick (\", _jsx(_components.em, {\n        children: \"miss rate\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"false negative rate\"\n      }), \") is 1/7. I'm of course just copying this from Wikipedia. Nobody knows the entire list of names.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As a lecturer, I believe that emphasizing this list of names too much may do more harm than good. I usually show them the list just to say that all these things have names, but then try to compute something meaningful and not fancy-named. Try forcing them to learn the list and then give them a task like above. They'll likely spend the entire exam guessing whether you want them to compute \", _jsx(_components.em, {\n        children: \"specificity\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"critical success index\"\n      }), \" -- instead of simply computing the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \". Which is not even on Wikipedia.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Yet.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":{"width":1280,"height":831,"src":"/blog/2019-09-on-expected-vomiting-time/__optimized-images__/2019-09-28-vomiting-pumpkin.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"on-expected-vomiting-time"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Yet.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2019-09-28","draft":false,"title":"On Expected Vomiting Time","thumbImage":"2019-09-28-vomiting-pumpkin.jpg","frontPageImage":"2019-09-28-vomiting-pumpkin.jpg","blog":["model performance","confusion matrix","education"],"shortExcerpt":"Computing meaningful performance scores of models should be creative","longExcerpt":"A report of an interesting ending to a lecture about setting probability thresholds for predictive models","oldUrl":"/blog/2019/2019-09-28-on-expected-vomiting-time/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    table: \"table\",\n    thead: \"thead\",\n    tr: \"tr\",\n    th: \"th\",\n    tbody: \"tbody\",\n    td: \"td\",\n    em: \"em\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    h3: \"h3\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We just finished a lecture and a student came with a question. The lecture was, in short, about how predictive models compute probabilities; to use a model for making decisions, we must find a threshold that will minimize the cost, maximize the profit, strike a good balance between sensitivity and specificity, or increase the general human well-being in some other way. So this student came with a question about something I didn't explain well, and she gave me one last chance.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I had some data that looked like this.\"\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            align: \"right\",\n            children: \"ID\"\n          }), _jsx(_components.th, {\n            align: \"center\",\n            children: \"   predicted prob.   \"\n          }), _jsx(_components.th, {\n            align: \"center\",\n            children: \"аctual class\"\n          })]\n        })\n      }), _jsxs(_components.tbody, {\n        children: [_jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"1\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.984\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"2\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.907\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"3\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.881\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"4\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.865\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"5\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.815\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"6\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.741\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"7\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.735\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"8\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.635\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"9\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.582\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"10\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.480\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"11\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.413\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"12\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.317\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"13\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.287\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"14\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.225\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"15\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.216\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"p\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            align: \"right\",\n            children: \"16\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"0.183\"\n          }), _jsx(_components.td, {\n            align: \"center\",\n            children: \"n\"\n          })]\n        })]\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The data is made up, so I had to make up its meaning to form an example. In the rush of a moment, I succumbed to classics: \\\"\", _jsx(_components.em, {\n        children: \"For each of those 16 people you predicted the probability for being sick. Whom are you going to give the cure?\"\n      }), \"\\\" Her answer was in line with the best manners of modern medical practice (who cares about bacteria becoming resistant to antibiotics?): \\\"\", _jsx(_components.em, {\n        children: \"To all!\"\n      }), \"\\\" I didn't expect this, but anyway planned to continue with emphasizing that this medicine is not harmless: \\\"\", _jsx(_components.em, {\n        children: \"People who take it will vomit for one week.\"\n      }), \"\\\" She turned by 180 degrees: \\\"\", _jsx(_components.em, {\n        children: \"Oh, no. To nobody!\"\n      }), \"\\\" I didn't expect this either, but it still led nicely to my next condition: \\\"\", _jsx(_components.em, {\n        children: \"But if a person is indeed sick and you don't give him the drug, he'll vomit for four weeks.\"\n      }), \"\\\"\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Decision making becomes much more interesting when both options have consequences.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, to restate:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"if you give the medicine to anybody, sick or healthy, he vomits for a week;\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"if you don't give the cure to a sick person, he vomits for four weeks -- and you can no longer help him after he starts.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Optimal threshold\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To solve the problem, we had to compute the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \". This being a fairly new medical term, we need to define it. For instance, what would be the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \" for a person who is sick with a probability of 0.865? If we have 1000 people with such probability of being sick, 865 will vomit (for four weeks) and 135 won't.\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"If we don't give these 1000 people the medicine, 865 will vomit for 4 weeks, hence the total vomiting time will be 4 x 865 = 3460 weeks, or 3.460 weeks per person, where the average includes those who are not vomiting. This is of course the same as if we just multiplied 4 x 0.865, without multiplying and then dividing by 1000.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"If we give these 1000 people the medicine, all 1000 will vomit for a week, thus totaling 1000 weeks of vomiting or, obviously, 1 week per person.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The latter option is better. Administering the medicine will reduce the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \" (per person) from 3.460 weeks to 1 week. Well done.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What about people with 0.216 probability of being sick? Expected vomiting time of such persons is 4 x 0.216 = 0.864, which is less than a week. No drug for them. (The poor bloke with the 0.216 probability is actually sick. But we don't know this yet. S..t happens. I mean, vomit happens.)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Obviously, the optimal threshold here is 0.250, which gives the expected vomiting time of 1 week, with or without the drug. We should administer the drug to all except the last three people in the list.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We could have computed this threshold even before having the model. It is a property of the disease and the drug, not the model.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For an extra task, compute the expected vomiting time with a threshold of 0.25. Note that you can't assume that 25 % of untreated people will vomit for 4 weeks: some of those below the 0.25 threshold may also have just 12 % or 3 % chance of being sick. Assume a uniform probability distribution.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Optimal threshold for the model\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have so far used just probabilities and ignored the information whether somebody is sick (p) or not (n), which is given in the third column. We also assumed that predicted probabilities were correct because we had nothing else to go by.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now let us take the true status into account. It will allow us to compute the actual vomiting times when using this concrete model at particular thresholds.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If we indeed used the threshold of 0.25 and administered the drug to the first 13 people, we'd have 13 people vomiting for 1 week, and the poor guy #15 suffering for 4, which gives us (well, which gives \", _jsx(_components.em, {\n        children: \"them\"\n      }), \") the total vomiting time of 13 + 4 = 17 weeks, or 17/16 = 1.0625 per person.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we instead used a threshold of 0.2, 15 people would vomit for 1 week and nobody for 4 weeks. This reduces total vomiting time to 15 weeks.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"(The actual story we developed with the student was slightly different, but roughly at this point, a student who was doing something else and has just started listening to our debate about expected and total vomiting times, interrupted us with: \\\"\", _jsx(_components.em, {\n        children: \"Who are all these people and why are they vomiting?\"\n      }), \"\\\" We had to explain that nobody is vomiting and that we (that is: I) just have a \", _jsx(_components.em, {\n        children: \"sick\"\n      }), \" sense for examples.)\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But there's an even better threshold. If we give the drug only to people whose probability is at least 0.48, 10 people will vomit for one week and the poor guy #15 for four weeks, giving us a total of 14 weeks of vomiting. Or 14/16 = 0.875 weeks per person.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We shouldn't go any higher. Every sick person whom we don't give a drug needs to be counterbalanced by at least three healthy people being freed from a one-week ordeal, and there are not enough healthy persons left above 0.48.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this way, we simulated what would happen if we used this model on the population represented by this sample of people. So if we use it in practice and our goal is to reduce the total (or average) vomiting time, we know to administer the drug to everybody with p ≥ 0.48.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Why the discrepancy? Why isn't the threshold that we computed above, 0.250, also the optimal threshold? 0.250 would be the optimal threshold if predicted probabilities were true probabilities. Model's may be badly calibrated. While there exist methods for fixing their calibration (though not with so little data), we here took a more direct approach and computed the threshold for probabilities as predicted by our model.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Pedagogical Moral\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The nice thing about the story is that we didn't care about classification accuracy. Oh, yes, after fixing the threshold to 0.48, we can compute it (it's 11/16 -- just count the p's above and n's below the threshold). We also see that the probability of administering a drug to a healthy person (and making him needlessly hug the toilet for seven days) is 40 %, because in our sample, we had 4 such cases out of 10 whom we gave the drug. We call such poor victims \", _jsx(_components.em, {\n        children: \"false positives\"\n      }), \", and 40 % is the \", _jsx(_components.em, {\n        children: \"false discovery rate\"\n      }), \". The miserables whom we don't give the drug though we should, are \", _jsx(_components.em, {\n        children: \"false negatives\"\n      }), \". The \", _jsx(_components.em, {\n        children: \"false omission rate\"\n      }), \" (being sick if you're not given a drug) is 1/6. The probability of not being given the drug if you're sick (\", _jsx(_components.em, {\n        children: \"miss rate\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"false negative rate\"\n      }), \") is 1/7. I'm of course just copying this from Wikipedia. Nobody knows the entire list of names.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As a lecturer, I believe that emphasizing this list of names too much may do more harm than good. I usually show them the list just to say that all these things have names, but then try to compute something meaningful and not fancy-named. Try forcing them to learn the list and then give them a task like above. They'll likely spend the entire exam guessing whether you want them to compute \", _jsx(_components.em, {\n        children: \"specificity\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"critical success index\"\n      }), \" -- instead of simply computing the \", _jsx(_components.em, {\n        children: \"expected vomiting time\"\n      }), \". Which is not even on Wikipedia.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Yet.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":{"width":1280,"height":831,"src":"/blog/2019-09-on-expected-vomiting-time/__optimized-images__/2019-09-28-vomiting-pumpkin.jpg"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"on-expected-vomiting-time"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange25</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange 2.5: code conversion</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MARKO</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 20, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange 2.5 unifies Orange&#x27;s C++ core and Python modules into a single module hierarchy. To use the new module hierarchy, import <strong>Orange</strong> instead of <strong>orange</strong> and accompanying <strong>orng</strong>* modules. While we will maintain backward compatibility in 2.* releases, we nevertheless suggest programmers to use the new interface. The provided <a href="http://orange.biolab.si/trac/intertrac/wiki%3AOrange25/RefactoringTool">conversion tool</a> can help refactor your code to use the new interface.</p>
 <p>The conversion script, <strong>orange2to25.py</strong>, resides in Orange&#x27;s main directory. To refactor <strong>accuracy8.py</strong> from the &quot;Orange for beginners&quot; tutorial run<strong>python orange2to25.py -w -o accuracy8_25.py doc/ofb-rst/code/accuracy8.py</strong>.</p>
 <p>The old code</p>
@@ -185,4 +185,4 @@
     cm = Orange.evaluation.scoring.compute_confusion_matrices(res,
             classIndex=data.domain.classVar.values.index(&#x27;democrat&#x27;))
 </code></pre>
-<p>Read more about <a href="http://orange.biolab.si/trac/intertrac/wiki%3AOrange25/RefactoringTool">the refactoring tool on the wiki</a> and on the help page (<strong>python orange2to25.py --help</strong>).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-12-20 12:21:00+00:00","draft":false,"title":"Orange 2.5: code conversion","blog":["orange25"],"oldUrl":"/blog/2011/12/20/orange-2-5-code-conversion/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange 2.5 unifies Orange's C++ core and Python modules into a single module hierarchy. 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+<p>Read more about <a href="http://orange.biolab.si/trac/intertrac/wiki%3AOrange25/RefactoringTool">the refactoring tool on the wiki</a> and on the help page (<strong>python orange2to25.py --help</strong>).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-12-20 12:21:00+00:00","draft":false,"title":"Orange 2.5: code conversion","blog":["orange25"],"oldUrl":"/blog/2011/12/20/orange-2-5-code-conversion/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange 2.5 unifies Orange's C++ core and Python modules into a single module hierarchy. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange 2.5 progress</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange 2.5 progress"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-05-20 15:08:00+00:00"/><meta property="article:author" content="MARKO"/><meta property="article:tag" content="orange25"/><meta name="next-head-count" content="17"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange25</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange 2.5 progress</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MARKO</strong></p><p class="sc-b9a2839b-4 ehbERY">May 20, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We decided that we should reorganize Orange to provide more intuitive interface to the scripting interface. The next release, Orange 2.5 is getting better every day. But fear not, dear reader, we are working hard to ensure that your scripts will still work.</p>
 <p>In the last morning of the camp in Bohinj we decided to use <strong>undercase_separated</strong> names instead of <strong>CamelCase</strong>. We have been steadily converting them and the <a href="/doc/orange25/Orange.misc.html#deprecation-utility-functions">deprecation utilities</a> by Aleš help a lot. We just list the name changes for class attributes or arguments and their renaming is handled gracefully; they also remain accessible with the old names. Therefore, the code does not need to be duplicated to ensure backwards compatibility.</p>
 <p>A simple example from the documentation of bagging and boosting. The old version first:</p>
@@ -183,4 +183,4 @@
 <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(learners)):
     <span class="hljs-built_in">print</span> (<span class="hljs-string">&quot;%15s: %5.3f&quot;</span>) % (learners[i].name, Orange.evaluation.scoring.CA(results)[i])
 </code></pre>
-<p>In new Orange we only need to import a single module, <strong>Orange</strong>, the root of the new hierarchy.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-05-20 15:08:00+00:00","draft":false,"title":"Orange 2.5 progress","blog":["orange25"],"oldUrl":"/blog/2011/05/20/orange-2-5-progress/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We decided that we should reorganize Orange to provide more intuitive interface to the scripting interface. The next release, Orange 2.5 is getting better every day. But fear not, dear reader, we are working hard to ensure that your scripts will still work.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the last morning of the camp in Bohinj we decided to use \", _jsx(_components.strong, {\n        children: \"undercase_separated\"\n      }), \" names instead of \", _jsx(_components.strong, {\n        children: \"CamelCase\"\n      }), \". We have been steadily converting them and the \", _jsx(_components.a, {\n        href: \"/doc/orange25/Orange.misc.html#deprecation-utility-functions\",\n        children: \"deprecation utilities\"\n      }), \" by Aleš help a lot. We just list the name changes for class attributes or arguments and their renaming is handled gracefully; they also remain accessible with the old names. 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+<p>In new Orange we only need to import a single module, <strong>Orange</strong>, the root of the new hierarchy.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2011-05-20 15:08:00+00:00","draft":false,"title":"Orange 2.5 progress","blog":["orange25"],"oldUrl":"/blog/2011/05/20/orange-2-5-progress/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"We decided that we should reorganize Orange to provide more intuitive interface to the scripting interface. 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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, pypi, release</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange 2.5a2 available</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 23, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange 2.5a2 has been uploaded to <a href="http://pypi.python.org/pypi/Orange">PyPI</a>. It now includes basic support for multi-label classification (developed during the Google Summer of Code 2011), some new widget icons and documentation for basket format. Release is also tagged on our <a href="https://bitbucket.org/biolab/orange/">Bitbucket repository</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-01-23 11:23:00+00:00","draft":false,"title":"Orange 2.5a2 available","blog":["gsoc","pypi","release"],"oldUrl":"/blog/2012/01/23/orange-2-5a2-available/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"Orange 2.5a2 has been uploaded to \", _jsx(_components.a, {\n      href: \"http://pypi.python.org/pypi/Orange\",\n      children: \"PyPI\"\n    }), \". 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It now includes basic support for multi-label classification (developed during the Google Summer of Code 2011), some new widget icons and documentation for basket format. 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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange 2.6</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange 2.6"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2013-01-21 14:23:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="pypi"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange 2.6</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange 2.6"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2013-01-21 14:23:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="pypi"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, pypi</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange 2.6</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 21, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>A new version of Orange, 2.6, has been uploaded to <a href="http://pypi.python.org/pypi/Orange/2.6/">Python Package Index</a>. Since the version on the Orange website is always up to date (we post daily builds), this may not affect you. Nevertheless, let us explain what we were working on for the last year.</p>
 <p>The most important improvement to Orange is an implementation of add-on framework that is much more &quot;standard pythonic&quot;. As a consequence, the add-on installation procedure has been simplified for both individual users and system administrators. For developers, the new framework eases the development and distribution of add-ons. This enabled us to make first steps towards the goal of removing the rarely used parts of Orange from the core distribution, which will ultimately result in less external dependencies and less warnings on module import. Orange 2.6 lacks the modules for network analysis (Orange.network) and prediction reliability assesment (Orange.reliability), but fear not: you can get them back by <a href="/blog/2012/11/30/the-easy-way-to-install-add-ons/">installing</a> the <a href="http://pypi.python.org/pypi/Orange-Network">Orange-Network</a> and <a href="http://pypi.python.org/pypi/Orange-Reliability">Orange-Reliability</a> add-ons.</p>
 <p>Apart from that, we have been mostly squashing bugs. A fun spare time activity - you can join us anytime by cloning our <a href="https://bitbucket.org/biolab/orange/">repository</a> and sending us a pull request. :)</p>
@@ -157,4 +157,4 @@
 <ul>
 <li>If you install orange from pypi, the version (Orange.version.full_version) will be something like 2.6 or 2.6.1.</li>
 <li>If you use our daily builds or build orange yourself from the source available in our repository, your version will be 2.6.1.dev-8804fbc. (minor will be larger by one and .dev- suffix will show the source control revision that was used for the build)</li>
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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange 2.7</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange 2.7"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2013-05-25 10:09:00+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="update"/><meta property="article:tag" content="version"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange 2.7</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange 2.7"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2013-05-25 10:09:00+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="update"/><meta property="article:tag" content="version"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -154,4 +154,4 @@
 <p>As a result of the project, Orange will be better connected with databases. Currently, all data is stored in working memory, while the forthcoming Orange 3.0 will be able to handle data that is stored in the database. We are working on a parallel computation architecture. Visualization of large data also presents a big challenge: we cannot transfer large amounts of data from the database to the desktop, and on the other hand it is difficult to provide a rich interactive experience if visualizations are created on the server-side. Also, most visualizations are intrinsically unsuitable for large data sets. For instance, the scatter plot represents each data instance with a symbol. Even when the datum is represented with a single pixel, only a few million data points fits on the computer screen. So in the context of big data, we will have to replace scatterplots with heatmaps.</p>
 <p>What have we got so far? Orange 3, which is in early stage of development, features a new architecture, which allows the data to be stored either in memory or on a database. In the latter case, selecting a subset of features or filtering the data does not copy the data but only modifies the queries that are used to access the data when needed. Computation of, for instance, distributions or contingency matrices is performed on the server, so only the minimal amount of data is transferred to the client.</p>
 <p>We also already have a small suite of widgets that work with this new architecture. Just to wet your appetite, here is the new box plot widget.</p>
-<a href="/blog/2013-09-orange-and-axle-project/__optimized-images__/boxplot-orange30.png__620x311_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="620" height="311" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-09-orange-and-axle-project/__optimized-images__/boxplot-orange30.png__620x311_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-09-02 21:27:00+00:00","draft":false,"title":"Orange and AXLE project","blog":["dataloading","features","future","orange3","sql"],"oldUrl":"/blog/2013/09/02/orange-and-axle-project/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Our group at University of Ljubljana is a partner in the EU 7FP project \", _jsx(_components.a, {\n        href: \"http://axleproject.eu/\",\n        children: \"Advanced Analytics for Extremely Large European Databases (AXLE)\"\n      }), \". 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange and SQL</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange and SQL"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2014-05-29 22:34:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="orange3"/><meta name="next-head-count" content="17"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange and SQL</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">May 29, 2014</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange 3.0 will also support working with data stored in a database.</p>
 <p>While we have already talked about this some time ago, we here describe some technical details for anybody interested. This is not a thorough tecnical report, its purpose is only to provide an impression about the architecture of the upcoming version of Orange.</p>
 <p>So, data tables in Orange 3.0 can refer to data in the working memory or in the database. Any (properly written) code that uses tables should work the same with both storages. When the data is stored in the database, the table is implemented as a &quot;proxy object&quot; with the necessary meta-data for constructing the SQL query to retrieve the data when needed. Operations on the data only modify the meta-data without retrieving any actual data. For instance, construction of a new table with some selected data subset, say all instances that match a certain condition, creates a new proxy with additional conditions for the WHERE clause. Similarly, selecting a subset of features only changes the domain (the list of features), which is later reflected in the columns of the SELECT clause.</p>
@@ -173,4 +173,4 @@
 <p>Point 9 requires some caution with regard to how the data is retrieved and what it is used for. Access to individual rows should be used sparingly. Sequential retrieval - especially of all rows - needs to be avoided. For efficiency, most methods that did so in the previous versions of Orange will need to be reimplemented to use aggregate data (possibly as approximations) or to be aware of the data storage and execute some operations directly through SQL.</p>
 <p>We have already ported a number of visualizations and other widgets to the new Orange. Here is one nice example: Mosaic needs to discretize the variables and then compute contingency matrices for discrete variables. Within the above scheme, the widget does not care about the storage mechanism, yet its computation is still as efficient as possible.</p>
 <a href="/blog/2014-05-orange-and-sql/__optimized-images__/mosaic.png__600x408_q95_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="599" height="408" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2014-05-orange-and-sql/__optimized-images__/mosaic.png__600x408_q95_upscale.png"/></a>
-<p>The described activities were funded in part by the European Union&#x27;s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 318633.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2014-05-29 22:34:00+00:00","draft":false,"title":"Orange and SQL","blog":["orange3"],"oldUrl":"/blog/2014/05/29/orange-and-sql/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ol: \"ol\",\n    li: \"li\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange 3.0 will also support working with data stored in a database.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While we have already talked about this some time ago, we here describe some technical details for anybody interested. This is not a thorough tecnical report, its purpose is only to provide an impression about the architecture of the upcoming version of Orange.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"So, data tables in Orange 3.0 can refer to data in the working memory or in the database. Any (properly written) code that uses tables should work the same with both storages. When the data is stored in the database, the table is implemented as a \\\"proxy object\\\" with the necessary meta-data for constructing the SQL query to retrieve the data when needed. Operations on the data only modify the meta-data without retrieving any actual data. For instance, construction of a new table with some selected data subset, say all instances that match a certain condition, creates a new proxy with additional conditions for the WHERE clause. 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The storage (a class derived from Table) now provides specialized methods for computing aggregates, distributions and contingencies, which use numpy for in-memory data and SQL for the data on the database.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Points 5 to 7 are implemented “lazily”, by modifying the SQL query describing the data as described above.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Point 8 is difficult to implement efficiently in common relational databases and, besides, results in a data matrix that is larger than the actual data. Methods that require such a matrix will need to be reimplemented and be aware of the storage mechanism.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Point 9 requires some caution with regard to how the data is retrieved and what it is used for. Access to individual rows should be used sparingly. Sequential retrieval - especially of all rows - needs to be avoided. 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+<p>The described activities were funded in part by the European Union&#x27;s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 318633.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2014-05-29 22:34:00+00:00","draft":false,"title":"Orange and SQL","blog":["orange3"],"oldUrl":"/blog/2014/05/29/orange-and-sql/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ol: \"ol\",\n    li: \"li\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange 3.0 will also support working with data stored in a database.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While we have already talked about this some time ago, we here describe some technical details for anybody interested. 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+<p>\</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-06-28","draft":false,"title":"Orange at 32nd Bled eConference","thumbImage":"2019-06-28_bled-small.png","frontPageImage":"2019-06-28_bled-small.png","blog":["workshop","education","data science"],"shortExcerpt":"We held a short workshop for the participants of the 32nd Bled eConference.","longExcerpt":"We held a short workshop covering the basics of Data Science for the participants of the 32nd Bled eConference. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>conference, education, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange at Eurostat&#x27;s Big Data Workshop</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 02, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>A <a href="http://ec.europa.eu/eurostat/cros/content/programme-ess-big-data-workshop-2016_en">Eurostat&#x27;s Big Data Workshop</a> recently took place in Ljubljana. In a presentation we have showcased <a href="http://orange.biolab.si">Orange</a> as a tool to teach data science.</p>
 <p>The meeting was organised by <a href="http://www.stat.si/StatWeb/en/home">Statistical Office of Slovenia</a> and by <a href="http://ec.europa.eu/eurostat">Eurostat</a>, a Statistical Office of the European Union, and was a primary gathering of representatives from national statistical institutes joined within <a href="http://ec.europa.eu/eurostat/web/ess">European Statistical System</a>. The meeting discussed possibilities that big data offers to modern statistics and the role it could play in statistical offices around the world. Say, can one use twitter data to measure costumer satisfaction? Or predict employment rates? Or use traffic information to predict GDP?</p>
 <p>During the meeting, <a href="https://www.nieuwbourg.com/english/#professional-experience">Philippe Nieuwbourg</a> from Canada pointed out that the stack of tools for big data analysis, and actually the tool stack for data science, are rather big and are growing larger each day. There is no way that data owners can master data bases, warehouses, Python, R, web development stacks, and similar. Are we alienating the owners and users from their own sources of information?</p>
@@ -170,4 +170,4 @@
 </ul>
 <p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2/">Data Mining Course at Baylor College of Medicine in Houston</a></p>
 <a href="/blog/2016-11-orange-at-eurostats-big-data-workshop/__optimized-images__/ess-big-data-cow.jpg" data-gallery="true"><img alt="" loading="lazy" width="1280" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-11-orange-at-eurostats-big-data-workshop/__optimized-images__/ess-big-data-cow.jpg"/></a>
-<p>The Eurostat meeting was very interesting and packed with new ideas. Our thanks to Boro Nikić for inviting us, and thanks to attendees of our session for the many questions and requests we have received during presentation and after the meeting.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-11-02 12:22:59+00:00","draft":false,"title":"Orange at Eurostat's Big Data Workshop","blog":["conference","education","orange3"],"oldUrl":"/blog/2016/11/02/orange-at-eurostats-big-data-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.a, {\n        href: \"http://ec.europa.eu/eurostat/cros/content/programme-ess-big-data-workshop-2016_en\",\n        children: \"Eurostat's Big Data Workshop\"\n      }), \" recently took place in Ljubljana. In a presentation we have showcased \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" as a tool to teach data science.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The meeting was organised by \", _jsx(_components.a, {\n        href: \"http://www.stat.si/StatWeb/en/home\",\n        children: \"Statistical Office of Slovenia\"\n      }), \" and by \", _jsx(_components.a, {\n        href: \"http://ec.europa.eu/eurostat\",\n        children: \"Eurostat\"\n      }), \", a Statistical Office of the European Union, and was a primary gathering of representatives from national statistical institutes joined within \", _jsx(_components.a, {\n        href: \"http://ec.europa.eu/eurostat/web/ess\",\n        children: \"European Statistical System\"\n      }), \". 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+<p>The Eurostat meeting was very interesting and packed with new ideas. Our thanks to Boro Nikić for inviting us, and thanks to attendees of our session for the many questions and requests we have received during presentation and after the meeting.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-11-02 12:22:59+00:00","draft":false,"title":"Orange at Eurostat's Big Data Workshop","blog":["conference","education","orange3"],"oldUrl":"/blog/2016/11/02/orange-at-eurostats-big-data-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.a, {\n        href: \"http://ec.europa.eu/eurostat/cros/content/programme-ess-big-data-workshop-2016_en\",\n        children: \"Eurostat's Big Data Workshop\"\n      }), \" recently took place in Ljubljana. In a presentation we have showcased \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" as a tool to teach data science.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The meeting was organised by \", _jsx(_components.a, {\n        href: \"http://www.stat.si/StatWeb/en/home\",\n        children: \"Statistical Office of Slovenia\"\n      }), \" and by \", _jsx(_components.a, {\n        href: \"http://ec.europa.eu/eurostat\",\n        children: \"Eurostat\"\n      }), \", a Statistical Office of the European Union, and was a primary gathering of representatives from national statistical institutes joined within \", _jsx(_components.a, {\n        href: \"http://ec.europa.eu/eurostat/web/ess\",\n        children: \"European Statistical System\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>geo, GIS, hierarchical clustering</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange at GIS Ostrava</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaz Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 24, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Ostrava is a city in the north-east of the Czech Republic and is the capital of the Moravian-Silesian Region. <a href="https://gisak.vsb.cz/gisostrava/index.php" target="_blank" rel="noreferrer">GIS Ostrava</a> is a yearly conference organized by Jiří Horák and his team at the <a href="https://www.vsb.cz/en" target="_blank" rel="noreferrer">Technical University of Ostrava</a>. University has a nice campus with a number of new developments. I have learned that this is the largest university campus in central and eastern Europe, as most of the universities, like mine, are city universities with buildings dispersed around the city.</p>
 <p>During the conference, I gave an invited talk on &quot;Data Science for Everyone&quot; and showed how Orange can be used to teach basic data science concepts in a few hours so that the trainee can gain some intuition about what data science is and then, preferably, use the software on their own data. To prove this concept, I gave an example workshop during the next day of the conference. The workshop was also attended by several teachers that are thinking of incorporating Orange within their data science curricula.</p>
 <p>\</p>
@@ -161,4 +161,4 @@
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 <p><br/>
 <!-- -->\</p>
-<p>Here, I would like to thank Igor Ivan and Jiří Horák for the invitation, and their group and specifically Michal Kacmarik for the hospitality.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaz Zupan","date":"2019-04-24","draft":false,"title":"Orange at GIS Ostrava","thumbImage":"ostrava-workshop.jpg","blog":["geo","GIS","hierarchical clustering"],"shortExcerpt":"In late March 2019, we have been invited to present Orange during a GIS Ostrava conference.","longExcerpt":"In late March 2019, we have been invited to present Orange during a GIS Ostrava conference. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, gsoc2016, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange at Google Summer of Code 2016</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 03, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange team is extremely excited to be a part of this year&#x27;s Google Summer of Code! GSoC is a great opportunity for students around the world to spend their summer contributing to an open-source software, gaining experience and earning money.</p>
 <p>Accepted students will help us develop Orange (or other chosen OSS project) from May to August. Each student is expected to select and define a project of his/her interest and will be ascribed a mentor to guide him/her through the entire process.</p>
 <p>Apply here:</p>
@@ -159,4 +159,4 @@
 <p>Our GSoC community forum:</p>
 <p><a href="https://groups.google.com/forum/#!forum/orange-gsoc">https://groups.google.com/forum/#!forum/orange-gsoc</a></p>
 <p>Spread the word! (and don&#x27;t forget to apply ;) )</p>
-<a href="/blog/2016-03-orange-at-google-summer-of-code-2016/__optimized-images__/1.png" data-gallery="true"><img alt="" loading="lazy" width="1119" height="1583" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-03-orange-at-google-summer-of-code-2016/__optimized-images__/1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-03-03 08:31:39+00:00","draft":false,"title":"Orange at Google Summer of Code 2016","blog":["gsoc","gsoc2016","orange3"],"oldUrl":"/blog/2016/03/03/orange-at-google-summer-of-code-2016/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange team is extremely excited to be a part of this year's Google Summer of Code! 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index bb129e1b4..fc30c4374 100644
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@@ -1,4 +1,4 @@
-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange at ISMB/ECCB 2011</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange at ISMB/ECCB 2011"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-08-03 09:14:00+00:00"/><meta property="article:author" content="MARKO"/><meta property="article:tag" content="bioinformatics"/><meta property="article:tag" content="bioorange"/><meta property="article:tag" content="conference"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange at ISMB/ECCB 2011</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange at ISMB/ECCB 2011"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-08-03 09:14:00+00:00"/><meta property="article:author" content="MARKO"/><meta property="article:tag" content="bioinformatics"/><meta property="article:tag" content="bioorange"/><meta property="article:tag" content="conference"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -154,4 +154,4 @@
 <p>We were giving out Orange stickers (with the URL) to the poster&#x27;s visitors. There was some interest; in the end we gave out about 10 of them, mostly to biologists, who were excited to perform some of the analysis themselves. Among the visitors was also a developer of a similar tool who seemed slightly surprised that something like this already exists, while another was disappointed because Orange only runs locally.</p>
 <p>See the poster in action on the photo taken by Gregor Rot.</p>
 <a href="/blog/2011-08-orange-at-ismbeccb-2011/__optimized-images__/orange-poster-pic.png__160x160_q95_crop.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-08-orange-at-ismbeccb-2011/__optimized-images__/orange-poster-pic.png__160x160_q95_crop.jpg"/></a>
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@@ -157,4 +157,4 @@
 <p><a href="https://www.iscb.org/ismbeccb2019">ISMB/ECCB 2019</a> is the largest and most prestigious bioinformatics conference. It joins the meetings of International and European and Society for Computational Biology. Martin Stražar, a post-doc at our Bioinformatics Lab in Ljubljana and responsible for much of developments in scOrange, presented <a href="https://singlecell.biolab.si">scOrange</a> in light of its support for case-based teaching and hands-on workshops. During the talk, Martin showcased several scOrange&#x27;s workflows. The one I liked best includes automatic annotation of cell-types in point-based visualizations.</p>
 <a href="/blog/2019-07-scorange/__optimized-images__/scorange-automatic-annotation.png" data-gallery="true"><img alt="" loading="lazy" width="800" height="622" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2019-07-scorange/__optimized-images__/scorange-automatic-annotation.png"/></a>
 <p>Martin&#x27;s presentation was based on our ISMB/ECCB paper just published in Bioinformatics:</p>
-<p>Martin Stražar,  Lan Žagar,  Jaka Kokošar,  Vesna Tanko,  Aleš Erjavec,  Pavlin G Poličar, Anže Starič,  Janez Demšar,  Gad Shaulsky,  Vilas Menon,  Andrew Lemire,  Anup Parikh, Blaž Zupan (2019) <a href="https://academic.oup.com/bioinformatics/article/35/14/i4/5529249">scOrange—a tool for hands-on training of concepts from single-cell data analytics</a>, Bioinformatics 35(14):i4–i12.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2019-07-25","draft":false,"title":"Orange at ISMB/ECCB 2019","thumbImage":"scorange-annotation-thumb.png","frontPageImage":"scorange-annotation-thumb.png","blog":["education","bioinformatics"],"shortExcerpt":"One of Orange's latest features is its add-on for single-cell data analytics.","oldUrl":"/blog/2019/2019-07-25-scorange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Our entry to this year's largest bioinformatics conference was on the training of single-cell data analytics. 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+<p>Martin Stražar,  Lan Žagar,  Jaka Kokošar,  Vesna Tanko,  Aleš Erjavec,  Pavlin G Poličar, Anže Starič,  Janez Demšar,  Gad Shaulsky,  Vilas Menon,  Andrew Lemire,  Anup Parikh, Blaž Zupan (2019) <a href="https://academic.oup.com/bioinformatics/article/35/14/i4/5529249">scOrange—a tool for hands-on training of concepts from single-cell data analytics</a>, Bioinformatics 35(14):i4–i12.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2019-07-25","draft":false,"title":"Orange at ISMB/ECCB 2019","thumbImage":"scorange-annotation-thumb.png","frontPageImage":"scorange-annotation-thumb.png","blog":["education","bioinformatics"],"shortExcerpt":"One of Orange's latest features is its add-on for single-cell data analytics.","oldUrl":"/blog/2019/2019-07-25-scorange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Our entry to this year's largest bioinformatics conference was on the training of single-cell data analytics. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange at Station Houston</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange at Station Houston"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-09-15 21:29:23+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="clustering"/><meta property="article:tag" content="images"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, images, orange3, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange at Station Houston</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 15, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>With over 262 member companies, <a href="http://stationhouston.com">Station Houston</a> is the largest hub for tech startups in Houston.</p>
 <a href="/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston.jpg" data-gallery="true"><img alt="" loading="lazy" width="1280" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston.jpg"/></a>
 <p>One of its members is also <a href="https://www.genialis.com">Genialis</a>, a life science data exploration company that emerged from our lab and is now delivering pipelines and user-friendly apps for analytics in systems biology.</p>
@@ -169,4 +169,4 @@
 <p>Data science and startups aside: there are some beautiful views from Station Houston. From the kitchen, there is a straight sight to Houston&#x27;s medical center looming about 4 miles away.</p>
 <a href="/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston-medical-center.jpg" data-gallery="true"><img alt="" loading="lazy" width="1280" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston-medical-center.jpg"/></a>
 <p>And on the other side, there is a great view of the downtown.</p>
-<a href="/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston-downtown.jpg" data-gallery="true"><img alt="" loading="lazy" width="1280" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston-downtown.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-09-15 21:29:23+00:00","draft":false,"title":"Orange at Station Houston","blog":["clustering","images","orange3","workshop"],"oldUrl":"/blog/2017/09/15/orange-at-station-houston/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"With over 262 member companies, \", _jsx(_components.a, {\n        href: \"http://stationhouston.com\",\n        children: \"Station Houston\"\n      }), \" is the largest hub for tech startups in Houston.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-orange-at-station-houston/__optimized-images__/station-houston.jpg\",\n      alt: \"\",\n      width: \"1280\",\n      height: \"960\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"One of its members is also \", _jsx(_components.a, {\n        href: \"https://www.genialis.com\",\n        children: \"Genialis\"\n      }), \", a life science data exploration company that emerged from our lab and is now delivering pipelines and user-friendly apps for analytics in systems biology.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Thanks to the invitation by the director of operations \", _jsx(_components.a, {\n        href: \"https://www.linkedin.com/in/alexdelafuente/\",\n        children: \"Alex de la Fuente\"\n      }), \", we gave a seminar on \", _jsx(_components.a, {\n        href: \"https://www.eventbrite.com/e/data-science-for-everyone-dr-blaz-zupan-baylor-college-of-medicine-tickets-36909445144#\",\n        children: \"Data Science for Everyone\"\n      }), \". 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 <p>Manuel Sanchez del Rio, from <a href="http://www.esrf.eu/">The European Synchrotron Facility</a> in Grenoble, France, and Luca Rebuffi from <a href="http://www.elettra.trieste.it/">Elettra-Sincrotrone, Trieste</a>, Italy, were looking for a tool that would help them integrate the various tools for x-ray optics simulations, like the popular SHADOW and SRW. They discovered that the data workflow paradigm, like the one used in Orange Canvas, fits their needs perfectly. They took Orange, and replaced the existing widgets with new widgets that represent sources of photons (bending magnets, in the case of ESRF), various optical elements, like lenses and mirrors, and detectors. The channels between the widgets no longer pass data tables, like in the standard Orange, but rays of photons. How cool is this?</p>
 <p>The result is a system in which the user can arrange the elements in a system that resembles the actual physical system, and then run the simulations using the most powerful tools available in x-ray optics.</p>
 <p>The tool prototype has been presented at the <a href="http://spie.org/optics-photonics.xml">SPIE Optics + Photonic 2014</a> in San Diego, the largest meeting of its kind.</p>
 <p>We&#x27;re really excited about this novel use of Orange Canvas.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>fairness, adversarial debiasing</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Fairness - Adversarial Debiasing</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Žan Mervič</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 19, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the <a href="/blog/2023-09-19-fairness-reweighing-preprocessor/">previous blog post</a>, we talked about how to use the Reweighing widget as a preprocessor for a model. This blog post will discuss the Adversarial Debiasing model, a bias-aware model. We will also show how to use it in Orange.</p>
 <h3>Adversarial Debiasing:</h3>
 <p><a href="https://arxiv.org/abs/1801.07593">Adversarial Debiasing</a> is an in-processing type of fairness mitigation algorithm. It is a technique that uses adversarial training to mitigate bias. It involves simultaneous training of a predictor and a discriminator. The goal of the predictor is to predict the target variable accurately. At the same time, the discriminator aims to predict the protected variable (such as gender or race) based on the predictor&#x27;s predictions. The main goal is to maximize the predictor&#x27;s ability to predict the target variable while reducing the discriminator&#x27;s ability to predict the protected variable based on those predictions. Because of the Adversarial Debiasing implementation in the <a href="https://aif360.res.ibm.com/">AIF360</a> package we are using, the algorithm focuses on Disparate Impact and Statistical Parity Difference fairness metrics.</p>
@@ -185,4 +185,4 @@ <h2>Orange use case</h2>
 <a href="/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-bias.png" data-gallery="true"><img alt="" loading="lazy" width="966" height="461" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-bias.png"/></a>
 <a href="/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-debias.png" data-gallery="true"><img alt="" loading="lazy" width="967" height="465" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-debias.png"/></a>
 <p>From the first box plot we can see that when using the model without debiasing, males (the privileged group) tends to get classified with &quot;&gt;50K&quot; (the favorable class) more often than females (the unprivileged grouo) do. This is reflected in the Disparate Impact and Statistical Parity Difference metrics which are 0.294 and -0.180, respectively, both below their optimal value, indicating bias towards the unprivileged group.</p>
-<p>In the second box plot, we can see that when using the model with debiasing, males and females get classified with the favorable class at a very similar rate. This is also indicated by the Disparate Impact and Statistical Parity Difference metrics which are 1.051 and 0.006, respectively, both very close to their optimal value, indicating a negligible amount of bias towards the privileged group.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 01:00:00+00:00","draft":false,"title":"Orange Fairness - Adversarial Debiasing","thumbImage":"2023-09-19-fairness-adversarial-debiasing-thumb.png","frontPageImage":"2023-09-19-fairness-adversarial-debiasing-thumb.png","blog":["fairness","adversarial debiasing"],"shortExcerpt":"Learn how to use the Adversarial Debiasing model in Orange for fairer machine learning.","longExcerpt":"This blog post focuses on the Adversarial Debiasing model in Orange, a tool for enhancing fairness in your machine learning algorithms. We will walk through how to use it and explain the trade-offs that come with using fairness algorithms.","oldUrl":"/blog/2023/2023-09-19-fairness-adversarial-debiasing/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-reweighing-preprocessor/\",\n        children: \"previous blog post\"\n      }), \", we talked about how to use the Reweighing widget as a preprocessor for a model. This blog post will discuss the Adversarial Debiasing model, a bias-aware model. We will also show how to use it in Orange.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Adversarial Debiasing:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1801.07593\",\n        children: \"Adversarial Debiasing\"\n      }), \" is an in-processing type of fairness mitigation algorithm. It is a technique that uses adversarial training to mitigate bias. It involves simultaneous training of a predictor and a discriminator. The goal of the predictor is to predict the target variable accurately. At the same time, the discriminator aims to predict the protected variable (such as gender or race) based on the predictor's predictions. The main goal is to maximize the predictor's ability to predict the target variable while reducing the discriminator's ability to predict the protected variable based on those predictions. Because of the Adversarial Debiasing implementation in the \", _jsx(_components.a, {\n        href: \"https://aif360.res.ibm.com/\",\n        children: \"AIF360\"\n      }), \" package we are using, the algorithm focuses on Disparate Impact and Statistical Parity Difference fairness metrics.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget's interface is shown in the image below.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing.png\",\n      width: \"242\",\n      height: \"398\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As seen from the image, there are some unique parameters for this widget:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Neurons in hidden layers: The number of neurons in each of the hidden layers of the neural network.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Use Debiasing: Whether to use the debiasing or not. The widget will function as a regular neural network model if this option is not selected.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Adversary loss weight: The weight of the adversary loss in the total loss function. The adversary loss is the loss function of the discriminator. The higher the weight, the more the model will try to reduce the discriminator's ability to predict the protected variable at the expense of the predictor's ability to predict the target variable.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we know how the Adversarial Debiasing widget works and how to use it, let us look at a real-world example for a classification task.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this example, we will use the \", _jsx(_components.a, {\n        href: \"https://archive.ics.uci.edu/ml/datasets/adult\",\n        children: \"Adult dataset\"\n      }), \", which we have used \", _jsx(_components.a, {\n        href: \"/blog/2023-08-23-fairness-dataset-bias/\",\n        children: \"before\"\n      }), \". The Adult dataset consists of 48824 instances with 15 attributes describing demographic details from the 1994 census. The primary task is to predict if an individual earns more than $50,000 annually. Unlike previously, we will not use the As Fairness widget to select fairness attributes; instead, we will keep the default ones, \\\"sex\\\" for the protected attribute and \\\"male\\\" for the privileged protected attribute value.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will train two Adversarial Debiasing models, one with and one without debiasing, and compare them to Random Forests.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-use-case.png\",\n      width: \"822\",\n      height: \"368\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-use-case.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-scores.png\",\n      width: \"968\",\n      height: \"531\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Test and Score shows that debiasing improved fairness metrics, particularly Disparate Impact, and Statistical Parity Difference. Without debiasing, the results are similar to that of the Random Forest model. Disparate Impact moved from 0.294 to 1.051, while Statistical Parity Difference went from -0.180 to 0.006, indicating a near-zero bias between these groups regarding favorable outcomes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"However, this does not mean all forms of bias were addressed. Equal Opportunity Difference and Average Odds Difference metrics worsened from -0.097 to 0.358 and -0.087 to 0.197, respectively. This suggests that even though the algorithm has been optimized for specific fairness criteria, other biases have emerged or become more pronounced.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Why is this happening?\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Trade-offs in fairness: Addressing one type of fairness sometimes comes at the expense of another. When we optimize for Disparate Impact or Statistical Parity Difference, it may affect how often positive outcomes are correctly predicted for both privileged and unprivileged groups (equal opportunity). Moreover, it could alter the balance between true positive rates and false positive rates for these groups (average odds difference).\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Adversarial Debiasing implementation: The current algorithm implementation aims to make predictions independent of protected attributes, focusing on balancing outcomes. This approach inherently focuses on balancing outcomes (reflected in the Disparate Impact and Statistical Parity Difference metrics). However, the nuances of other fairness measures might not be as effectively addressed.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When we use debiasing, there can also be a slight decrease in accuracy. This happens because the model is now not only trying to be accurate but also fair. This balance is a necessary trade-off we accept when we want to remove bias. However, it is worth noting that in this example, the accuracy remains comparable to the scenario without debiasing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, let us look at the Box Plot widget. We will use it to show the Disparate Impact and Statistical Parity Difference, much like our previous blog approach.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-box-plot-bias.png\",\n      width: \"966\",\n      height: \"461\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-bias.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-box-plot-debias.png\",\n      width: \"967\",\n      height: \"465\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-debias.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"From the first box plot we can see that when using the model without debiasing, males (the privileged group) tends to get classified with \\\"\u003e50K\\\" (the favorable class) more often than females (the unprivileged grouo) do. This is reflected in the Disparate Impact and Statistical Parity Difference metrics which are 0.294 and -0.180, respectively, both below their optimal value, indicating bias towards the unprivileged group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the second box plot, we can see that when using the model with debiasing, males and females get classified with the favorable class at a very similar rate. This is also indicated by the Disparate Impact and Statistical Parity Difference metrics which are 1.051 and 0.006, respectively, both very close to their optimal value, indicating a negligible amount of bias towards the privileged group.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":470,"height":513,"src":"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-adversarial-debiasing"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>In the second box plot, we can see that when using the model with debiasing, males and females get classified with the favorable class at a very similar rate. This is also indicated by the Disparate Impact and Statistical Parity Difference metrics which are 1.051 and 0.006, respectively, both very close to their optimal value, indicating a negligible amount of bias towards the privileged group.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 01:00:00+00:00","draft":false,"title":"Orange Fairness - Adversarial Debiasing","thumbImage":"2023-09-19-fairness-adversarial-debiasing-thumb.png","frontPageImage":"2023-09-19-fairness-adversarial-debiasing-thumb.png","blog":["fairness","adversarial debiasing"],"shortExcerpt":"Learn how to use the Adversarial Debiasing model in Orange for fairer machine learning.","longExcerpt":"This blog post focuses on the Adversarial Debiasing model in Orange, a tool for enhancing fairness in your machine learning algorithms. We will walk through how to use it and explain the trade-offs that come with using fairness algorithms.","oldUrl":"/blog/2023/2023-09-19-fairness-adversarial-debiasing/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-reweighing-preprocessor/\",\n        children: \"previous blog post\"\n      }), \", we talked about how to use the Reweighing widget as a preprocessor for a model. This blog post will discuss the Adversarial Debiasing model, a bias-aware model. We will also show how to use it in Orange.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Adversarial Debiasing:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1801.07593\",\n        children: \"Adversarial Debiasing\"\n      }), \" is an in-processing type of fairness mitigation algorithm. It is a technique that uses adversarial training to mitigate bias. It involves simultaneous training of a predictor and a discriminator. The goal of the predictor is to predict the target variable accurately. At the same time, the discriminator aims to predict the protected variable (such as gender or race) based on the predictor's predictions. The main goal is to maximize the predictor's ability to predict the target variable while reducing the discriminator's ability to predict the protected variable based on those predictions. Because of the Adversarial Debiasing implementation in the \", _jsx(_components.a, {\n        href: \"https://aif360.res.ibm.com/\",\n        children: \"AIF360\"\n      }), \" package we are using, the algorithm focuses on Disparate Impact and Statistical Parity Difference fairness metrics.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget's interface is shown in the image below.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing.png\",\n      width: \"242\",\n      height: \"398\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"As seen from the image, there are some unique parameters for this widget:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Neurons in hidden layers: The number of neurons in each of the hidden layers of the neural network.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Use Debiasing: Whether to use the debiasing or not. The widget will function as a regular neural network model if this option is not selected.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Adversary loss weight: The weight of the adversary loss in the total loss function. The adversary loss is the loss function of the discriminator. The higher the weight, the more the model will try to reduce the discriminator's ability to predict the protected variable at the expense of the predictor's ability to predict the target variable.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we know how the Adversarial Debiasing widget works and how to use it, let us look at a real-world example for a classification task.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this example, we will use the \", _jsx(_components.a, {\n        href: \"https://archive.ics.uci.edu/ml/datasets/adult\",\n        children: \"Adult dataset\"\n      }), \", which we have used \", _jsx(_components.a, {\n        href: \"/blog/2023-08-23-fairness-dataset-bias/\",\n        children: \"before\"\n      }), \". The Adult dataset consists of 48824 instances with 15 attributes describing demographic details from the 1994 census. The primary task is to predict if an individual earns more than $50,000 annually. Unlike previously, we will not use the As Fairness widget to select fairness attributes; instead, we will keep the default ones, \\\"sex\\\" for the protected attribute and \\\"male\\\" for the privileged protected attribute value.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will train two Adversarial Debiasing models, one with and one without debiasing, and compare them to Random Forests.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-use-case.png\",\n      width: \"822\",\n      height: \"368\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-use-case.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-scores.png\",\n      width: \"968\",\n      height: \"531\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Test and Score shows that debiasing improved fairness metrics, particularly Disparate Impact, and Statistical Parity Difference. Without debiasing, the results are similar to that of the Random Forest model. Disparate Impact moved from 0.294 to 1.051, while Statistical Parity Difference went from -0.180 to 0.006, indicating a near-zero bias between these groups regarding favorable outcomes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"However, this does not mean all forms of bias were addressed. Equal Opportunity Difference and Average Odds Difference metrics worsened from -0.097 to 0.358 and -0.087 to 0.197, respectively. This suggests that even though the algorithm has been optimized for specific fairness criteria, other biases have emerged or become more pronounced.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Why is this happening?\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Trade-offs in fairness: Addressing one type of fairness sometimes comes at the expense of another. When we optimize for Disparate Impact or Statistical Parity Difference, it may affect how often positive outcomes are correctly predicted for both privileged and unprivileged groups (equal opportunity). Moreover, it could alter the balance between true positive rates and false positive rates for these groups (average odds difference).\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Adversarial Debiasing implementation: The current algorithm implementation aims to make predictions independent of protected attributes, focusing on balancing outcomes. This approach inherently focuses on balancing outcomes (reflected in the Disparate Impact and Statistical Parity Difference metrics). However, the nuances of other fairness measures might not be as effectively addressed.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When we use debiasing, there can also be a slight decrease in accuracy. This happens because the model is now not only trying to be accurate but also fair. This balance is a necessary trade-off we accept when we want to remove bias. However, it is worth noting that in this example, the accuracy remains comparable to the scenario without debiasing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, let us look at the Box Plot widget. We will use it to show the Disparate Impact and Statistical Parity Difference, much like our previous blog approach.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-box-plot-bias.png\",\n      width: \"966\",\n      height: \"461\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-bias.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/2023-09-19-fairness-adversarial-debiasing-box-plot-debias.png\",\n      width: \"967\",\n      height: \"465\",\n      src: \"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-box-plot-debias.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"From the first box plot we can see that when using the model without debiasing, males (the privileged group) tends to get classified with \\\"\u003e50K\\\" (the favorable class) more often than females (the unprivileged grouo) do. This is reflected in the Disparate Impact and Statistical Parity Difference metrics which are 0.294 and -0.180, respectively, both below their optimal value, indicating bias towards the unprivileged group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the second box plot, we can see that when using the model with debiasing, males and females get classified with the favorable class at a very similar rate. This is also indicated by the Disparate Impact and Statistical Parity Difference metrics which are 1.051 and 0.006, respectively, both very close to their optimal value, indicating a negligible amount of bias towards the privileged group.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":470,"height":513,"src":"/blog/2023-09-fairness-adversarial-debiasing/__optimized-images__/2023-09-19-fairness-adversarial-debiasing-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-adversarial-debiasing"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange Fairness - Dataset Bias</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange Fairness - Dataset Bias"/><meta property="og:description" content="Orange now supports methods for detecting and mitigating bias in machine learning."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-09-18"/><meta property="article:author" content="Žan Mervič"/><meta property="article:tag" content="fairness"/><meta property="article:tag" content="dataset bias"/><meta property="og:image" content="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"/><meta property="og:image:width" content="562"/><meta property="og:image:height" content="546"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -205,4 +205,4 @@ <h2>Orange use case</h2>
 <a href="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-use-case.png" data-gallery="true"><img alt="" loading="lazy" width="947" height="291" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-use-case.png"/></a>
 <p>The results show that the dataset exhibits bias: the disparate impact is 0.358, and the statistical parity difference stands at -0.196. In an ideal scenario signifying no bias, these values would be 1 and 0, respectively. We can further visualize the dataset&#x27;s bias using the Box Plot.</p>
 <a href="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-box-plot.png" data-gallery="true"><img alt="" loading="lazy" width="1038" height="473" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-box-plot.png"/></a>
-<p>The Box Plot illustrates the distribution of the target variable across both privileged and unprivileged groups. Hovering over the plot reveals that only 10.95% of the females in the dataset belong to the favorable class, in contrast to the 30.57% of males. By dividing the percentage of the favorable class for the unprivileged group by that of the privileged group, we arrive at a disparate impact value of 0.358—precisely what the Dataset Bias widget determined. We can do a similar calculation for the statistical parity difference.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-18","draft":false,"title":"Orange Fairness - Dataset Bias","thumbImage":"2023-09-18-fairness-dataset-bias-thumb.png","frontPageImage":"2023-09-18-fairness-dataset-bias-thumb.png","blog":["fairness","dataset bias"],"shortExcerpt":"Orange now supports methods for detecting and mitigating bias in machine learning.","longExcerpt":"In an era where AI drives decisions impacting real lives, fairness in machine learning is paramount. Take the `Adult` dataset, which shows discrepancies in salary predictions based on gender. Addressing such concerns, Orange introduces a fairness add-on. Using new widgets, users can identify and mitigate biases in their datasets or model predictions.","oldUrl":"/blog/2023/2023-09-18-fairness-dataset-bias/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\",\n    h3: \"h3\",\n    h4: \"h4\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"Fairness in Machine Learning\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Artificial intelligence and machine learning are increasingly used in everyday decisions that deeply impact individuals. This includes areas like employment, court sentencing, and credit application approvals. It's crucial that these decision-making tools don't favor certain demographic groups over others.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Bias in machine learning can stem from multiple sources. It might arise from skewed training data or the model's design. This bias can result in unequal model outcomes: unprivileged groups might receive lower salaries despite having similar qualifications as privileged ones or face longer prison sentences for identical crimes. Such biases can significantly affect individuals' lives.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For example, in the \\\"Adult\\\" dataset, a notable bias exists between male and female genders. When predicting if a person's salary is \\\"\u003e 50K\\\" or \\\"≤ 50K\\\", females tend to be classified as \\\"≤ 50K\\\" more frequently than their male counterparts with similar attributes. This disparity showcases the dataset's bias, which can lead to biased and unfair predictions.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"But why is it a problem to have models that reflect real-world bias?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In essence, machine learning models that merely mimic real-world biases perpetuate those biases, resulting in a vicious cycle. For instance, if we were to train a model on a dataset reflecting gender wage disparities and then use this model to automate salary decisions, we would reinforce the same disparity. This is not about denying the reality of the dataset but recognizing that our goal in AI and machine learning should be to achieve fairer, more equitable outcomes. If models are only used to replicate existing societal conditions, then we miss the opportunity for technology to be a force for positive change.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Furthermore, creating models that actively seek to reduce or eliminate bias is essential for maintaining trust in AI and machine learning systems. When people see these systems merely replicating past biases, they are less likely to trust and adopt them. In contrast, fairness-aware models can be trusted to make decisions that prioritize justice and equity over merely echoing the patterns in the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Recognizing these challenges, there has been a surge in efforts to mitigate this bias. The outcome has been the development of models that aim for balanced outcomes for all demographic groups or maintain consistent true positive rates across them. Mitigating bias might mean a slight dip in overall accuracy, but it is a necessary trade-off to ensure fairness in our models. Using fairness algorithms is ethical and highly beneficial to ensure people's trust in machine learning and AI.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Introducing the Fairness add-on in Orange\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hopefully, the above has emphasized the importance of fairness in machine learning. With this in mind, let us discuss the new Orange fairness add-on. It aims to empower users with tools to detect and mitigate bias in machine-learning workflows. At the time of writing, the add-on includes seven widgets, four of which are used for bias detection and mitigation. The remaining three are used to support the other widgets and provide additional functionality.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this blog and the following ones, we will familiarize ourselves with the add-on widgets and their use cases. We will also demonstrate how to use them in real-world scenarios and explain relevant concepts. Let us take a look at the first two widgets in more detail.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Exploring the Widgets: Dataset Bias and As Fairness\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This blog will introduce the first two fairness widgets: As Fairness Data and Dataset Bias. Both aim to address the different bias identification and correction stages in machine learning workflows.\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"1. As Fairness Widget:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The As Fairness Widget does not do anything about fairness on its own. Instead, it allows users to designate fairness attributes in the dataset, which are essential for other fairness algorithms to operate.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When inputting a dataset into the widget, options for three attributes will appear:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Favorable Class Value: Define which class value is viewed as favorable.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Protected Attribute: Select the dataset feature representing or containing potentially biased groups or those for which fair predictions are desired, such as race, gender, or age.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Privileged Protected Attribute Values: Specify values within the protected attribute deemed privileged.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias-as-fairness-data.png\",\n      width: \"408\",\n      height: \"357\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-as-fairness-data.png\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"2. Dataset Bias Widget:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Before training a model, it is crucial to understand if the data itself is skewed. The Dataset Bias widget aids in precisely that. After giving it a dataset as input, the widget will calculate two fairness metrics according to the dataset:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Disparate Impact (DI): Measures the ratio of favorable outcomes for an unprivileged group to that of the privileged group. An ideal value of 1.0 means the ratio of favorable outcomes is the same for both groups.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"DI \u003c 1.0: The privileged group receives favorable outcomes at a higher rate.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"DI \u003e 1.0: The privileged group receives favorable outcomes at a lower rate.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Statistical Parity Difference (SPD): This is very similar to disparate impact. Instead of the ratio, it measures the difference in favorable outcomes between the unprivileged and the privileged groups. An ideal value for this metric is 0.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003c 0: The privileged group has a higher rate of favorable outcomes.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003e 0: The privileged group has a lower rate of favorable outcomes.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias.png\",\n      width: \"303\",\n      height: \"167\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For anyone interested in learning more about these metrics, other fairness metrics and ai fairness in general, we recommend reading the article \", _jsx(_components.a, {\n        href: \"https://arxiv.org/pdf/1908.09635.pdf\",\n        children: \"A Survey on Bias and Fairness in Machine Learning\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we understand the functions of the As Fairness and Dataset Bias widgets, let us examine their application in a real-world scenario.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this example, we will utilize the \", _jsx(_components.a, {\n        href: \"https://archive.ics.uci.edu/ml/datasets/adult\",\n        children: \"Adult dataset\"\n      }), \", which is among the most popular in the machine learning community. It consists of 48824 instances with 15 attributes describing demographic details from the 1994 census. These attributes include six numerical and seven categorical factors such as age, employment type, education, marital status, occupation, race, gender, capital gain, capital loss, weekly working hours, and native country. The primary task is to predict if an individual earns more than $50,000 annually. This dataset's most commonly protected attributes are gender, age, and race.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using the As Fairness Data widget, we will define \\\"sex\\\" as the protected attribute and \\\"male\\\" as the privileged, protected attribute value. Subsequently, we will employ the Dataset Bias widget to compute the fairness metrics and a Box Plot for visualization.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias-use-case.png\",\n      width: \"947\",\n      height: \"291\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-use-case.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results show that the dataset exhibits bias: the disparate impact is 0.358, and the statistical parity difference stands at -0.196. In an ideal scenario signifying no bias, these values would be 1 and 0, respectively. We can further visualize the dataset's bias using the Box Plot.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias-box-plot.png\",\n      width: \"1038\",\n      height: \"473\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-box-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Box Plot illustrates the distribution of the target variable across both privileged and unprivileged groups. Hovering over the plot reveals that only 10.95% of the females in the dataset belong to the favorable class, in contrast to the 30.57% of males. By dividing the percentage of the favorable class for the unprivileged group by that of the privileged group, we arrive at a disparate impact value of 0.358—precisely what the Dataset Bias widget determined. We can do a similar calculation for the statistical parity difference.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":562,"height":546,"src":"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-dataset-bias"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>The Box Plot illustrates the distribution of the target variable across both privileged and unprivileged groups. Hovering over the plot reveals that only 10.95% of the females in the dataset belong to the favorable class, in contrast to the 30.57% of males. By dividing the percentage of the favorable class for the unprivileged group by that of the privileged group, we arrive at a disparate impact value of 0.358—precisely what the Dataset Bias widget determined. We can do a similar calculation for the statistical parity difference.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-18","draft":false,"title":"Orange Fairness - Dataset Bias","thumbImage":"2023-09-18-fairness-dataset-bias-thumb.png","frontPageImage":"2023-09-18-fairness-dataset-bias-thumb.png","blog":["fairness","dataset bias"],"shortExcerpt":"Orange now supports methods for detecting and mitigating bias in machine learning.","longExcerpt":"In an era where AI drives decisions impacting real lives, fairness in machine learning is paramount. Take the `Adult` dataset, which shows discrepancies in salary predictions based on gender. Addressing such concerns, Orange introduces a fairness add-on. Using new widgets, users can identify and mitigate biases in their datasets or model predictions.","oldUrl":"/blog/2023/2023-09-18-fairness-dataset-bias/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\",\n    h3: \"h3\",\n    h4: \"h4\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"Fairness in Machine Learning\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Artificial intelligence and machine learning are increasingly used in everyday decisions that deeply impact individuals. This includes areas like employment, court sentencing, and credit application approvals. It's crucial that these decision-making tools don't favor certain demographic groups over others.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Bias in machine learning can stem from multiple sources. It might arise from skewed training data or the model's design. This bias can result in unequal model outcomes: unprivileged groups might receive lower salaries despite having similar qualifications as privileged ones or face longer prison sentences for identical crimes. Such biases can significantly affect individuals' lives.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For example, in the \\\"Adult\\\" dataset, a notable bias exists between male and female genders. When predicting if a person's salary is \\\"\u003e 50K\\\" or \\\"≤ 50K\\\", females tend to be classified as \\\"≤ 50K\\\" more frequently than their male counterparts with similar attributes. This disparity showcases the dataset's bias, which can lead to biased and unfair predictions.\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"But why is it a problem to have models that reflect real-world bias?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In essence, machine learning models that merely mimic real-world biases perpetuate those biases, resulting in a vicious cycle. For instance, if we were to train a model on a dataset reflecting gender wage disparities and then use this model to automate salary decisions, we would reinforce the same disparity. This is not about denying the reality of the dataset but recognizing that our goal in AI and machine learning should be to achieve fairer, more equitable outcomes. If models are only used to replicate existing societal conditions, then we miss the opportunity for technology to be a force for positive change.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Furthermore, creating models that actively seek to reduce or eliminate bias is essential for maintaining trust in AI and machine learning systems. When people see these systems merely replicating past biases, they are less likely to trust and adopt them. In contrast, fairness-aware models can be trusted to make decisions that prioritize justice and equity over merely echoing the patterns in the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Recognizing these challenges, there has been a surge in efforts to mitigate this bias. The outcome has been the development of models that aim for balanced outcomes for all demographic groups or maintain consistent true positive rates across them. Mitigating bias might mean a slight dip in overall accuracy, but it is a necessary trade-off to ensure fairness in our models. Using fairness algorithms is ethical and highly beneficial to ensure people's trust in machine learning and AI.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Introducing the Fairness add-on in Orange\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hopefully, the above has emphasized the importance of fairness in machine learning. With this in mind, let us discuss the new Orange fairness add-on. It aims to empower users with tools to detect and mitigate bias in machine-learning workflows. At the time of writing, the add-on includes seven widgets, four of which are used for bias detection and mitigation. The remaining three are used to support the other widgets and provide additional functionality.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this blog and the following ones, we will familiarize ourselves with the add-on widgets and their use cases. We will also demonstrate how to use them in real-world scenarios and explain relevant concepts. Let us take a look at the first two widgets in more detail.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Exploring the Widgets: Dataset Bias and As Fairness\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This blog will introduce the first two fairness widgets: As Fairness Data and Dataset Bias. Both aim to address the different bias identification and correction stages in machine learning workflows.\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"1. As Fairness Widget:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The As Fairness Widget does not do anything about fairness on its own. Instead, it allows users to designate fairness attributes in the dataset, which are essential for other fairness algorithms to operate.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When inputting a dataset into the widget, options for three attributes will appear:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Favorable Class Value: Define which class value is viewed as favorable.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Protected Attribute: Select the dataset feature representing or containing potentially biased groups or those for which fair predictions are desired, such as race, gender, or age.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Privileged Protected Attribute Values: Specify values within the protected attribute deemed privileged.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias-as-fairness-data.png\",\n      width: \"408\",\n      height: \"357\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-as-fairness-data.png\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"2. Dataset Bias Widget:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Before training a model, it is crucial to understand if the data itself is skewed. The Dataset Bias widget aids in precisely that. After giving it a dataset as input, the widget will calculate two fairness metrics according to the dataset:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Disparate Impact (DI): Measures the ratio of favorable outcomes for an unprivileged group to that of the privileged group. An ideal value of 1.0 means the ratio of favorable outcomes is the same for both groups.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"DI \u003c 1.0: The privileged group receives favorable outcomes at a higher rate.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"DI \u003e 1.0: The privileged group receives favorable outcomes at a lower rate.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Statistical Parity Difference (SPD): This is very similar to disparate impact. Instead of the ratio, it measures the difference in favorable outcomes between the unprivileged and the privileged groups. An ideal value for this metric is 0.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003c 0: The privileged group has a higher rate of favorable outcomes.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003e 0: The privileged group has a lower rate of favorable outcomes.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias.png\",\n      width: \"303\",\n      height: \"167\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For anyone interested in learning more about these metrics, other fairness metrics and ai fairness in general, we recommend reading the article \", _jsx(_components.a, {\n        href: \"https://arxiv.org/pdf/1908.09635.pdf\",\n        children: \"A Survey on Bias and Fairness in Machine Learning\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we understand the functions of the As Fairness and Dataset Bias widgets, let us examine their application in a real-world scenario.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this example, we will utilize the \", _jsx(_components.a, {\n        href: \"https://archive.ics.uci.edu/ml/datasets/adult\",\n        children: \"Adult dataset\"\n      }), \", which is among the most popular in the machine learning community. It consists of 48824 instances with 15 attributes describing demographic details from the 1994 census. These attributes include six numerical and seven categorical factors such as age, employment type, education, marital status, occupation, race, gender, capital gain, capital loss, weekly working hours, and native country. The primary task is to predict if an individual earns more than $50,000 annually. This dataset's most commonly protected attributes are gender, age, and race.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using the As Fairness Data widget, we will define \\\"sex\\\" as the protected attribute and \\\"male\\\" as the privileged, protected attribute value. Subsequently, we will employ the Dataset Bias widget to compute the fairness metrics and a Box Plot for visualization.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias-use-case.png\",\n      width: \"947\",\n      height: \"291\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-use-case.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results show that the dataset exhibits bias: the disparate impact is 0.358, and the statistical parity difference stands at -0.196. In an ideal scenario signifying no bias, these values would be 1 and 0, respectively. We can further visualize the dataset's bias using the Box Plot.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-dataset-bias/2023-09-18-fairness-dataset-bias-box-plot.png\",\n      width: \"1038\",\n      height: \"473\",\n      src: \"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-box-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Box Plot illustrates the distribution of the target variable across both privileged and unprivileged groups. Hovering over the plot reveals that only 10.95% of the females in the dataset belong to the favorable class, in contrast to the 30.57% of males. By dividing the percentage of the favorable class for the unprivileged group by that of the privileged group, we arrive at a disparate impact value of 0.358—precisely what the Dataset Bias widget determined. We can do a similar calculation for the statistical parity difference.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":562,"height":546,"src":"/blog/2023-09-fairness-dataset-bias/__optimized-images__/2023-09-18-fairness-dataset-bias-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-dataset-bias"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange Fairness - Equal Odds Postprocessing</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange Fairness - Equal Odds Postprocessing"/><meta property="og:description" content="Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model&#x27;s fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-09-19 02:00:00+00:00"/><meta property="article:author" content="Žan Mervič"/><meta property="article:tag" content="fairness"/><meta property="article:tag" content="equal odds postprocessing"/><meta property="og:image" content="/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"/><meta property="og:image:width" content="596"/><meta property="og:image:height" content="277"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>fairness, equal odds postprocessing</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Fairness - Equal Odds Postprocessing</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Žan Mervič</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 19, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the <a href="/blog/2023-09-19-fairness-adversarial-debiasing/">previous blog post</a>, we discussed the Adversarial Debiasing model, a bias-aware model. This blog post will discuss the Equal Odds Postprocessing widget, a bias-aware post-processor, which can be used with any model to mitigate bias in its predictions.</p>
 <h3>Equal Odds Postprocessing:</h3>
 <p>The <a href="https://arxiv.org/abs/1610.02413">Equal Odds Postprocessing</a> widget is a post-processing type of fairness mitigation algorithm for supervised learning. It modifies the predictions of any given classifier to meet certain fairness criteria, specifically focusing on &quot;Equalized Odds&quot; or more relaxed criteria like Equal Opportunity. Because it is a post-processing algorithm, it is versatile and can be used with most models, unlike some pre-processing or in-processing algorithms.</p>
@@ -181,4 +181,4 @@ <h2>Orange use case</h2>
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 <p>In the visualizations, each column&#x27;s red and blue parts represent the true positive and false negative rates, respectively, for each group. You can ignore the width of the columns as that represents the number of instances in each group, which is irrelevant to us.</p>
 <p>In the first visualization, which represents predictions from the model without debiasing, we can see that the privileged group (≥ 26) has a higher True Positive Rate than the unprivileged group (&lt; 26). This can be considered unfair towards the unprivileged group because it means a loan candidate from the unprivileged group is more likely to be falsely rejected, because of the higher false negative rate, than a loan candidate from the privileged group.</p>
-<p>In the second visualization, representing predictions from the model with debiasing, we can see that the True Positive Rate for the privileged group has decreased and is now almost equal to the True Positive Rate for the unprivileged group. While this means the model is now less accurate for the privileged group, it is as accurate as it is for the unprivileged group, which could be considered a fairer outcome.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 02:00:00+00:00","draft":false,"title":"Orange Fairness - Equal Odds Postprocessing","thumbImage":"2023-09-19-fairness-equal-odds-postprocessing.png","frontPageImage":"2023-09-19-fairness-equal-odds-postprocessing.png","blog":["fairness","equal odds postprocessing"],"shortExcerpt":"Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model's fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset.","longExcerpt":"In this blog, we delve into the Equal Odds Postprocessing widget, a tool designed to enhance fairness in machine learning models. We break down how the algorithm works by modifying predictions to meet Equalized Odds criteria. Using a real-world example with the German credit dataset, we demonstrate its efficacy in improving fairness metrics while marginally affecting accuracy.","oldUrl":"/blog/2023/2023-09-19-fairness-equal-odds-postprocessing/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h3: \"h3\",\n    code: \"code\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-adversarial-debiasing/\",\n        children: \"previous blog post\"\n      }), \", we discussed the Adversarial Debiasing model, a bias-aware model. This blog post will discuss the Equal Odds Postprocessing widget, a bias-aware post-processor, which can be used with any model to mitigate bias in its predictions.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Equal Odds Postprocessing:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1610.02413\",\n        children: \"Equal Odds Postprocessing\"\n      }), \" widget is a post-processing type of fairness mitigation algorithm for supervised learning. It modifies the predictions of any given classifier to meet certain fairness criteria, specifically focusing on \\\"Equalized Odds\\\" or more relaxed criteria like Equal Opportunity. Because it is a post-processing algorithm, it is versatile and can be used with most models, unlike some pre-processing or in-processing algorithms.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Equalized Odds Postprocessing widget introduces a unique functionality in the Orange environment. It works similarly to other Orange widgets representing models, except it also expects a learner as an input.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It works by first fitting the learner to the training data, creating a model, and using it to get the predictions. It then uses these predictions to fit the Equalized Odds Postprocessing algorithm from the \", _jsx(_components.a, {\n        href: \"https://aif360.res.ibm.com/\",\n        children: \"AIF360\"\n      }), \" library, which creates a post-processor. This post-processor is then used to adjust the model's predictions on the test data. The result is a model that has been adjusted to meet the fairness criteria of Equalized Odds.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Equalized Odds Postprocessing algorithm works by solving a linear program with some constraints and the following objective function:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.code, {\n        children: \"c = [fpr_0 - tpr_0, tnr_0 - fnr_0, fpr_1 - tpr_1, tnr_1 - fnr_1]\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Where \", _jsx(_components.code, {\n        children: \"fpr\"\n      }), \", \", _jsx(_components.code, {\n        children: \"tpr\"\n      }), \", \", _jsx(_components.code, {\n        children: \"tnr\"\n      }), \", and \", _jsx(_components.code, {\n        children: \"fnr\"\n      }), \" are the false positive, true positive, true negative, and false negative rates for privileged (\", _jsx(_components.code, {\n        children: \"0\"\n      }), \") and unprivileged (\", _jsx(_components.code, {\n        children: \"1\"\n      }), \") groups.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The algorithm then finds the optimal solution to the linear program, which results in a set of probabilities with which to flip the model's predictions to equalize the odds of being correctly or incorrectly classified for both privileged and unprivileged groups:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"sp2p\"\n        }), \": From positive to negative for the privileged group.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"sn2p\"\n        }), \": From negative to positive for the privileged group.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"op2p\"\n        }), \": From positive to negative for the unprivileged group.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"on2p\"\n        }), \": From negative to positive for the unprivileged group.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we know how the Equal Odds Postprocessing widget works and how to use it let us look at a real-world example for a classification task.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this example, we will use the \", _jsx(_components.a, {\n        href: \"http://archive.ics.uci.edu/dataset/144/statlog+german+credit+data\",\n        children: \"German credit dataset\"\n      }), \", which we have used \", _jsx(_components.a, {\n        href: \"/blog/2023/2023-08-25-fairness-reweighing-preprocessor/\",\n        children: \"before\"\n      }), \". The German Credit is a dataset related to bank loans. The main goal is to assess an individual's credit risk - whether a person is a good or bad credit risk. Unlike previously, we will not use the default fairness attributes that come with the dataset. Instead, we will use the age attribute, which we first discretize to two groups, younger and older than 26. We will set the older group as the privileged group.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will train two Logistic Regression models, one with and one without the Equal Odds Postprocessing widget, and compare the predictions of these models.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-use-case.png\",\n      width: \"957\",\n      height: \"475\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-use-case.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-scores.png\",\n      width: \"845\",\n      height: \"458\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results show that the model using the Equal Odds Postprocessing widget has better fairness metrics than the model without it. Equal Odds Difference and Average Odds Difference are the main objectives of the Equal Odds Postprocessing algorithm, and we can see that they are much closer to zero when using the widget. We can also see that the Disparate Impact and Statistical Parity Difference metrics are much closer to their ideal values, which is a beneficial side effect rather than a direct result of the algorithm's optimization process and is not guaranteed to happen in all cases.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can also see that the model's accuracy using the Equal Odds Postprocessing widget is slightly lower than without it. This is expected because the model is now not only trying to be accurate but also fair. This balance is a necessary trade-off we accept when we want to remove bias.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When using the Equal Odds Postprocessing, it is worth noting that the AUC value is not an accurate representation of the model's performance. This is because the Equal Odds Postprocessing algorithm changes only the predictions of the model, not the prediction probabilities. This means the AUC value would be calculated from the original model's prediction probabilities, not the adjusted ones. We have decided to alter the prediction probabilities when using the Equal Odds Postprocessing widget to 1 if the prediction is of the positive class and 0 otherwise. This is done to ensure that the AUC value is calculated from the adjusted predictions, which is a more accurate representation of the model's performance but still not a perfect one.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, let us look at the mosaic display widget. We will use it to show the True Positive Rate for each group and thus the Equal Opportunity Difference metric, which is the difference of the True Positive Rates between unprivileged and privileged groups. To do this, we first had to use the Select Rows widget to keep only the instances with a positive class because we are not interested in the True Negative Rate. This was the result:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-mosaic-bias.png\",\n      width: \"752\",\n      height: \"667\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-mosaic-bias.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-mosaic-debias.png\",\n      width: \"755\",\n      height: \"666\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-mosaic-debias.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the visualizations, each column's red and blue parts represent the true positive and false negative rates, respectively, for each group. You can ignore the width of the columns as that represents the number of instances in each group, which is irrelevant to us.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the first visualization, which represents predictions from the model without debiasing, we can see that the privileged group (≥ 26) has a higher True Positive Rate than the unprivileged group (\u003c 26). This can be considered unfair towards the unprivileged group because it means a loan candidate from the unprivileged group is more likely to be falsely rejected, because of the higher false negative rate, than a loan candidate from the privileged group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the second visualization, representing predictions from the model with debiasing, we can see that the True Positive Rate for the privileged group has decreased and is now almost equal to the True Positive Rate for the unprivileged group. While this means the model is now less accurate for the privileged group, it is as accurate as it is for the unprivileged group, which could be considered a fairer outcome.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":596,"height":277,"src":"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-equal-odds-postprocessing"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>In the second visualization, representing predictions from the model with debiasing, we can see that the True Positive Rate for the privileged group has decreased and is now almost equal to the True Positive Rate for the unprivileged group. While this means the model is now less accurate for the privileged group, it is as accurate as it is for the unprivileged group, which could be considered a fairer outcome.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 02:00:00+00:00","draft":false,"title":"Orange Fairness - Equal Odds Postprocessing","thumbImage":"2023-09-19-fairness-equal-odds-postprocessing.png","frontPageImage":"2023-09-19-fairness-equal-odds-postprocessing.png","blog":["fairness","equal odds postprocessing"],"shortExcerpt":"Explore the Equal Odds Postprocessing widget in Orange, designed to fine-tune your model's fairness. We explain how the algorithm operates and showcase its effectiveness with an example using the German credit dataset.","longExcerpt":"In this blog, we delve into the Equal Odds Postprocessing widget, a tool designed to enhance fairness in machine learning models. We break down how the algorithm works by modifying predictions to meet Equalized Odds criteria. Using a real-world example with the German credit dataset, we demonstrate its efficacy in improving fairness metrics while marginally affecting accuracy.","oldUrl":"/blog/2023/2023-09-19-fairness-equal-odds-postprocessing/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h3: \"h3\",\n    code: \"code\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-adversarial-debiasing/\",\n        children: \"previous blog post\"\n      }), \", we discussed the Adversarial Debiasing model, a bias-aware model. This blog post will discuss the Equal Odds Postprocessing widget, a bias-aware post-processor, which can be used with any model to mitigate bias in its predictions.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Equal Odds Postprocessing:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://arxiv.org/abs/1610.02413\",\n        children: \"Equal Odds Postprocessing\"\n      }), \" widget is a post-processing type of fairness mitigation algorithm for supervised learning. It modifies the predictions of any given classifier to meet certain fairness criteria, specifically focusing on \\\"Equalized Odds\\\" or more relaxed criteria like Equal Opportunity. Because it is a post-processing algorithm, it is versatile and can be used with most models, unlike some pre-processing or in-processing algorithms.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Equalized Odds Postprocessing widget introduces a unique functionality in the Orange environment. It works similarly to other Orange widgets representing models, except it also expects a learner as an input.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It works by first fitting the learner to the training data, creating a model, and using it to get the predictions. It then uses these predictions to fit the Equalized Odds Postprocessing algorithm from the \", _jsx(_components.a, {\n        href: \"https://aif360.res.ibm.com/\",\n        children: \"AIF360\"\n      }), \" library, which creates a post-processor. This post-processor is then used to adjust the model's predictions on the test data. The result is a model that has been adjusted to meet the fairness criteria of Equalized Odds.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Equalized Odds Postprocessing algorithm works by solving a linear program with some constraints and the following objective function:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.code, {\n        children: \"c = [fpr_0 - tpr_0, tnr_0 - fnr_0, fpr_1 - tpr_1, tnr_1 - fnr_1]\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Where \", _jsx(_components.code, {\n        children: \"fpr\"\n      }), \", \", _jsx(_components.code, {\n        children: \"tpr\"\n      }), \", \", _jsx(_components.code, {\n        children: \"tnr\"\n      }), \", and \", _jsx(_components.code, {\n        children: \"fnr\"\n      }), \" are the false positive, true positive, true negative, and false negative rates for privileged (\", _jsx(_components.code, {\n        children: \"0\"\n      }), \") and unprivileged (\", _jsx(_components.code, {\n        children: \"1\"\n      }), \") groups.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The algorithm then finds the optimal solution to the linear program, which results in a set of probabilities with which to flip the model's predictions to equalize the odds of being correctly or incorrectly classified for both privileged and unprivileged groups:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"sp2p\"\n        }), \": From positive to negative for the privileged group.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"sn2p\"\n        }), \": From negative to positive for the privileged group.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"op2p\"\n        }), \": From positive to negative for the unprivileged group.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.code, {\n          children: \"on2p\"\n        }), \": From negative to positive for the unprivileged group.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we know how the Equal Odds Postprocessing widget works and how to use it let us look at a real-world example for a classification task.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this example, we will use the \", _jsx(_components.a, {\n        href: \"http://archive.ics.uci.edu/dataset/144/statlog+german+credit+data\",\n        children: \"German credit dataset\"\n      }), \", which we have used \", _jsx(_components.a, {\n        href: \"/blog/2023/2023-08-25-fairness-reweighing-preprocessor/\",\n        children: \"before\"\n      }), \". The German Credit is a dataset related to bank loans. The main goal is to assess an individual's credit risk - whether a person is a good or bad credit risk. Unlike previously, we will not use the default fairness attributes that come with the dataset. Instead, we will use the age attribute, which we first discretize to two groups, younger and older than 26. We will set the older group as the privileged group.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will train two Logistic Regression models, one with and one without the Equal Odds Postprocessing widget, and compare the predictions of these models.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-use-case.png\",\n      width: \"957\",\n      height: \"475\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-use-case.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-scores.png\",\n      width: \"845\",\n      height: \"458\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results show that the model using the Equal Odds Postprocessing widget has better fairness metrics than the model without it. Equal Odds Difference and Average Odds Difference are the main objectives of the Equal Odds Postprocessing algorithm, and we can see that they are much closer to zero when using the widget. We can also see that the Disparate Impact and Statistical Parity Difference metrics are much closer to their ideal values, which is a beneficial side effect rather than a direct result of the algorithm's optimization process and is not guaranteed to happen in all cases.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can also see that the model's accuracy using the Equal Odds Postprocessing widget is slightly lower than without it. This is expected because the model is now not only trying to be accurate but also fair. This balance is a necessary trade-off we accept when we want to remove bias.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When using the Equal Odds Postprocessing, it is worth noting that the AUC value is not an accurate representation of the model's performance. This is because the Equal Odds Postprocessing algorithm changes only the predictions of the model, not the prediction probabilities. This means the AUC value would be calculated from the original model's prediction probabilities, not the adjusted ones. We have decided to alter the prediction probabilities when using the Equal Odds Postprocessing widget to 1 if the prediction is of the positive class and 0 otherwise. This is done to ensure that the AUC value is calculated from the adjusted predictions, which is a more accurate representation of the model's performance but still not a perfect one.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, let us look at the mosaic display widget. We will use it to show the True Positive Rate for each group and thus the Equal Opportunity Difference metric, which is the difference of the True Positive Rates between unprivileged and privileged groups. To do this, we first had to use the Select Rows widget to keep only the instances with a positive class because we are not interested in the True Negative Rate. This was the result:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-mosaic-bias.png\",\n      width: \"752\",\n      height: \"667\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-mosaic-bias.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/2023-09-19-fairness-equal-odds-postprocessing-mosaic-debias.png\",\n      width: \"755\",\n      height: \"666\",\n      src: \"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing-mosaic-debias.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the visualizations, each column's red and blue parts represent the true positive and false negative rates, respectively, for each group. You can ignore the width of the columns as that represents the number of instances in each group, which is irrelevant to us.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the first visualization, which represents predictions from the model without debiasing, we can see that the privileged group (≥ 26) has a higher True Positive Rate than the unprivileged group (\u003c 26). This can be considered unfair towards the unprivileged group because it means a loan candidate from the unprivileged group is more likely to be falsely rejected, because of the higher false negative rate, than a loan candidate from the privileged group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the second visualization, representing predictions from the model with debiasing, we can see that the True Positive Rate for the privileged group has decreased and is now almost equal to the True Positive Rate for the unprivileged group. While this means the model is now less accurate for the privileged group, it is as accurate as it is for the unprivileged group, which could be considered a fairer outcome.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":596,"height":277,"src":"/blog/2023-09-fairness-equal-odds-postprocessing/__optimized-images__/2023-09-19-fairness-equal-odds-postprocessing.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-equal-odds-postprocessing"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange Fairness - Reweighing a Dataset</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange Fairness - Reweighing a Dataset"/><meta property="og:description" content="Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-09-19 03:00:00+00:00"/><meta property="article:author" content="Žan Mervič"/><meta property="article:tag" content="fairness"/><meta property="article:tag" content="reweighing"/><meta property="og:image" content="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-thumb.png"/><meta property="og:image:width" content="678"/><meta property="og:image:height" content="455"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>fairness, reweighing</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Fairness - Reweighing a Dataset</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Žan Mervič</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 19, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the <a href="/blog/2023-09-18-fairness-dataset-bias/">previous blog post</a>, we introduced the Orange fairness addon along with the Dataset Bias and As Fairness widgets. We also demonstrated how to use them to detect bias in a dataset and visualized the results for a better understanding. In this blog, we will introduce the Reweighing widget, which we can use to mitigate bias in a dataset, resulting in fairer machine learning models learning from it.</p>
 <h3>Reweighing:</h3>
 <p>The Reweighing widget offers a solution to mitigate bias in datasets by assigning weights to individual instances. These weights are determined using the <a href="https://link.springer.com/article/10.1007/s10115-011-0463-8">Reweighing algorithm</a>. Essentially, the algorithm gives higher weights to underrepresented pairs of protected attributes and classes and reduces weights for overrepresented ones. This strategy encourages the model to prioritize learning from underrepresented groups while de-emphasizing overrepresented groups. In Orange, we can use the Reweighing widget in two ways:</p>
@@ -175,4 +175,4 @@ <h2>Orange use case</h2>
 <p>The box plot widget reveals that lower weights were given to instances of unprivileged groups with unfavorable class values and privileged groups with favorable class values. The opposite is true for the higher weights. The results show that the reweighing algorithm assigned weights to the instances in a way that will encourage the model to prioritize learning from underrepresented groups while de-emphasizing overrepresented groups.</p>
 <p>In the context of the dataset, the reweighing algorithm recognized that there were more instances with the race &quot;Caucasian&quot; and the favorable class value &quot;no&quot; than any other race and the opposite for the unfavorable class value &quot;yes&quot;. Because of this, it assigned the instances with the race &quot;Caucasian&quot; and the class value &quot;no&quot; a lower weight, encouraging the model to focus less on these instances, while giving the instances of other races with the class value &quot;no&quot; a higher weight, encouraging the model to focus more on these instances. The same is true for the class value &quot;yes&quot; but in the opposite direction.</p>
 <p>Another way to see the effects of using the Reweighing widget on a dataset is to use a Data Table widget, where we can see that a new meta attribute called weights has been added to the dataset. This attribute contains the weights assigned to each instance.</p>
-<a href="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-data-table.png" data-gallery="true"><img alt="" loading="lazy" width="838" height="450" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-data-table.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 03:00:00+00:00","draft":false,"title":"Orange Fairness - Reweighing a Dataset","thumbImage":"2023-09-19-fairness-reweighing-dataset-thumb.png","frontPageImage":"2023-09-19-fairness-reweighing-dataset-thumb.png","blog":["fairness","reweighing"],"shortExcerpt":"Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation.","longExcerpt":"Building on our exploration of the Orange fairness addon, this blog delves into the Reweighing widget. 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+<a href="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-data-table.png" data-gallery="true"><img alt="" loading="lazy" width="838" height="450" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-data-table.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 03:00:00+00:00","draft":false,"title":"Orange Fairness - Reweighing a Dataset","thumbImage":"2023-09-19-fairness-reweighing-dataset-thumb.png","frontPageImage":"2023-09-19-fairness-reweighing-dataset-thumb.png","blog":["fairness","reweighing"],"shortExcerpt":"Introducing the Reweighing widget in Orange, a solution for dataset bias mitigation.","longExcerpt":"Building on our exploration of the Orange fairness addon, this blog delves into the Reweighing widget. 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We also demonstrated how to use them to detect bias in a dataset and visualized the results for a better understanding. In this blog, we will introduce the Reweighing widget, which we can use to mitigate bias in a dataset, resulting in fairer machine learning models learning from it.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Reweighing:\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The Reweighing widget offers a solution to mitigate bias in datasets by assigning weights to individual instances. These weights are determined using the \", _jsx(_components.a, {\n        href: \"https://link.springer.com/article/10.1007/s10115-011-0463-8\",\n        children: \"Reweighing algorithm\"\n      }), \". Essentially, the algorithm gives higher weights to underrepresented pairs of protected attributes and classes and reduces weights for overrepresented ones. This strategy encourages the model to prioritize learning from underrepresented groups while de-emphasizing overrepresented groups. In Orange, we can use the Reweighing widget in two ways:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"Reweighing a dataset:\"\n          }), \" The widget can be used to reweigh a dataset, which we can then use to train a model. Simply connect your dataset to the widget, and the output will be a dataset with appropriately weighted instances.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"Reweighing as a preprocessor:\"\n          }), \" The widget can function as a preprocessor for a specific model. By connecting the Reweighing widget directly to your chosen model, the dataset is automatically reweighed prior to the training phase.\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we have grasped how the Reweighing widget functions and its applications, let us explore a real-world example of using it to assign weights to data.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this illustration, we will use the \", _jsx(_components.a, {\n        href: \"https://github.com/propublica/compas-analysis\",\n        children: \"Compas dataset\"\n      }), \". The COMPAS dataset, obtained from ProPublica, contains 7,214 cases with 52 attributes, of which 33 are categorical and 20 are numerical. The most frequently used protected attributes are race and gender. The main goal with this dataset is to predict the likelihood of a convict being re-arrested.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Contrary to the example in the previous blog, we will not use the As Fairness widget to select fairness attributes. This is because datasets with a fairness tag come with default fairness attributes. Specifically, for the Compas dataset, \\\"race\\\" is identified as the protected attribute, with \\\"Caucasian\\\" set as the privileged, protected attribute value.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-dataset/2023-09-19-fairness-reweighing-dataset-use-case.png\",\n      width: \"1126\",\n      height: \"533\",\n      src: \"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-use-case.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"From the results, we can see that after reweighing the dataset, bias decreased substantially, nearing zero. Let us start by highlighting instances with lower or higher weights in the distribution widget to understand how the reweighing process assigned weights. Following this, we will analyze which combinations of protected attributes and classes were allocated these distinct weights.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-dataset/2023-09-19-fairness-reweighing-dataset-distributions.png\",\n      width: \"987\",\n      height: \"558\",\n      src: \"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-distributions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the distributions widget we selected instances which have been assigned a weight lower than 1.0. Let's inspect the selected instances using a box plot widget.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-dataset/2023-09-19-fairness-reweighing-dataset-box-plot.png\",\n      width: \"1017\",\n      height: \"472\",\n      src: \"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-box-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The box plot widget reveals that lower weights were given to instances of unprivileged groups with unfavorable class values and privileged groups with favorable class values. The opposite is true for the higher weights. The results show that the reweighing algorithm assigned weights to the instances in a way that will encourage the model to prioritize learning from underrepresented groups while de-emphasizing overrepresented groups.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the context of the dataset, the reweighing algorithm recognized that there were more instances with the race \\\"Caucasian\\\" and the favorable class value \\\"no\\\" than any other race and the opposite for the unfavorable class value \\\"yes\\\". Because of this, it assigned the instances with the race \\\"Caucasian\\\" and the class value \\\"no\\\" a lower weight, encouraging the model to focus less on these instances, while giving the instances of other races with the class value \\\"no\\\" a higher weight, encouraging the model to focus more on these instances. The same is true for the class value \\\"yes\\\" but in the opposite direction.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Another way to see the effects of using the Reweighing widget on a dataset is to use a Data Table widget, where we can see that a new meta attribute called weights has been added to the dataset. This attribute contains the weights assigned to each instance.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-dataset/2023-09-19-fairness-reweighing-dataset-data-table.png\",\n      width: \"838\",\n      height: \"450\",\n      src: \"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-data-table.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":678,"height":455,"src":"/blog/2023-09-fairness-reweighing-dataset/__optimized-images__/2023-09-19-fairness-reweighing-dataset-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-reweighing-a-dataset"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>fairness, reweighing</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Fairness - Reweighing as a preprocessor</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Žan Mervič</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 19, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the <a href="/blog/2023-09-19-fairness-reweighing-dataset/">previous blog post</a>, we introduced the Orange fairness Reweighing widget and used it to reweigh a dataset. In this blog, we will explore another use case for the Reweighing widget: using it as a preprocessor for a specific model.</p>
 <h3>Fairness metrics:</h3>
 <p>With the fairness addon and widgets that come with it, we also introduced four bias scoring metrics which we can use to evaluate the fairness of model predictions. The metrics are:</p>
@@ -204,4 +204,4 @@ <h2>Orange use case</h2>
 <a href="/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-bias.png" data-gallery="true"><img alt="" loading="lazy" width="1208" height="520" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-bias.png"/></a>
 <a href="/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-debias.png" data-gallery="true"><img alt="" loading="lazy" width="1203" height="513" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-debias.png"/></a>
 <p>The first box plot shows the ratio of favorable and unfavorable outcomes for the unprivileged and privileged groups for predictions from the model without reweighing. The second box plot shows the same for the model with reweighing.</p>
-<p>We can see that when using reweighing the amount of favorable outcomes increased for the unprivileged group and decreased for the privileged group bringing the ratios closer between the two groups.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 04:00:00+00:00","draft":false,"title":"Orange Fairness - Reweighing as a preprocessor","thumbImage":"2023-09-19-fairness-reweighing-preprocessor-thumb.png","frontPageImage":"2023-09-19-fairness-reweighing-preprocessor-thumb.png","blog":["fairness","reweighing"],"shortExcerpt":"Expanding on the Orange fairness Reweighing widget: using it as a preprocessor and integrating new fairness scoring metrics.","longExcerpt":"Diving deeper into the Orange fairness Reweighing widget, we explore its use as a preprocessor for models. Discover the new widgets and fairness scoring metrics; all illustrated using the German credit dataset, supplemented with visual insights through box plots.","oldUrl":"/blog/2023/2023-09-19-fairness-reweighing-preprocessor/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-reweighing-dataset/\",\n        children: \"previous blog post\"\n      }), \", we introduced the Orange fairness Reweighing widget and used it to reweigh a dataset. In this blog, we will explore another use case for the Reweighing widget: using it as a preprocessor for a specific model.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Fairness metrics:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With the fairness addon and widgets that come with it, we also introduced four bias scoring metrics which we can use to evaluate the fairness of model predictions. The metrics are:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Disparate Impact (DI): The ratio of favorable outcomes for an unprivileged group to that of the privileged group. An ideal value of 1.0 means the ratio is the same for both groups.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"DI \u003c 1.0: The privileged group receives favorable outcomes at a higher rate than the unprivileged group.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"DI \u003e 1.0: The privileged group receives favorable outcomes at a lower rate than the unprivileged group.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Statistical Parity Difference (SPD): Very similar to disparate impact. Instead of the ratio, it measures the difference in favorable outcomes. An ideal value is 0.0.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003c 0: The privileged group has a higher rate of favorable outcomes.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003e 0: The privileged group has a lower rate of favorable outcomes.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Average Odds Difference (AOD): This metric calculates the average difference between the true positive rates (correctly predicting a positive outcome) and false positive rates (incorrectly predicting a positive outcome) for both the privileged and unprivileged groups. A value of 0.0 indicates equal rates for both groups, signifying fairness.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"AOD \u003c 0: Indicates bias in favor of the privileged group.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"AOD \u003e 0: Indicates bias against the privileged group.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Equal Opportunity Difference (EOD): Similar to the Average Odds Difference. It measures the difference in true positive rates. An ideal value is 0.0, indicating the difference in true positive rates is the same for both groups.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"EOD \u003c 0: The privileged group has a higher true positive rate.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"EOD \u003e 0: The privileged group has a lower true positive rate.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Weighted Logistic Regression and Combine Preprocessors:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At the time of writing, there were two limitations in using the Reweighing widget as a preprocessor:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Currently, model widgets in Orange do not account for instance weights when training.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model widgets only accept one preprocessor widget as an input; this usually would be fine since all preprocessors can be added from a single widget called Preprocess, but Reweighing is a separate widget and thus could not be combined with other methods.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To combat this, we introduced two new widgets which we will use in the example below:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Weighted Logistic Regression is an adapted form of the Logistic Regression widget designed to handle weighted datasets.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Combine Preprocessors, which enables users to merge multiple preprocessors into one before inputting it into a widget. This becomes handy when integrating preprocessors other than Reweighing with a model.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we know all the required widgets and new fairness scoring metrics, let us explore a real-world example of using the reweighing widget as a preprocessor. Doing so will allow us to use the reweighing algorithm when evaluating model performance using cross-validation, random sampling, etc.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use the \", _jsx(_components.a, {\n        href: \"http://archive.ics.uci.edu/dataset/144/statlog+german+credit+data\",\n        children: \"German credit dataset\"\n      }), \" for this illustration. The German Credit is a dataset related to bank loans. The main goal is to assess an individual's credit risk - whether a person is a good or bad credit risk. The collection consists of 1,000 cases with 20 attributes, of which 13 are categorical and 7 are numerical. The attributes include financial characteristics such as the amount and purpose of the loan, employment status, and credit history, as well as personal features like age, gender, housing status, and foreign worker status. The most frequently used protected attributes in this collection are gender and age.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Like the previous blog, we will not use the As Fairness Data widget to select fairness attributes. Instead, we will use the default attributes, \\\"sex\\\" as the protected attribute and \\\"male\\\" as the privileged group, already defined in the dataset.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-use-case.png\",\n      width: \"1037\",\n      height: \"467\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-use-case.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-scores.png\",\n      width: \"1217\",\n      height: \"471\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results show that the model using reweighing as a preprocessor achieved better fairness scores than the model without reweighing at the cost of slightly lower accuracy. 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For a clearer visualization, we have used the Edit Domain widget to merge all values of each protected group into one, which resulted in two groups: males and females.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-box-plot-bias.png\",\n      width: \"1208\",\n      height: \"520\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-bias.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-box-plot-debias.png\",\n      width: \"1203\",\n      height: \"513\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-debias.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first box plot shows the ratio of favorable and unfavorable outcomes for the unprivileged and privileged groups for predictions from the model without reweighing. The second box plot shows the same for the model with reweighing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that when using reweighing the amount of favorable outcomes increased for the unprivileged group and decreased for the privileged group bringing the ratios closer between the two groups.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":856,"height":500,"src":"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-thumb.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-fairness-reweighing-as-a-preprocessor"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>We can see that when using reweighing the amount of favorable outcomes increased for the unprivileged group and decreased for the privileged group bringing the ratios closer between the two groups.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 04:00:00+00:00","draft":false,"title":"Orange Fairness - Reweighing as a preprocessor","thumbImage":"2023-09-19-fairness-reweighing-preprocessor-thumb.png","frontPageImage":"2023-09-19-fairness-reweighing-preprocessor-thumb.png","blog":["fairness","reweighing"],"shortExcerpt":"Expanding on the Orange fairness Reweighing widget: using it as a preprocessor and integrating new fairness scoring metrics.","longExcerpt":"Diving deeper into the Orange fairness Reweighing widget, we explore its use as a preprocessor for models. Discover the new widgets and fairness scoring metrics; all illustrated using the German credit dataset, supplemented with visual insights through box plots.","oldUrl":"/blog/2023/2023-09-19-fairness-reweighing-preprocessor/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-reweighing-dataset/\",\n        children: \"previous blog post\"\n      }), \", we introduced the Orange fairness Reweighing widget and used it to reweigh a dataset. In this blog, we will explore another use case for the Reweighing widget: using it as a preprocessor for a specific model.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Fairness metrics:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With the fairness addon and widgets that come with it, we also introduced four bias scoring metrics which we can use to evaluate the fairness of model predictions. The metrics are:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Disparate Impact (DI): The ratio of favorable outcomes for an unprivileged group to that of the privileged group. An ideal value of 1.0 means the ratio is the same for both groups.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"DI \u003c 1.0: The privileged group receives favorable outcomes at a higher rate than the unprivileged group.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"DI \u003e 1.0: The privileged group receives favorable outcomes at a lower rate than the unprivileged group.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Statistical Parity Difference (SPD): Very similar to disparate impact. Instead of the ratio, it measures the difference in favorable outcomes. An ideal value is 0.0.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003c 0: The privileged group has a higher rate of favorable outcomes.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"SPD \u003e 0: The privileged group has a lower rate of favorable outcomes.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Average Odds Difference (AOD): This metric calculates the average difference between the true positive rates (correctly predicting a positive outcome) and false positive rates (incorrectly predicting a positive outcome) for both the privileged and unprivileged groups. A value of 0.0 indicates equal rates for both groups, signifying fairness.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"AOD \u003c 0: Indicates bias in favor of the privileged group.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"AOD \u003e 0: Indicates bias against the privileged group.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Equal Opportunity Difference (EOD): Similar to the Average Odds Difference. It measures the difference in true positive rates. An ideal value is 0.0, indicating the difference in true positive rates is the same for both groups.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"EOD \u003c 0: The privileged group has a higher true positive rate.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"EOD \u003e 0: The privileged group has a lower true positive rate.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Weighted Logistic Regression and Combine Preprocessors:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At the time of writing, there were two limitations in using the Reweighing widget as a preprocessor:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Currently, model widgets in Orange do not account for instance weights when training.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model widgets only accept one preprocessor widget as an input; this usually would be fine since all preprocessors can be added from a single widget called Preprocess, but Reweighing is a separate widget and thus could not be combined with other methods.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To combat this, we introduced two new widgets which we will use in the example below:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Weighted Logistic Regression is an adapted form of the Logistic Regression widget designed to handle weighted datasets.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Combine Preprocessors, which enables users to merge multiple preprocessors into one before inputting it into a widget. This becomes handy when integrating preprocessors other than Reweighing with a model.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Orange use case\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now that we know all the required widgets and new fairness scoring metrics, let us explore a real-world example of using the reweighing widget as a preprocessor. Doing so will allow us to use the reweighing algorithm when evaluating model performance using cross-validation, random sampling, etc.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use the \", _jsx(_components.a, {\n        href: \"http://archive.ics.uci.edu/dataset/144/statlog+german+credit+data\",\n        children: \"German credit dataset\"\n      }), \" for this illustration. The German Credit is a dataset related to bank loans. The main goal is to assess an individual's credit risk - whether a person is a good or bad credit risk. 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Instead, we will use the default attributes, \\\"sex\\\" as the protected attribute and \\\"male\\\" as the privileged group, already defined in the dataset.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-use-case.png\",\n      width: \"1037\",\n      height: \"467\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-use-case.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-scores.png\",\n      width: \"1217\",\n      height: \"471\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The results show that the model using reweighing as a preprocessor achieved better fairness scores than the model without reweighing at the cost of slightly lower accuracy. The Reweighing algorithm mainly focuses on the Disparate Impact and Statistical Parity Difference metrics, which reflect the ratio and difference between the favorable outcomes of the unprivileged and privileged groups.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this case the ratio of favorable outcomes went from 0.869 to 0.952, while the difference went from -0.105 to -0.038. Both metrics indicate less bias towards the unprivileged group.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can easily visualize the results using a box plot. For a clearer visualization, we have used the Edit Domain widget to merge all values of each protected group into one, which resulted in two groups: males and females.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-box-plot-bias.png\",\n      width: \"1208\",\n      height: \"520\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-bias.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/2023-09-19-fairness-reweighing-preprocessor-box-plot-debias.png\",\n      width: \"1203\",\n      height: \"513\",\n      src: \"/blog/2023-09-fairness-reweighing-preprocessor/__optimized-images__/2023-09-19-fairness-reweighing-preprocessor-box-plot-debias.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first box plot shows the ratio of favorable and unfavorable outcomes for the unprivileged and privileged groups for predictions from the model without reweighing. 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 <p>But there’s more. We are currently hosting an expert on data fusion from the University of Leuven, prof. dr. Yves Moreau, to discuss new venues and niches for the development of Orange. The big debate is how to scale the program to fit large data sets and make it possible to process such sets in a shorter period of time. If you have any ideas and suggestions, please feel free to share them on our <a href="https://stackoverflow.com/questions/tagged/orange">community forum</a>.</p>
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 <p>prof. dr. Yves Moreau - Prioritization of candidate disease genes and drug—target interactions by genomic data fusion</p>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange GSoC: A Fully-Featured Neural Network Library Implementation with Extension for Deep Learning</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange GSoC: A Fully-Featured Neural Network Library Implementation with Extension for Deep Learning"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2012-05-06 09:24:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="neuralnetwork"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, neuralnetwork</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange GSoC: A Fully-Featured Neural Network Library Implementation with Extension for Deep Learning</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">May 06, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This project aims to build a neural network library based on some great existing NN libraries, notably the Flood Library, which already provides a fully functional Multilayer Perceptron (MLP) implementation. The project starts with implementing a robust, efficient feed forward neural network library, and then will extend it in significant ways that add support for state-of-the-art deep learning techniques. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>computervision, gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange GSoC: Computer vision add-on for Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">May 15, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This summer I got the chance to develop an add-on for Orange that will introduce basic computer vision functionality, as a part of <a href="http://www.google-melange.com/gsoc/homepage/google/gsoc2012">Google Summer of Code</a>.</p>
 <p>The add-on will consist of a set of widgets, each with it&#x27;s own dedicated purpose, which can be seamlessly connected to provide most commonly used image preprocessing functionality.</p>
 <p>Here is a list of the widgets:</p>
@@ -164,4 +164,4 @@
 <p>Also, if there is enough time left throughout the GSoC period, a face detection widget will be built in order to demonstrate the power of the underlying libraries.</p>
 <p>These are all things that have been implemented in Python before. Reimplementing them is of course a rather bad idea, so I will use an library called <a href="http://opencv.willowgarage.com/wiki/">OpenCV</a>. It is written in C++ and has Python bindings, and is the most widely used computer vision library, by far. So the core of the widgets will be written in it, and the GUI using <a href="http://www.riverbankcomputing.co.uk/software/pyqt/intro">PyQT</a>, the library used for building the Orange Canvas.</p>
 <p>Although working with images is not Oranges&#x27; main thing, the knowledge gathered while developing the add-on will be used to improve in a number of ways: finding a general structure for add-ons developed in the future, improving the way they are distributed and the way they are tested.</p>
-<p>Finally, I want to thank the Orange core team for having faith in me and giving me the chance to spend the summer working on an idea I care about. I&#x27;m very grateful for that and I hope I&#x27;ll exceed their expectations.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-05-15 06:00:00+00:00","draft":false,"title":"Orange GSoC: Computer vision add-on for Orange","blog":["computervision","gsoc"],"oldUrl":"/blog/2012/05/15/orange-gsoc-computer-vision-add-on-for-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This summer I got the chance to develop an add-on for Orange that will introduce basic computer vision functionality, as a part of \", _jsx(_components.a, {\n        href: \"http://www.google-melange.com/gsoc/homepage/google/gsoc2012\",\n        children: \"Google Summer of Code\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The add-on will consist of a set of widgets, each with it's own dedicated purpose, which can be seamlessly connected to provide most commonly used image preprocessing functionality.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here is a list of the widgets:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Widget for viewing image files (add description)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for resizing an image\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for rotation/flipping of the image\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for converting the color mode (RGB, HSV, Grayscale etc.)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for changing the hue/saturation, brightness/contrast and inverting the image\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for generic transformations through convolution with a matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Also, if there is enough time left throughout the GSoC period, a face detection widget will be built in order to demonstrate the power of the underlying libraries.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"These are all things that have been implemented in Python before. 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+<p>Finally, I want to thank the Orange core team for having faith in me and giving me the chance to spend the summer working on an idea I care about. I&#x27;m very grateful for that and I hope I&#x27;ll exceed their expectations.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-05-15 06:00:00+00:00","draft":false,"title":"Orange GSoC: Computer vision add-on for Orange","blog":["computervision","gsoc"],"oldUrl":"/blog/2012/05/15/orange-gsoc-computer-vision-add-on-for-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This summer I got the chance to develop an add-on for Orange that will introduce basic computer vision functionality, as a part of \", _jsx(_components.a, {\n        href: \"http://www.google-melange.com/gsoc/homepage/google/gsoc2012\",\n        children: \"Google Summer of Code\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The add-on will consist of a set of widgets, each with it's own dedicated purpose, which can be seamlessly connected to provide most commonly used image preprocessing functionality.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here is a list of the widgets:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Widget for viewing image files (add description)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for resizing an image\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for rotation/flipping of the image\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for converting the color mode (RGB, HSV, Grayscale etc.)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for changing the hue/saturation, brightness/contrast and inverting the image\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Widget for generic transformations through convolution with a matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Also, if there is enough time left throughout the GSoC period, a face detection widget will be built in order to demonstrate the power of the underlying libraries.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"These are all things that have been implemented in Python before. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange GSoC: MF Techniques for Data Mining</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange GSoC: MF Techniques for Data Mining"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-06-24 22:10:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="matrixfactorization"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, matrixfactorization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange GSoC: MF Techniques for Data Mining</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 24, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>I am one of three students who are working on GSoC projects for Orange this year. The objective of the project Matrix Factorization Techniques for Data Mining is to provide the Orange community with a unified and efficient interface to matrix factorization algorithms and methods.</p>
 <p>For that purpose I have been developing a library which will include a number of published factorization algorithms and initialization methods and will facilitate combinations of these to produce new strategies. Extensive documentation with working examples that demonstrate applications, commonly used benchmark data and possibly some visualization methods will be provided to help with the interpretation and comprehension of the factorization results.</p>
 <p>Main factorization techniques and their variations included in the library are:</p>
@@ -162,4 +162,4 @@
 <p>Different multiplicative and update algorithms for NMF will be analyzed which minimize least-squares error or generalized Kullback-Leibler divergence.</p>
 <p>For those interested some more information with details about algorithms is available at <a href="http://orange.biolab.si/trac/intertrac/wiki%3AMatrixFactorization">project home page.</a></p>
 <p>There is still much work to do but I have been enjoying at it and I am looking forward to continuing with the project.</p>
-<p>Thanks to the Orange team and mentor prof. dr. Blaz Zupan for support and advice.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-06-24 22:10:00+00:00","draft":false,"title":"Orange GSoC: MF Techniques for Data Mining","blog":["gsoc","matrixfactorization"],"oldUrl":"/blog/2011/06/24/orange-gsoc-mf-techniques-for-data-mining/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"I am one of three students who are working on GSoC projects for Orange this year. 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+<p>Thanks to the Orange team and mentor prof. dr. Blaz Zupan for support and advice.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-06-24 22:10:00+00:00","draft":false,"title":"Orange GSoC: MF Techniques for Data Mining","blog":["gsoc","matrixfactorization"],"oldUrl":"/blog/2011/06/24/orange-gsoc-mf-techniques-for-data-mining/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"I am one of three students who are working on GSoC projects for Orange this year. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, multilabel</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange GSoC: Multi-label Classification Implementation</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 20, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Multi-label classification is one of the three projects of Google Summer Code 2011 for Orange. The main goal is to extend the Orange to support multi-label, including dataset support, two basic multi-label classifications-problem-transformation methods &amp; algorithm adaptation methods, evaluation measures, GUI support, documentation, testing, and so on.</p>
 <p>My name is Wencan Luo, from China. I&#x27;m very happy to work with my mentor Matija. Until now, we have finished a framework for multi-label support for Orange.</p>
 <p>To support multi-label data structure, a special value is added into their &#x27;attributes&#x27; dictionary. In this way, we can know whether the attribute is a type of class without altering the old Example Table class.</p>
@@ -163,4 +163,4 @@
 <li>ranking-based evaluator</li>
 <li>widgets to support the above methods</li>
 <li>testing</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-07-20 11:35:00+00:00","draft":false,"title":"Orange GSoC: Multi-label Classification Implementation","blog":["gsoc","multilabel"],"oldUrl":"/blog/2011/07/20/orange-gsoc-multi-label-classification-implementation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Multi-label classification is one of the three projects of Google Summer Code 2011 for Orange. 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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-07-20 11:35:00+00:00","draft":false,"title":"Orange GSoC: Multi-label Classification Implementation","blog":["gsoc","multilabel"],"oldUrl":"/blog/2011/07/20/orange-gsoc-multi-label-classification-implementation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Multi-label classification is one of the three projects of Google Summer Code 2011 for Orange. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, gsoc, multitarget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange GSoC: Multi-Target Learning for Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 30, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange already supports multi-target classification, but the current implementation of clustering trees is written in Python. One of the five projects Orange has chosen at this year&#x27;s Google Summer of Code is the implementation of clustering trees in C. The goal of my project is to speed up the building time of clustering trees and lower their spatial complexity, especially when used in random forests. Implementation will be based on Orange&#x27;s SimpleTreeLearner and will be integrated with Orange 3.0.</p>
 <p>Once the clustering trees are implemented and integrated, documentation and unit tests will be written. Additionally I intend to make an experimental study that will compare the effectiveness of clustering trees with established multi-target classifiers (like PLS and chain classifiers) on benchmark data-sets. I will also work on some additional tasks related to multi-target classification that I had not included in my original proposal but Orange&#x27;s team thinks would be useful to include. Among these is a chain classifier framework that Orange is currently missing.</p>
 <p>If any reader is interested in learning more about clustering trees or chain classifiers these articles should cover the basics:</p>
@@ -157,4 +157,4 @@
 <li><a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.3353">Top-Down Induction of Clustering Trees (1998), by Hendrik Blockeel, Luc De Raedt, Jan Ramong</a></li>
 <li><a href="http://www.cs.waikato.ac.nz/~eibe/pubs/chains.pdf">Classifier Chains for Multi-label Classification (2009), by Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank</a></li>
 </ul>
-<p>I am a third year undergraduate student at the Faculty of Computer and Information Science in Ljubljana and my project will be mentored by prof. dr. Blaž Zupan. I thank him and the rest of the Orange team for advice and support.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-04-30 12:00:00+00:00","draft":false,"title":"Orange GSoC: Multi-Target Learning for Orange","blog":["classification","gsoc","multitarget"],"oldUrl":"/blog/2012/04/30/orange-gsoc-multi-target-learning-for-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange already supports multi-target classification, but the current implementation of clustering trees is written in Python. 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+<p>I am a third year undergraduate student at the Faculty of Computer and Information Science in Ljubljana and my project will be mentored by prof. dr. Blaž Zupan. 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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange GSoC: Visualizations with Qt</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange GSoC: Visualizations with Qt"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-06-30 12:55:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange GSoC: Visualizations with Qt</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 30, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Hello, my name is Miha Čančula and this summer I&#x27;m working on Orange as part of Google&#x27;s Summer of Code program, mentored by Miha Štajdohar. My task is to replace the current visualization framework based on Qwt with a custom library, depending only on Qt. This library will better support Orange&#x27;s very specific visualizations and will replace the unmaintained PyQwt.</p>
 <p>I have a lot of experience with Qt and its graphics classes, both in C++ and Python, but I&#x27;m relatively now to Orange. As a physics student, especially now that I&#x27;m selecting a computational physics program, this a great learning opportunity for me.</p>
 <p>I think my work is progressing very well, because many visualizations already work with the new library with only minor modifications:</p>
@@ -163,4 +163,4 @@
 <p>Graphs made by Qwt are not very flexible, they only support graphs with cartesian axes. On the other hand, visualization Orange often use custom axes and transformations. That&#x27;s why I designed the new library with support for arbitrary axes, curves and other elements. All of these can be extendeng with classes written in Python, C++, or a combination thereof. The required changes to visualizations I already ported to the new OWGraph class were mostly simplifications, with very little new code added.</p>
 <p>For example, zooming and selection is implemented completely in the new OWGraph class, with the same function and attribute names as before, so visualizations themselves didn&#x27;t need any changes at all.</p>
 <p>The new framework is also able to produce much nicer graphs. I haven&#x27;t had the time to customize the looks much, but it&#x27;s possible to set colors, line widths, point sizes and symbols from Python. There are still some settings that have no UI configuration, but I will focus on that after making sure that visualization widgets work with the new library.</p>
-<p>Currently I&#x27;m trying to change as many widgets as possible to the new classes. As I said above, I only completed a few of them, but I believe the others won&#x27;t require too much work.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-06-30 12:55:00+00:00","draft":false,"title":"Orange GSoC: Visualizations with Qt","blog":["gsoc","visualization"],"oldUrl":"/blog/2011/06/30/orange-gsoc-visualizations-with-qt/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Hello, my name is Miha Čančula and this summer I'm working on Orange as part of Google's Summer of Code program, mentored by Miha Štajdohar. 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\ No newline at end of file
+<p>Currently I&#x27;m trying to change as many widgets as possible to the new classes. As I said above, I only completed a few of them, but I believe the others won&#x27;t require too much work.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-06-30 12:55:00+00:00","draft":false,"title":"Orange GSoC: Visualizations with Qt","blog":["gsoc","visualization"],"oldUrl":"/blog/2011/06/30/orange-gsoc-visualizations-with-qt/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Hello, my name is Miha Čančula and this summer I'm working on Orange as part of Google's Summer of Code program, mentored by Miha Štajdohar. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange has been accepted into GSoC 2011</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 18, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This year Orange has been accepted into the <a href="http://socghop.appspot.com/gsoc/homepage/google/gsoc2011">Google summer of Code</a> program as a mentoring organization. It is one of <a href="http://socghop.appspot.com/gsoc/accepted_orgs/google/gsoc2011">175 open-source organizations/projects/groups</a> which will this year mentor students while they will be working on those accepted open source projects.</p>
 <p>We have prepared <a href="http://orange.biolab.si/trac/intertrac/wiki%3AGSoC">a page on our Trac</a> with more information about the Google Summer of Code program, especially how the interested students should apply with their proposals. There is also a list of <a href="http://orange.biolab.si/trac/intertrac/wiki%3AGSoC/Ideas">of some ideas we are proposing</a> for this year. Check out also <a href="http://socghop.appspot.com/gsoc/org/google/gsoc2011/orange">official project page</a> for Orange.</p>
-<p>Google Summer of Code is a <a href="http://www.google.com/">Google</a>-sponsored program where Google stipends students working for a summer job on an open source projects from all around the world. Student is paid $5000 (and a t-shirt!) for approximately two months of work/contribution to the project. More about the program is available on <a href="http://socghop.appspot.com/">its homepage</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-03-18 21:10:00+00:00","draft":false,"title":"Orange has been accepted into GSoC 2011","blog":["gsoc"],"oldUrl":"/blog/2011/03/18/orange-has-been-accepted-into-gsoc-2011/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This year Orange has been accepted into the \", _jsx(_components.a, {\n        href: \"http://socghop.appspot.com/gsoc/homepage/google/gsoc2011\",\n        children: \"Google summer of Code\"\n      }), \" program as a mentoring organization. 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+<p>Google Summer of Code is a <a href="http://www.google.com/">Google</a>-sponsored program where Google stipends students working for a summer job on an open source projects from all around the world. Student is paid $5000 (and a t-shirt!) for approximately two months of work/contribution to the project. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>workshop, addons</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in a synchrotron</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Marko Toplak</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 14, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange has a cousin. His name is <a href="https://quasar.codes/">Quasar</a>. Quasar extends Orange with methods for the analysis of spectral data and allows researchers to use a single interactive workflow-based tool for acquisition method-specific data processing, machine learning, and visualization. Quasar was born from a collaboration between the University of Ljubljana and two synchrotrons, Soleil (France) and Elettra (Italy). Users from synchrotrons form an important group of Quasar users.</p>
 <p>A <a href="https://en.wikipedia.org/wiki/Synchrotron_light_source">synchrotron light source</a> is a particle accelerator where the beam travels in a closed loop, and wherever it bends, it generates electromagnetic radiation (or light). This light is captured on the beamlines, which can, for example, use infrared light to learn about the chemical composition of the samples or x-rays to learn about the samples&#x27; internal structure. The synchrotron light has a very tight beam and is very strong, which makes synchrotrons a very flexible light source. I sometimes imagine them as very big but high-quality light bulbs. A small section of the Diamond synchrotron&#x27;s storage ring, where the beam travels around a 560-meter-long loop, is shown below.</p>
 <a href="/blog/2022-11-orange-in-a-syncrotron/__optimized-images__/20221101_diamond-storage.jpg" data-gallery="true"><img alt="" loading="lazy" width="1200" height="796" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2022-11-orange-in-a-syncrotron/__optimized-images__/20221101_diamond-storage.jpg"/></a>
@@ -159,4 +159,4 @@
 <a href="/blog/2022-11-orange-in-a-syncrotron/__optimized-images__/20220103_different-classes-hair.png" data-gallery="true"><img alt="" loading="lazy" width="889" height="360" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2022-11-orange-in-a-syncrotron/__optimized-images__/20220103_different-classes-hair.png"/></a>
 <p>The above workflow demonstrates that even a (very) slight bias added to random classes introduces enough signal into the data so that the classes become perfectly separable. The added bias was very small, similar in size to mixing solutions of slightly different concentrations when preparing samples for measurement. We hope that we managed to show our students how easy it is to make an error that can invalidate our analysis.</p>
 <p>I also learned two important facts about England on this trip: (1) pubs are the places to go for dinner, and, (2) English food tastes good.</p>
-<a href="/blog/2022-11-orange-in-a-syncrotron/__optimized-images__/20221103_pub.jpg" data-gallery="true"><img alt="" loading="lazy" width="1200" height="796" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2022-11-orange-in-a-syncrotron/__optimized-images__/20221103_pub.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Marko Toplak","date":"2022-11-14","draft":false,"title":"Orange in a synchrotron","thumbImage":"20221101_inside.jpg","frontPageImage":"20221101_inside_s.jpg","blog":["workshop","addons"],"shortExcerpt":"Workshop about spectral data analysis in a synchrotron.","longExcerpt":"We taught a three-day workshop on Orange for spectral data in UK’s national synchrotron, Diamond Light Source.","oldUrl":"/blog/2022/2022-11-14-orange-in-a-syncrotron/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange has a cousin. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange, education, teaching, university</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in Classroom, pt. 2</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 14, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>A year ago, we put out a survey asking the Orange community if they use Orange for teaching (or learning) and at which educational institution. We got around 300 replies from 200 universities in the first round.</p>
 <p>In the past year, we were starting to establish a global community of educators that teach statistics, data mining, and machine learning (or perhaps something entirely different) and are using Orange for this purpose. Hence we continued gathering the data. We got 417 replies from 305 universities and educational institutions in 76 countries in the second round! That is 39% of the world!</p>
 <p>We once again thank everyone for your invaluable support!</p>
@@ -465,4 +465,4 @@
 <li><a href="https://www.yarsi.ac.id/">YARSI University</a>, Indonesia</li>
 <li><a href="http://english.uny.ac.id/">Yogyakarta State University</a>, Indonesia</li>
 <li><a href="https://www.fh-zwickau.de/english/">Zwickau University of Applied Sciences</a>, Germany</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2022-01-14","draft":false,"title":"Orange in Classroom, pt. 2","thumbImage":"2022-01-14-universities-small.png","frontPageImage":"2022-01-14-universities-small.png","blog":["orange","education","teaching","university"],"shortExcerpt":"Orange is being used in over 300 universities around the world.","longExcerpt":"Orange is being used inside and outside the classroom, by professors and students in over 300 universities around the world.","x2images":false,"oldUrl":"/blog/2022/2022-01-14-universities/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"A year ago, we put out a survey asking the Orange community if they use Orange for teaching (or learning) and at which educational institution. 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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2022-01-14","draft":false,"title":"Orange in Classroom, pt. 2","thumbImage":"2022-01-14-universities-small.png","frontPageImage":"2022-01-14-universities-small.png","blog":["orange","education","teaching","university"],"shortExcerpt":"Orange is being used in over 300 universities around the world.","longExcerpt":"Orange is being used inside and outside the classroom, by professors and students in over 300 universities around the world.","x2images":false,"oldUrl":"/blog/2022/2022-01-14-universities/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"A year ago, we put out a survey asking the Orange community if they use Orange for teaching (or learning) and at which educational institution. 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diff --git a/blog/orange-in-classroom/index.html b/blog/orange-in-classroom/index.html
index 8af396c04..4fb74c08f 100644
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+++ b/blog/orange-in-classroom/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>Orange, education, teaching, university</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in Classroom</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 11, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Orange in Classroom</h1>
 <p>About three weeks ago, we put out a short survey asking professors, teaching assistants, and students to tell us how they use Orange in class. We have gotten four-hundred responses, and it turns out that over two hundred universities from around the world actively use Orange in the classroom.</p>
 <p>We sincerely thank everyone for the answers!</p>
@@ -361,4 +361,4 @@
 <li><a href="https://weill.cornell.edu/">Weill Cornell Medicine</a>, USA</li>
 <li><a href="http://english.uny.ac.id/">Yogyakarta State University</a>, Indonesia</li>
 <li><a href="https://www.fh-zwickau.de/english/">Zwickau University of Applied Sciences</a>, Germany</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-01-11","draft":false,"title":"Orange in Classroom","thumbImage":"2021-01-11-orange-in-education-small.png","frontPageImage":"2021-01-11-orange-in-education-small.png","blog":["Orange","education","teaching","university"],"shortExcerpt":"Orange is used in over two hundred universities around the world.","longExcerpt":"Orange is actively used in classrooms at over two hundred universities from around the world.","x2images":true,"oldUrl":"/blog/2021/2021-01-11-orange-in-classroom/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Orange in Classroom\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"About three weeks ago, we put out a short survey asking professors, teaching assistants, and students to tell us how they use Orange in class. 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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-01-11","draft":false,"title":"Orange in Classroom","thumbImage":"2021-01-11-orange-in-education-small.png","frontPageImage":"2021-01-11-orange-in-education-small.png","blog":["Orange","education","teaching","university"],"shortExcerpt":"Orange is used in over two hundred universities around the world.","longExcerpt":"Orange is actively used in classrooms at over two hundred universities from around the world.","x2images":true,"oldUrl":"/blog/2021/2021-01-11-orange-in-classroom/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WorkflowScreenshot} = _components;\n  if (!WorkflowScreenshot) _missingMdxReference(\"WorkflowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Orange in Classroom\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"About three weeks ago, we put out a short survey asking professors, teaching assistants, and students to tell us how they use Orange in class. We have gotten four-hundred responses, and it turns out that over two hundred universities from around the world actively use Orange in the classroom.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We sincerely thank everyone for the answers!\"\n    }), \"\\n\", _jsx(WorkflowScreenshot, {\n      src: \"/blog/2021-01-orange-in-classroom/2021-01-11-orange-in-education.png\",\n      width: \"1015\",\n      height: \"685\",\n      src: \"/blog/2021-01-orange-in-classroom/__optimized-images__/2021-01-11-orange-in-education.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here is a a list of universities, in alphabetical order, we have compiled from the survey.\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://uad.ac.id/en/\",\n          children: \"Ahmad Dahlan University\"\n        }), \", Indonesia\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://www.univ-amu.fr/en\",\n          children: \"Aix-Marseille University\"\n        }), \", France\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://aut.ac.ir/en/\",\n          children: \"Amirkabir University of Technology\"\n        }), \", Iran\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://www.ucab.edu.ve/\",\n          children: \"Andrés Bello Catholic University\"\n        }), \", Venezuela\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://www.anits.edu.in/\",\n          children: \"Anil Neerukonda Institute of Technology \u0026 Sciences\"\n        }), \", India\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://www.upao.edu.pe/\",\n          children: 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diff --git a/blog/orange-in-kolkata-india/index.html b/blog/orange-in-kolkata-india/index.html
index f7e76acc8..976460bf9 100644
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+++ b/blog/orange-in-kolkata-india/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange in Kolkata, India</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange in Kolkata, India"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-11-08 09:11:29+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="education"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, orange3, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in Kolkata, India</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 08, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We have just completed the hands-on course on data science at one the most famous Indian educational institutions, <a href="http://www.isical.ac.in">Indian Statistical Institute</a>. A one week course was invited by Institute&#x27;s director <a href="http://www.isical.ac.in/~sanghami/">Prof. Dr. Sanghamitra </a>Bandyopadhyay, and financially supported by the founding of India&#x27;s <a href="http://www.gian.iitkgp.ac.in">Global Initiative of Academic Networks</a>.</p>
 <a href="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-house-mahalanobis.jpg" data-gallery="true"><img alt="" loading="lazy" width="2048" height="1536" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-house-mahalanobis.jpg"/></a>
 <p>Indian Statistical Institute lies in the hearth of old Kolkata. A peaceful oasis of picturesque campus with mango orchards and waterlily lakes was founded by <a href="https://en.wikipedia.org/wiki/Prasanta_Chandra_Mahalanobis">Prof. Prasanta Chandra Mahalanobis</a>, one of the giants of statistics. Today, the Institute researches statistics and computational approaches to data analysis and runs a grad school, where a rather small number of students are hand-picked from tens of thousands of applicants.</p>
@@ -158,4 +158,4 @@
 <p>The course was not one of the lightest for the lecturer (Blaž Zupan). About five full hours each day for five days in a row, extremely motivated students with questions filling all of the coffee breaks, the need for deeper dive into some of the methods after questions in the classroom, and much need for improvisation to adapt our standard data science course to possibly the brightest pack of data science students we have seen so far. We have covered almost a full spectrum of data science topics: from data visualization to supervised learning (classification and regression, regularization), model exploration and estimation of quality. Plus computation of distances, unsupervised learning, outlier detection, data projection, and methods for parameter estimation. We have applied these to data from health care, business (which proposal on Kickstarter will succeed?), and images. Again, just like in our other data science courses, the use of Orange&#x27;s educational widgets, such as <a href="/widget-catalog/data/paintdata/">Paint Data</a>, <a href="/widget-catalog/educational/interactive-kmeans/">Interactive k-Means</a>, and <a href="/widget-catalog/educational/polynomial-regression/">Polynomial Regression</a> helped us in intuitive understanding of the machine learning techniques.</p>
 <a href="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-lecture.jpg" data-gallery="true"><img alt="" loading="lazy" width="6016" height="4016" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-lecture.jpg"/></a>
 <p>The course was beautifully organized by Prof. Dr. Saurabh Das with the help of Prof. Dr. Shubhra Sankar Ray and we would like to thank them for their devotion and excellent organization skills. And of course, many thanks to participating students: for an educator, it is always a great pleasure to lecture and work with highly motivated and curious colleagues that made our trip to Kolkata fruitful and fun.</p>
-<a href="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-participants.jpg" data-gallery="true"><img alt="" loading="lazy" width="6016" height="2816" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-participants.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-11-08 09:11:29+00:00","draft":false,"title":"Orange in Kolkata, India","blog":["education","orange3","workshop"],"oldUrl":"/blog/2017/11/08/orange-in-kolkata-india/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have just completed the hands-on course on data science at one the most famous Indian educational institutions, \", _jsx(_components.a, {\n        href: \"http://www.isical.ac.in\",\n        children: \"Indian Statistical Institute\"\n      }), \". 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+<a href="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-participants.jpg" data-gallery="true"><img alt="" loading="lazy" width="6016" height="2816" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-participants.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-11-08 09:11:29+00:00","draft":false,"title":"Orange in Kolkata, India","blog":["education","orange3","workshop"],"oldUrl":"/blog/2017/11/08/orange-in-kolkata-india/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have just completed the hands-on course on data science at one the most famous Indian educational institutions, \", _jsx(_components.a, {\n        href: \"http://www.isical.ac.in\",\n        children: \"Indian Statistical Institute\"\n      }), \". A one week course was invited by Institute's director \", _jsx(_components.a, {\n        href: \"http://www.isical.ac.in/~sanghami/\",\n        children: \"Prof. Dr. Sanghamitra \"\n      }), \"Bandyopadhyay, and financially supported by the founding of India's \", _jsx(_components.a, {\n        href: \"http://www.gian.iitkgp.ac.in\",\n        children: \"Global Initiative of Academic Networks\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-11-orange-in-kolkata-india/__optimized-images__/isi-house-mahalanobis.jpg\",\n      alt: \"\",\n      width: \"2048\",\n      height: \"1536\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Indian Statistical Institute lies in the hearth of old Kolkata. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, python, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in Pavia, Italy</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 19, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>These days, we (Blaz Zupan and Marinka Zitnik, with full background support of entire <a href="http://www.biolab.si/en/">Bioinformatics Lab</a>) are running a three-day course on Data Mining in Python. <a href="http://www.labmedinfo.org/people/bellazzi">Riccardo Bellazzi</a>, a professor at <a href="http://www.unipv.eu/">University of Pavia</a>, a world-renown researcher in biomedical informatics, and most of all, a great friend, has invited us to run the elective course for Pavia&#x27;s grad students. The enrollment was, he says, overwhelming, as with over 50 students this is by far the best attended grad course at Pavia&#x27;s faculty of engineering in the past years.</p>
 <p>We have opted for the hands-on course and a running it as a workshop. The lectures include a new, development version of <a href="http://orange.biolab.si/orange3/">Orange 3</a>, and mix it with <a href="http://www.numpy.org">numpy</a>, <a href="http://scikit-learn.org/stable/">scikit-learn</a>, <a href="http://matplotlib.org">matplotlib</a>, <a href="https://networkx.github.io">networkx</a> and bunch of other libraries. Course themes are classification, clustering, data projection and network analysis.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, infraorange, infrared spectra, python, spectroscopy</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in Space</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 21, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Did you know that Orange has already been to space? <a href="https://www.ias.u-psud.fr/en/content/team-members-0">Rosario Brunetto</a> (<a href="https://www.ias.u-psud.fr/">IAS-Orsay</a>, France) has been working on the analysis of infrared images of asteroid Ryugu as a member of the <a href="http://global.jaxa.jp/projects/sat/hayabusa2/">JAXA Hayabusa2</a> team. The <a href="https://en.wikipedia.org/wiki/Hayabusa2">Hayabusa2</a> asteroid sample-return mission aims to retrieve data and samples from the near-Earth Ryugu asteroid and analyze its composition. Hayabusa2 arrived at Ryugu on June 27 and while the spacecraft will return to Earth with a sample only in late 2020, the mission already started collecting and sending back the data. And of course, a part of the analysis of Hayabusa&#x27;s space data has been done in Orange!</p>
 <p><a href="/blog/2018-09-orange-in-space/__optimized-images__/ryugu2.jpg" data-gallery="true"><img alt="" loading="lazy" width="1024" height="559" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-09-orange-in-space/__optimized-images__/ryugu2.jpg"/></a>
 An image of the asteroid Ryugu acquired by the Hayabusa2 (<em><a href="http://global.jaxa.jp/">©JAXA</a>)</em>.</p>
@@ -164,4 +164,4 @@
 <p><a href="/blog/2018-09-orange-in-space/__optimized-images__/Orange_paris_meteorite-1.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="831" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-09-orange-in-space/__optimized-images__/Orange_paris_meteorite-1.png"/></a>
 k-Means clusters plotted in the HyperSpectra widget.</p>
 <p>At the top, you can see a 2D map of the meteorite sample showing the distribution of the clusters that were identified with k-Means. At the bottom, you see cluster averages for the spectra. The green region is the most interesting one and it shows crystalline minerals, which formed billions of years ago as the hydrothermal processes in the asteroid parent body of the meteorite turned amorphous silicates into phyllosilicates. The purple, on the contrary, shows different micro-sized minerals.</p>
-<p>This is how to easily identify the compositional structure of samples with just a couple of widgets. Orange seems to love going to space and can&#x27;t wait to get its hands dirty with more astro-data!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-09-21 07:32:17+00:00","draft":false,"title":"Orange in Space","blog":["addons","infraorange","infrared spectra","python","spectroscopy"],"oldUrl":"/blog/2018/09/21/orange-in-space/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Did you know that Orange has already been to space? \", _jsx(_components.a, {\n        href: \"https://www.ias.u-psud.fr/en/content/team-members-0\",\n        children: \"Rosario Brunetto\"\n      }), \" (\", _jsx(_components.a, {\n        href: \"https://www.ias.u-psud.fr/\",\n        children: \"IAS-Orsay\"\n      }), \", France) has been working on the analysis of infrared images of asteroid Ryugu as a member of the \", _jsx(_components.a, {\n        href: \"http://global.jaxa.jp/projects/sat/hayabusa2/\",\n        children: \"JAXA Hayabusa2\"\n      }), \" team. The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Hayabusa2\",\n        children: \"Hayabusa2\"\n      }), \" asteroid sample-return mission aims to retrieve data and samples from the near-Earth Ryugu asteroid and analyze its composition. Hayabusa2 arrived at Ryugu on June 27 and while the spacecraft will return to Earth with a sample only in late 2020, the mission already started collecting and sending back the data. And of course, a part of the analysis of Hayabusa's space data has been done in Orange!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-09-orange-in-space/__optimized-images__/ryugu2.jpg\",\n        alt: \"\",\n        width: \"1024\",\n        height: \"559\"\n      }), \"\\nAn image of the asteroid Ryugu acquired by the Hayabusa2 (\", _jsxs(_components.em, {\n        children: [_jsx(_components.a, {\n          href: \"http://global.jaxa.jp/\",\n          children: \"©JAXA\"\n        }), \")\"]\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Within the Hayabusa2 project, near-infrared spectral data will be collected in three series: the first part is the macro data from remote sensing measurements that are being collected at different altitudes from the asteroid by the Japanese spectrometer NIRS3 (\", _jsxs(_components.em, {\n        children: [_jsx(_components.a, {\n          href: \"http://global.jaxa.jp/\",\n          children: \"©JAXA\"\n        }), \").\"]\n      }), \" The second part is surface infrared imaging at the micron scale that will soon be performed (October 2018) by the French \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/MicrOmega-IR\",\n        children: \"MicrOmega\"\n      }), \" instrument on the lander MASCOT (\", _jsx(_components.a, {\n        href: \"https://www.dlr.de/irs/en/desktopdefault.aspx/tabid-11302/#gallery/28470\",\n        children: \"DLR-CNES\"\n      }), \"). The third part are the samples that will be analyzed upon return. Among the techniques that will be used in different laboratories around the world in 2021 to analyze the returned samples are the hyperspectral imaging and micro-tomography with an infrared imaging FPA microscope, that will be performed by the IAS team at \", _jsx(_components.a, {\n        href: \"https://www.synchrotron-soleil.fr/en/beamlines/smis\",\n        children: \"SMIS-SOLEIL\"\n      }), \". This means the data will contain satellite spectral images as well as microscope measurements.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Dr. Brunetto is currently working with the first part of the data, namely the macro hyperspectral images of the asteroid. Several tens of thousands of spectra over 70 spectral channels have already been acquired. The main goal of this initial exploration was to constrain the surface composition.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once the data was preprocessed and cleaned in Python, separate surface regions were extracted in Orange with k-Means and PCA and plotted with the HyperSpectra widget, which comes as a part of the \", _jsx(_components.a, {\n        href: \"https://github.com/Quasars/orange-spectroscopy\",\n        children: \"Spectroscopy package\"\n      }), \". So why was Orange chosen over other tools? Dr. Brunetto says Orange is an easy and friendly tool for complicated things, such as exploring the compositional diversity of the asteroid at the different scales. There are many clustering techniques he can use in Orange and he likes how he can interactively change the number of clusters and the changes immediately show in the plot. This enables the researchers to determine the level of granularity of the analysis, while they can also immediately inspect how each cluster looks like in a hyperspectra plot.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Moreover, one can quickly test methods and visualize the effects and at the same time have a good overview of the workflow. Workflows can also be reused once the new data comes in or, if the pipeline is standard, used on a completely different data set!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-09-orange-in-space/__optimized-images__/OrangeBrunetto.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"906\"\n      }), \"\\nA simple workflow for the analysis of spectral data. 😁 A great thing about Orange is that you can label parts of the workflow and explore a different aspect of the data in each branch!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We would of course love to show you the results of the asteroid analysis, but as the project is still ongoing, the data is not yet available to the public. Instead, we asked Zélia Dionnet, dr. Brunetto's PhD student, to share the results of her work on the \", _jsx(_components.a, {\n        href: \"https://onlinelibrary.wiley.com/doi/pdf/10.1111/maps.13178\",\n        children: \"organic and mineralogic heterogeneity of the Paris meteorite\"\n      }), \", which were already published.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"She analyzed the composition of the Paris meteorite, which was discovered in 2008 in a statue. The story of how the \", _jsx(_components.a, {\n        href: \"https://www.mindat.org/loc-264051.html\",\n        children: \"meteorite was found\"\n      }), \" is quite interesting in itself, but we wanted to know more on how the sample was analyzed in Orange. Dionnet had a slightly larger data set, with 16,000 spectra and 1600 wavenumbers. Just like dr. Brunetto, she used k-Means to discover interesting regions in the sample and Hyperspectra widget to plot the results.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-09-orange-in-space/__optimized-images__/Orange_paris_meteorite-1.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"831\"\n      }), \"\\nk-Means clusters plotted in the HyperSpectra widget.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At the top, you can see a 2D map of the meteorite sample showing the distribution of the clusters that were identified with k-Means. At the bottom, you see cluster averages for the spectra. The green region is the most interesting one and it shows crystalline minerals, which formed billions of years ago as the hydrothermal processes in the asteroid parent body of the meteorite turned amorphous silicates into phyllosilicates. The purple, on the contrary, shows different micro-sized minerals.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is how to easily identify the compositional structure of samples with just a couple of widgets. Orange seems to love going to space and can't wait to get its hands dirty with more astro-data!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-in-space"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>This is how to easily identify the compositional structure of samples with just a couple of widgets. Orange seems to love going to space and can&#x27;t wait to get its hands dirty with more astro-data!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-09-21 07:32:17+00:00","draft":false,"title":"Orange in Space","blog":["addons","infraorange","infrared spectra","python","spectroscopy"],"oldUrl":"/blog/2018/09/21/orange-in-space/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Did you know that Orange has already been to space? \", _jsx(_components.a, {\n        href: \"https://www.ias.u-psud.fr/en/content/team-members-0\",\n        children: \"Rosario Brunetto\"\n      }), \" (\", _jsx(_components.a, {\n        href: \"https://www.ias.u-psud.fr/\",\n        children: \"IAS-Orsay\"\n      }), \", France) has been working on the analysis of infrared images of asteroid Ryugu as a member of the \", _jsx(_components.a, {\n        href: \"http://global.jaxa.jp/projects/sat/hayabusa2/\",\n        children: \"JAXA Hayabusa2\"\n      }), \" team. The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Hayabusa2\",\n        children: \"Hayabusa2\"\n      }), \" asteroid sample-return mission aims to retrieve data and samples from the near-Earth Ryugu asteroid and analyze its composition. Hayabusa2 arrived at Ryugu on June 27 and while the spacecraft will return to Earth with a sample only in late 2020, the mission already started collecting and sending back the data. And of course, a part of the analysis of Hayabusa's space data has been done in Orange!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-09-orange-in-space/__optimized-images__/ryugu2.jpg\",\n        alt: \"\",\n        width: \"1024\",\n        height: \"559\"\n      }), \"\\nAn image of the asteroid Ryugu acquired by the Hayabusa2 (\", _jsxs(_components.em, {\n        children: [_jsx(_components.a, {\n          href: \"http://global.jaxa.jp/\",\n          children: \"©JAXA\"\n        }), \")\"]\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Within the Hayabusa2 project, near-infrared spectral data will be collected in three series: the first part is the macro data from remote sensing measurements that are being collected at different altitudes from the asteroid by the Japanese spectrometer NIRS3 (\", _jsxs(_components.em, {\n        children: [_jsx(_components.a, {\n          href: \"http://global.jaxa.jp/\",\n          children: \"©JAXA\"\n        }), \").\"]\n      }), \" The second part is surface infrared imaging at the micron scale that will soon be performed (October 2018) by the French \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/MicrOmega-IR\",\n        children: \"MicrOmega\"\n      }), \" instrument on the lander MASCOT (\", _jsx(_components.a, {\n        href: \"https://www.dlr.de/irs/en/desktopdefault.aspx/tabid-11302/#gallery/28470\",\n        children: \"DLR-CNES\"\n      }), \"). The third part are the samples that will be analyzed upon return. Among the techniques that will be used in different laboratories around the world in 2021 to analyze the returned samples are the hyperspectral imaging and micro-tomography with an infrared imaging FPA microscope, that will be performed by the IAS team at \", _jsx(_components.a, {\n        href: \"https://www.synchrotron-soleil.fr/en/beamlines/smis\",\n        children: \"SMIS-SOLEIL\"\n      }), \". This means the data will contain satellite spectral images as well as microscope measurements.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Dr. Brunetto is currently working with the first part of the data, namely the macro hyperspectral images of the asteroid. Several tens of thousands of spectra over 70 spectral channels have already been acquired. The main goal of this initial exploration was to constrain the surface composition.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once the data was preprocessed and cleaned in Python, separate surface regions were extracted in Orange with k-Means and PCA and plotted with the HyperSpectra widget, which comes as a part of the \", _jsx(_components.a, {\n        href: \"https://github.com/Quasars/orange-spectroscopy\",\n        children: \"Spectroscopy package\"\n      }), \". So why was Orange chosen over other tools? Dr. Brunetto says Orange is an easy and friendly tool for complicated things, such as exploring the compositional diversity of the asteroid at the different scales. There are many clustering techniques he can use in Orange and he likes how he can interactively change the number of clusters and the changes immediately show in the plot. This enables the researchers to determine the level of granularity of the analysis, while they can also immediately inspect how each cluster looks like in a hyperspectra plot.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Moreover, one can quickly test methods and visualize the effects and at the same time have a good overview of the workflow. Workflows can also be reused once the new data comes in or, if the pipeline is standard, used on a completely different data set!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-09-orange-in-space/__optimized-images__/OrangeBrunetto.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"906\"\n      }), \"\\nA simple workflow for the analysis of spectral data. 😁 A great thing about Orange is that you can label parts of the workflow and explore a different aspect of the data in each branch!\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We would of course love to show you the results of the asteroid analysis, but as the project is still ongoing, the data is not yet available to the public. Instead, we asked Zélia Dionnet, dr. Brunetto's PhD student, to share the results of her work on the \", _jsx(_components.a, {\n        href: \"https://onlinelibrary.wiley.com/doi/pdf/10.1111/maps.13178\",\n        children: \"organic and mineralogic heterogeneity of the Paris meteorite\"\n      }), \", which were already published.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"She analyzed the composition of the Paris meteorite, which was discovered in 2008 in a statue. The story of how the \", _jsx(_components.a, {\n        href: \"https://www.mindat.org/loc-264051.html\",\n        children: \"meteorite was found\"\n      }), \" is quite interesting in itself, but we wanted to know more on how the sample was analyzed in Orange. Dionnet had a slightly larger data set, with 16,000 spectra and 1600 wavenumbers. Just like dr. Brunetto, she used k-Means to discover interesting regions in the sample and Hyperspectra widget to plot the results.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-09-orange-in-space/__optimized-images__/Orange_paris_meteorite-1.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"831\"\n      }), \"\\nk-Means clusters plotted in the HyperSpectra widget.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"At the top, you can see a 2D map of the meteorite sample showing the distribution of the clusters that were identified with k-Means. At the bottom, you see cluster averages for the spectra. The green region is the most interesting one and it shows crystalline minerals, which formed billions of years ago as the hydrothermal processes in the asteroid parent body of the meteorite turned amorphous silicates into phyllosilicates. The purple, on the contrary, shows different micro-sized minerals.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is how to easily identify the compositional structure of samples with just a couple of widgets. Orange seems to love going to space and can't wait to get its hands dirty with more astro-data!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-in-space"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/orange-in-the-cloud/index.html b/blog/orange-in-the-cloud/index.html
index 35129b554..ca1e84c9f 100644
--- a/blog/orange-in-the-cloud/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange in the Cloud</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange in the Cloud"/><meta property="og:description" content="Use Orange remotely by running it on a remote server as a docker container."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2020-01-16"/><meta property="article:author" content="Andrej Čopar"/><meta property="article:tag" content="cloud"/><meta property="article:tag" content="server"/><meta property="article:tag" content="remote"/><meta property="og:image" content="/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-orange-cloud.png"/><meta property="og:image:width" content="300"/><meta property="og:image:height" content="200"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>cloud, server, remote</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange in the Cloud</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Andrej Čopar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 16, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Many problems are too big and require too much processing power to be efficiently processed on your laptop or PC. In such cases, the data is usually transferred to a remote server and processed using custom code, which is often time consuming. Now, there is a way to run Orange on a remote server so that you can keep using its interactive graphical interface. We will show you how to run Orange on the remote server so that you can use it through your web browser.</p>
 <a href="/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-weborange.png" data-gallery="true"><img alt="" loading="lazy" width="1920" height="1080" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-weborange.png"/></a>
 <p>\</p>
@@ -160,4 +160,4 @@
 <p>Nginx redirects you to the <a href="https://guacamole.apache.org/">Apache Guacamole</a> web application. In Guacamole you can manage multiple users and specify which of your Orange instances each user has access to. Guacamole then connects you to a selected <a href="https://github.com/biolab/orange-docker">Orange-docker</a> container through an RDP or VNC connection. Once it is connected, you can see the remote desktop in your browser. You can use Orange just like on a local computer (see the image above), although you may need a few minutes to get used to the Linux environment.</p>
 <p>Dockers allow you to run many lightweight isolated Linux containers on the same machine. We prepared a <a href="https://github.com/biolab/orange-docker">docker image</a> that comes pre-configured with graphical desktop environment, a remote desktop server, Orange application and a few convenience applications (Libre Office, web browsers). You can run many isolated Orange-docker containers, so each user can work on a different project. When users collaborate on a project, they can connect to the same instance and share the same screen (read-only or full access). You can upload and download your data to and from the remote server using drag &amp; drop feature or the side menu. Alternatively you can transfer the data with one of the web browsers that are provided in the container.</p>
 <p>There are many other benefits to running Orange on the server infrastructure. First, <strong>Orange can stay open and continue to process the data</strong> even when you are offline. Second, you can <strong>access the same workflow from any computer</strong>. Third, multiple users can <strong>interactively collaborate on the same workflow</strong>. Finally, you do not have to keep the data on a local computer and <strong>you do not need to install Orange on a local computer</strong>. Note that this is a self-hosted solution, which means that all your data remains on the servers under your control.</p>
-<p>For a complete installation guide and more details see <a href="https://github.com/biolab/orange-docker">orange-docker</a> Github repository.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Andrej Čopar","date":"2020-01-16","draft":false,"title":"Orange in the Cloud","thumbImage":"2020-01-16-orange-cloud.png","frontPageImage":"2020-01-16-orange-cloud.png","blog":["cloud","server","remote"],"shortExcerpt":"Use Orange remotely by running it on a remote server as a docker container.","longExcerpt":"Use Orange remotely by running it on a remote server as a docker container.","oldUrl":"/blog/2020/2020-01-16-orange-cloud/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Many problems are too big and require too much processing power to be efficiently processed on your laptop or PC. In such cases, the data is usually transferred to a remote server and processed using custom code, which is often time consuming. Now, there is a way to run Orange on a remote server so that you can keep using its interactive graphical interface. We will show you how to run Orange on the remote server so that you can use it through your web browser.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-orange-cloud/2020-01-16-weborange.png\",\n      width: \"1920\",\n      height: \"1080\",\n      src: \"/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-weborange.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In a nutshell, you browser opens a remote desktop connection to an Orange instance that runs inside a docker container on a remote server. 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+<p>For a complete installation guide and more details see <a href="https://github.com/biolab/orange-docker">orange-docker</a> Github repository.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Andrej Čopar","date":"2020-01-16","draft":false,"title":"Orange in the Cloud","thumbImage":"2020-01-16-orange-cloud.png","frontPageImage":"2020-01-16-orange-cloud.png","blog":["cloud","server","remote"],"shortExcerpt":"Use Orange remotely by running it on a remote server as a docker container.","longExcerpt":"Use Orange remotely by running it on a remote server as a docker container.","oldUrl":"/blog/2020/2020-01-16-orange-cloud/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Many problems are too big and require too much processing power to be efficiently processed on your laptop or PC. In such cases, the data is usually transferred to a remote server and processed using custom code, which is often time consuming. Now, there is a way to run Orange on a remote server so that you can keep using its interactive graphical interface. We will show you how to run Orange on the remote server so that you can use it through your web browser.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-orange-cloud/2020-01-16-weborange.png\",\n      width: \"1920\",\n      height: \"1080\",\n      src: \"/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-weborange.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In a nutshell, you browser opens a remote desktop connection to an Orange instance that runs inside a docker container on a remote server. Now let us go into more details.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In our configuration we used several different technologies such as \", _jsx(_components.a, {\n        href: \"https://www.docker.com/\",\n        children: \"Docker\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"https://guacamole.apache.org/\",\n        children: \"Apache Guacamole\"\n      }), \", which are shown in the diagram. First, you need to set up \", _jsx(_components.a, {\n        href: \"https://nginx.org/en/\",\n        children: \"Nginx\"\n      }), \" (or alternatively \", _jsx(_components.a, {\n        href: \"https://httpd.apache.org/\",\n        children: \"Apache web server\"\n      }), \"), which is used to ensure that all communication to the server is SSL encrypted. This is very important, because remote desktop protocols such as \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Remote_Desktop_Protocol\",\n        children: \"Remote Desktop protocol (RDP)\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Virtual_Network_Computing\",\n        children: \"Virtual network computing (VNC)\"\n      }), \" are not encrypted. Failure to do so will expose your data to anyone listening on the network.\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-orange-cloud/2020-01-16-stack.png\",\n      width: \"848\",\n      height: \"504\",\n      src: \"/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-stack.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Nginx redirects you to the \", _jsx(_components.a, {\n        href: \"https://guacamole.apache.org/\",\n        children: \"Apache Guacamole\"\n      }), \" web application. In Guacamole you can manage multiple users and specify which of your Orange instances each user has access to. Guacamole then connects you to a selected \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange-docker\",\n        children: \"Orange-docker\"\n      }), \" container through an RDP or VNC connection. Once it is connected, you can see the remote desktop in your browser. You can use Orange just like on a local computer (see the image above), although you may need a few minutes to get used to the Linux environment.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Dockers allow you to run many lightweight isolated Linux containers on the same machine. We prepared a \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange-docker\",\n        children: \"docker image\"\n      }), \" that comes pre-configured with graphical desktop environment, a remote desktop server, Orange application and a few convenience applications (Libre Office, web browsers). You can run many isolated Orange-docker containers, so each user can work on a different project. When users collaborate on a project, they can connect to the same instance and share the same screen (read-only or full access). You can upload and download your data to and from the remote server using drag \u0026 drop feature or the side menu. Alternatively you can transfer the data with one of the web browsers that are provided in the container.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"There are many other benefits to running Orange on the server infrastructure. First, \", _jsx(_components.strong, {\n        children: \"Orange can stay open and continue to process the data\"\n      }), \" even when you are offline. Second, you can \", _jsx(_components.strong, {\n        children: \"access the same workflow from any computer\"\n      }), \". Third, multiple users can \", _jsx(_components.strong, {\n        children: \"interactively collaborate on the same workflow\"\n      }), \". Finally, you do not have to keep the data on a local computer and \", _jsx(_components.strong, {\n        children: \"you do not need to install Orange on a local computer\"\n      }), \". Note that this is a self-hosted solution, which means that all your data remains on the servers under your control.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For a complete installation guide and more details see \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange-docker\",\n        children: \"orange-docker\"\n      }), \" Github repository.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":300,"height":200,"src":"/blog/2020-01-orange-cloud/__optimized-images__/2020-01-16-orange-cloud.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-in-the-cloud"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange is again participating in GSoC</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 27, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This year Orange is again participating in <a href="https://socghop.appspot.com/gsoc/homepage/google/gsoc2012">Google Summer of Code</a> as a mentoring organization. Student proposal submission period is running and the deadline is on 6th April. We have prepared <a href="http://orange.biolab.si/trac/intertrac/wiki%3AGSoC">a page on our Trac</a> with more information about the Google Summer of Code program, especially how the interested students should apply with their proposals. There is also a list of <a href="http://orange.biolab.si/trac/intertrac/wiki%3AGSoC/Ideas">of some ideas we are proposing</a> for this year but feel free to suggest your own ideas how you could contribute to Orange and make it even better.</p>
-<p>Google Summer of Code is a <a href="http://www.google.com/">Google</a>-sponsored program where Google stipends students working for a summer job on an open source projects from all around the world. Student is paid $5000 (and a t-shirt!) for approximately two months of work/contribution to the project. More about the program is available on <a href="http://socghop.appspot.com/">its homepage</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-03-27 09:04:00+00:00","draft":false,"title":"Orange is again participating in GSoC","blog":["gsoc"],"oldUrl":"/blog/2012/03/27/orange-is-again-participating-in-gsoc/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This year Orange is again participating in \", _jsx(_components.a, {\n        href: \"https://socghop.appspot.com/gsoc/homepage/google/gsoc2012\",\n        children: \"Google Summer of Code\"\n      }), \" as a mentoring organization. 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+<p>Google Summer of Code is a <a href="http://www.google.com/">Google</a>-sponsored program where Google stipends students working for a summer job on an open source projects from all around the world. Student is paid $5000 (and a t-shirt!) for approximately two months of work/contribution to the project. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, interface, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange is Getting Smarter</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>IRGOLIC</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 22, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the past few months, Orange has been getting smarter and sleeker.</p>
 <p>Since version 3.15.0, Orange remembers which distinct widgets users like to connect, adjusting the sorting on the widget search menu accordingly. Additionally, there is a new look for the Edit Links window coming soon.</p>
 <p>Orange recently implemented a basic form of opt-in usage tracking, specifically targeting how users add widgets to the canvas.</p>
@@ -198,4 +198,4 @@
 <li>(Query), if type is Search or Extend</li>
 </ul>
 </li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"IRGOLIC","date":"2018-11-22 19:11:58+00:00","draft":false,"title":"Orange is Getting Smarter","blog":["analysis","interface","orange3"],"shortExcerpt":"Orange recently implemented a basic form of opt-in usage tracking, specifically targeting how users add widgets to the canvas.","oldUrl":"/blog/2018/11/22/orange-is-getting-smarter/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In the past few months, Orange has been getting smarter and sleeker.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Since version 3.15.0, Orange remembers which distinct widgets users like to connect, adjusting the sorting on the widget search menu accordingly. 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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"IRGOLIC","date":"2018-11-22 19:11:58+00:00","draft":false,"title":"Orange is Getting Smarter","blog":["analysis","interface","orange3"],"shortExcerpt":"Orange recently implemented a basic form of opt-in usage tracking, specifically targeting how users add widgets to the canvas.","oldUrl":"/blog/2018/11/22/orange-is-getting-smarter/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In the past few months, Orange has been getting smarter and sleeker.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Since version 3.15.0, Orange remembers which distinct widgets users like to connect, adjusting the sorting on the widget search menu accordingly. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange Lecture Notes</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange Lecture Notes"/><meta property="og:description" content="Lecture notes for Orange workshops on machine learning and data science are now available online."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2020-02-08"/><meta property="article:author" content="Blaž Zupan"/><meta property="article:tag" content="education"/><meta property="article:tag" content="workshop"/><meta property="og:image" content="/blog/2020-02-lecture-notes/__optimized-images__/2020-02-08-lecture-notes-small.png"/><meta property="og:image:width" content="300"/><meta property="og:image:height" content="200"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Lecture Notes</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 08, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the past, we, the developers of Orange at the University of Ljubljana, have carried out over fifty hands-on workshops. We carried out the workshops for students in secondary schools, universities, Ph.D. programs, employees of scientific institutes, companies, and government institutions. We carried out the courses around the world and lectured in places like Houston, <a href="https://orange.biolab.si/blog/2017/03/06/luxembourg-pavia-ljubljana/">Pavia</a>, <a href="https://orange.biolab.si/blog/2019/2019-07-02-data-science-made-easy/">Hanover</a>, <a href="https://orange.biolab.si/blog/2018/05/03/data-mining-course-at-higher-school-of-economics-moscow/">Moscow</a>, Verona, <a href="https://orange.biolab.si/blog/2017/08/08/text-analysis-workshop-at-digital-humanities-2017/">Montreal</a>, <a href="https://orange.biolab.si/blog/2017/03/06/luxembourg-pavia-ljubljana/">Luxemburg</a>, <a href="https://orange.biolab.si/blog/2017/11/08/orange-in-kolkata-india/">Kolkata</a>, Liverpool, <a href="https://orange.biolab.si/blog/2018/02/02/image-analytics-workshop-at-aiucd-2018/">Bari</a>, Ashburn, Sao Paolo, <a href="https://orange.biolab.si/blog/2017/03/08/workshop-on-infraorange/">Trieste</a>, <a href="https://orange.biolab.si/blog/2019/2019-06-28-bled-econference/">Bled</a>, <a href="https://orange.biolab.si/blog/2018/11/06/data-mining-for-anthropologists/">Lisbon</a>, Konstanz, Oslo, <a href="https://orange.biolab.si/blog/2019/2019-11-20-belgrade-workshop/">Belgrade</a>, <a href="https://orange.biolab.si/blog/2019/4/24/orange-at-gis-ostrava/">Ostrava</a>, Melbourne, <a href="https://orange.biolab.si/blog/2018/05/30/spectroscopy-workshop-at-biospec-and-how-to-merge-data/">Ås</a>, Bochum, and <a href="https://orange.biolab.si/blog/2019/2019-11-15-telekom-workshop/">Ljubljana</a>. The lectures comprised an introduction to machine learning and data visualization and sometimes focused on more specific topics, like text or image mining, mining of spectral data, or even data mining in molecular biology.</p>
 <p>All of our workshops are hands-on. We start with the data and a problem and then dive into solving a problem with Orange. No PowerPoint slides, no dull and detached lectures. In the workshop, Orange allows us to go straight into data analysis. Orange is different from other workflow-based tools since we designed it for teaching. Our workshops are, therefore, a perfect testbed for Orange. Through their design and execution, we learn about possible improvements and functionalities that we then add to Orange and try out at a forthcoming workshop.</p>
 <p>We plan our Orange workshops by crafting a workshop program that we assemble into lecture notes. These notes are a refreshment material for the students and help workshop organizers who follow the lectures in the notes more or less strictly, depending on the audience, their engagements, and questions they may have during the workshop.</p>
@@ -162,4 +162,4 @@
 </ul>
 <p>The lecture notes teach concepts in machine learning and data science. For example, here is a snapshot of one of the pages from our lecture notes.</p>
 <a href="/blog/2020-02-lecture-notes/__optimized-images__/2020-02-08-lecture-notes-example-page-a.jpg" data-gallery="true"><img alt="" loading="lazy" width="788" height="1106" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-02-lecture-notes/__optimized-images__/2020-02-08-lecture-notes-example-page-a.jpg"/></a>
-<p>We tend to explain this through examples, and most often avoid any heavy mathematics. Our aim is the democratization of data science and would like to see Orange as an enabling tool that reaches a broad audience and complements toolboxes in R and Python that are intended for more tech savvy.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2020-02-08","draft":false,"title":"Orange Lecture Notes","thumbImage":"2020-02-08-lecture-notes-small.png","frontPageImage":"2020-02-08-lecture-notes-small.png","blog":["education","workshop"],"shortExcerpt":"Lecture notes for Orange workshops on machine learning and data science are now available online.","longExcerpt":"Lecture notes for Orange workshops on machine learning and data science are now available online.","oldUrl":"/blog/2020/2020-02-08-lecture-notes/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the past, we, the developers of Orange at the University of Ljubljana, have carried out over fifty hands-on workshops. We carried out the workshops for students in secondary schools, universities, Ph.D. programs, employees of scientific institutes, companies, and government institutions. We carried out the courses around the world and lectured in places like Houston, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2017/03/06/luxembourg-pavia-ljubljana/\",\n        children: \"Pavia\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2019/2019-07-02-data-science-made-easy/\",\n        children: \"Hanover\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2018/05/03/data-mining-course-at-higher-school-of-economics-moscow/\",\n        children: \"Moscow\"\n      }), \", Verona, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2017/08/08/text-analysis-workshop-at-digital-humanities-2017/\",\n        children: \"Montreal\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2017/03/06/luxembourg-pavia-ljubljana/\",\n        children: \"Luxemburg\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2017/11/08/orange-in-kolkata-india/\",\n        children: \"Kolkata\"\n      }), \", Liverpool, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2018/02/02/image-analytics-workshop-at-aiucd-2018/\",\n        children: \"Bari\"\n      }), \", Ashburn, Sao Paolo, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2017/03/08/workshop-on-infraorange/\",\n        children: \"Trieste\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2019/2019-06-28-bled-econference/\",\n        children: \"Bled\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2018/11/06/data-mining-for-anthropologists/\",\n        children: \"Lisbon\"\n      }), \", Konstanz, Oslo, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2019/2019-11-20-belgrade-workshop/\",\n        children: \"Belgrade\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2019/4/24/orange-at-gis-ostrava/\",\n        children: \"Ostrava\"\n      }), \", Melbourne, \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2018/05/30/spectroscopy-workshop-at-biospec-and-how-to-merge-data/\",\n        children: \"Ås\"\n      }), \", Bochum, and \", _jsx(_components.a, {\n        href: \"https://orange.biolab.si/blog/2019/2019-11-15-telekom-workshop/\",\n        children: \"Ljubljana\"\n      }), \". 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+<p>We tend to explain this through examples, and most often avoid any heavy mathematics. Our aim is the democratization of data science and would like to see Orange as an enabling tool that reaches a broad audience and complements toolboxes in R and Python that are intended for more tech savvy.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2020-02-08","draft":false,"title":"Orange Lecture Notes","thumbImage":"2020-02-08-lecture-notes-small.png","frontPageImage":"2020-02-08-lecture-notes-small.png","blog":["education","workshop"],"shortExcerpt":"Lecture notes for Orange workshops on machine learning and data science are now available online.","longExcerpt":"Lecture notes for Orange workshops on machine learning and data science are now available online.","oldUrl":"/blog/2020/2020-02-08-lecture-notes/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the past, we, the developers of Orange at the University of Ljubljana, have carried out over fifty hands-on workshops. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, matrixfactorization, nmf</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange NMF add-on</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 06, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://nimfa.biolab.si">Nimfa</a>, a Python library for non-negative matrix factorization (NMF), which was part of Orange GSoC program back in 2011 got its own <a href="http://orange.biolab.si/addons/">add-on</a>.</p>
 <p>Nimfa provides a plethora of initialization and factorization algorithms, quality measures along with examples on real-world and synthetic data sets. However, until now the analysis was possible only through Python scripting. A recent increase of interest in NMF techniques motivated Fajwel Fogel (a PhD student from INRIA, Paris, <a href="http://www.di.ens.fr/sierra/">SIERRA team</a>) to design and implement several widgets that deal with missing data in target matrices, their normalizations, viewing and assessing the quality of matrix factors returned by different matrix factorization algorithms. He also provided an implementation of robust singular value decomposition (rSVD). All NMF methods call Nimfa library.</p>
 <a href="/blog/2013-02-orange-nmf-add-on/__optimized-images__/nmf-addon-demo.png__1000x1000_q95.jpg" data-gallery="true"><img alt="" loading="lazy" width="527" height="497" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-02-orange-nmf-add-on/__optimized-images__/nmf-addon-demo.png__1000x1000_q95.jpg"/></a>
 <p>Above is shown a simple scenario in Orange that applies <a href="http://nimfa.biolab.si/nimfa.methods.factorization.lsnmf.html">LSNMF algorithm from Nimfa</a> to decompose a non-negative target matrix and visualizes its basis matrix (W) and coefficient matrix (H) as heat maps. NMF finds a parts-based representation of the data due to the fact that only additive, not subtractive, combinations are allowed, which results in improved interpretability of matrix factors. That is possible because non-negativity constraints are imposed in the NMF model in contrast to SVD, PCA and ICA, which provide only holistic representations. The effect can be easily seen if we investigate heat maps produced by the scenario above. Below are shown the target, basis and coefficient matrices (from left to right, top down), respectively.</p>
-<a href="/blog/2013-02-orange-nmf-add-on/__optimized-images__/lsnmf-addon-demo.png__432x826_q95_crop_upscale.jpg" data-gallery="true"><img alt="" loading="lazy" width="432" height="826" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-02-orange-nmf-add-on/__optimized-images__/lsnmf-addon-demo.png__432x826_q95_crop_upscale.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-02-06 13:47:00+00:00","draft":false,"title":"Orange NMF add-on","blog":["addons","matrixfactorization","nmf"],"oldUrl":"/blog/2013/02/06/orange-nmf-add-on/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://nimfa.biolab.si\",\n        children: \"Nimfa\"\n      }), \", a Python library for non-negative matrix factorization (NMF), which was part of Orange GSoC program back in 2011 got its own \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si/addons/\",\n        children: \"add-on\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Nimfa provides a plethora of initialization and factorization algorithms, quality measures along with examples on real-world and synthetic data sets. 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-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Orange Now Speaks 50 Languages</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Orange Now Speaks 50 Languages"/><meta property="og:description" content="In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian... "/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-10-05 10:36:13+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="preprocessing"/><meta property="article:tag" content="text mining"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>preprocessing, text mining</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Now Speaks 50 Languages</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 05, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian... Language support was most evident in the list of stopwords, normalization and POS tagging.</p>
 <p><strong>Related:</strong> <a href="/blog/2018/09/11/text-workshops-in-ljubljana/">Text Workshops in Ljubljana</a></p>
 <p>Stopwords come from <a href="https://www.nltk.org/">NLTK library</a>, so we can only offer whatever is available there. However, <a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf">TF-IDF</a> already implicitly considers stopwords, so the functionality is already implemented. For POS tagging, we would rely on Stanford POS tagger, that already has pre-trained models available.</p>
@@ -168,4 +168,4 @@
 <p><a href="/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.26.31.png" data-gallery="true"><img alt="" loading="lazy" width="838" height="444" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.26.31.png"/></a>
 Final workflow, where we compared the results of no normalization and UDPipe normalization in a word cloud.</p>
 <p>This is it. UDPipe contains lemmatization models for 50 languages and only when you click on a particular language in the Language option, will the resource be loaded, so your computer won&#x27;t be flooded with models for languages you won&#x27;t ever use. The installation of UDPipe could also be a little tricky, but after some initial obstacles, we have managed to prepare packages for both pip (OSX and Linux) and conda (Windows).</p>
-<p>We hope you enjoy the new possibilities of a freshly multilingual Orange!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-10-05 10:36:13+00:00","draft":false,"title":"Orange Now Speaks 50 Languages","blog":["preprocessing","text mining"],"shortExcerpt":"In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian... ","oldUrl":"/blog/2018/10/05/orange-now-speaks-50-languages/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian... Language support was most evident in the list of stopwords, normalization and POS tagging.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2018/09/11/text-workshops-in-ljubljana/\",\n        children: \"Text Workshops in Ljubljana\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Stopwords come from \", _jsx(_components.a, {\n        href: \"https://www.nltk.org/\",\n        children: \"NLTK library\"\n      }), \", so we can only offer whatever is available there. However, \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Tf%E2%80%93idf\",\n        children: \"TF-IDF\"\n      }), \" already implicitly considers stopwords, so the functionality is already implemented. For POS tagging, we would rely on Stanford POS tagger, that already has pre-trained models available.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The main issue was with normalization. While English can do without lemmatization and stemming for simple tasks, morphologically rich languages, such as Slovenian, perform poorly on un-normalized tokens. Cases and declensions present a problem for natural language processing, so we wanted to provide a tool for normalization in many different languages. Luckily, we found \", _jsx(_components.a, {\n        href: \"http://ufal.mff.cuni.cz/udpipe\",\n        children: \"UDPipe\"\n      }), \", a Czech initiative that offers trained lemmatization models for 50 languages. UDPipe is actually a preprocessing pipeline and we are already thinking about how to bring all of its functionality to Orange, but let us talk a bit about the recent improvements for normalization.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us load a simple corpus from Corpus widget, say \", _jsx(_components.em, {\n        children: \"grimm-tales-selected.tab\"\n      }), \" that contain 44 tales from the Grimm Brothers. Now, pass them through Preprocess Text and keep just the defaults, namely lowercase transformation, tokenization by words, and removal of stopwords. Here we see that we have \", _jsx(_components.em, {\n        children: \"came\"\n      }), \" as quite a frequent word and \", _jsx(_components.em, {\n        children: \"come\"\n      }), \" as a bit less frequent. But semantically, they are the same word from the verb \", _jsx(_components.em, {\n        children: \"to come\"\n      }), \". Shouldn't we consider them as one word?\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.28.50.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"911\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can. This is what normalization does - it transforms all words into their lemmas or basic grammatical form. \", _jsx(_components.em, {\n        children: \"Came\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"come\"\n      }), \" will become \", _jsx(_components.em, {\n        children: \"come\"\n      }), \", \", _jsx(_components.em, {\n        children: \"sons\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"son\"\n      }), \" will become \", _jsx(_components.em, {\n        children: \"son\"\n      }), \", \", _jsx(_components.em, {\n        children: \"pretty\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"prettier\"\n      }), \" will become \", _jsx(_components.em, {\n        children: \"pretty\"\n      }), \". This will result in less tokens that capture the text better, semantically speaking.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.27.03.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1156\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can see that \", _jsx(_components.em, {\n        children: \"came\"\n      }), \" became \", _jsx(_components.em, {\n        children: \"come\"\n      }), \" with 435 counts. \", _jsx(_components.em, {\n        children: \"Went\"\n      }), \" became \", _jsx(_components.em, {\n        children: \"go\"\n      }), \". \", _jsx(_components.em, {\n        children: \"Said\"\n      }), \" became \", _jsx(_components.em, {\n        children: \"say\"\n      }), \". And so on. As we said, this doesn't work only on verbs, but on all word forms.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One thing to note here. UDPipe has an internal tokenizer, that works with sentences instead of tokens. You can enable it by selecting UDPipe tokenizer option. What is the difference? A quicker version would be to tokenize all the words and just look up their lemma. But sometimes this can be wrong. Consider the sentence:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"I am wearing a tie to work.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now the word \", _jsx(_components.em, {\n        children: \"tie\"\n      }), \" is obviously a piece of clothing, which is indicated by the word wearing before it. But \", _jsx(_components.em, {\n        children: \"tie\"\n      }), \" alone can also be the verb \", _jsx(_components.em, {\n        children: \"to tie\"\n      }), \". 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Finally, if you wish to go with the better albeit slower UDPipe tokenizer, tick the UDPipe tokenizer box.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, UDPipe does not remove punctuation, so you might end up with words like \", _jsx(_components.em, {\n        children: \"rose.\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"away.\"\n      }), \", with the full stop at the end. 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+<p>We hope you enjoy the new possibilities of a freshly multilingual Orange!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-10-05 10:36:13+00:00","draft":false,"title":"Orange Now Speaks 50 Languages","blog":["preprocessing","text mining"],"shortExcerpt":"In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian... ","oldUrl":"/blog/2018/10/05/orange-now-speaks-50-languages/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In the past couple of weeks we have been working hard on introducing a better language support for the Text add-on. Until recently, Orange supported only a limited number of languages, mostly English and some bigger languages, such as Spanish, German, Arabic, Russian... Language support was most evident in the list of stopwords, normalization and POS tagging.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2018/09/11/text-workshops-in-ljubljana/\",\n        children: \"Text Workshops in Ljubljana\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Stopwords come from \", _jsx(_components.a, {\n        href: \"https://www.nltk.org/\",\n        children: \"NLTK library\"\n      }), \", so we can only offer whatever is available there. However, \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Tf%E2%80%93idf\",\n        children: \"TF-IDF\"\n      }), \" already implicitly considers stopwords, so the functionality is already implemented. For POS tagging, we would rely on Stanford POS tagger, that already has pre-trained models available.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The main issue was with normalization. While English can do without lemmatization and stemming for simple tasks, morphologically rich languages, such as Slovenian, perform poorly on un-normalized tokens. Cases and declensions present a problem for natural language processing, so we wanted to provide a tool for normalization in many different languages. Luckily, we found \", _jsx(_components.a, {\n        href: \"http://ufal.mff.cuni.cz/udpipe\",\n        children: \"UDPipe\"\n      }), \", a Czech initiative that offers trained lemmatization models for 50 languages. UDPipe is actually a preprocessing pipeline and we are already thinking about how to bring all of its functionality to Orange, but let us talk a bit about the recent improvements for normalization.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us load a simple corpus from Corpus widget, say \", _jsx(_components.em, {\n        children: \"grimm-tales-selected.tab\"\n      }), \" that contain 44 tales from the Grimm Brothers. Now, pass them through Preprocess Text and keep just the defaults, namely lowercase transformation, tokenization by words, and removal of stopwords. Here we see that we have \", _jsx(_components.em, {\n        children: \"came\"\n      }), \" as quite a frequent word and \", _jsx(_components.em, {\n        children: \"come\"\n      }), \" as a bit less frequent. But semantically, they are the same word from the verb \", _jsx(_components.em, {\n        children: \"to come\"\n      }), \". Shouldn't we consider them as one word?\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.28.50.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"911\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can. This is what normalization does - it transforms all words into their lemmas or basic grammatical form. \", _jsx(_components.em, {\n        children: \"Came\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"come\"\n      }), \" will become \", _jsx(_components.em, {\n        children: \"come\"\n      }), \", \", _jsx(_components.em, {\n        children: \"sons\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"son\"\n      }), \" will become \", _jsx(_components.em, {\n        children: \"son\"\n      }), \", \", _jsx(_components.em, {\n        children: \"pretty\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"prettier\"\n      }), \" will become \", _jsx(_components.em, {\n        children: \"pretty\"\n      }), \". This will result in less tokens that capture the text better, semantically speaking.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.27.03.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1156\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can see that \", _jsx(_components.em, {\n        children: \"came\"\n      }), \" became \", _jsx(_components.em, {\n        children: \"come\"\n      }), \" with 435 counts. \", _jsx(_components.em, {\n        children: \"Went\"\n      }), \" became \", _jsx(_components.em, {\n        children: \"go\"\n      }), \". \", _jsx(_components.em, {\n        children: \"Said\"\n      }), \" became \", _jsx(_components.em, {\n        children: \"say\"\n      }), \". And so on. As we said, this doesn't work only on verbs, but on all word forms.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One thing to note here. UDPipe has an internal tokenizer, that works with sentences instead of tokens. You can enable it by selecting UDPipe tokenizer option. What is the difference? A quicker version would be to tokenize all the words and just look up their lemma. But sometimes this can be wrong. Consider the sentence:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"I am wearing a tie to work.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now the word \", _jsx(_components.em, {\n        children: \"tie\"\n      }), \" is obviously a piece of clothing, which is indicated by the word wearing before it. But \", _jsx(_components.em, {\n        children: \"tie\"\n      }), \" alone can also be the verb \", _jsx(_components.em, {\n        children: \"to tie\"\n      }), \". So the UDPipe tokenizer will consider the entire sentence and correctly lemmatize this word, while lemmatization on regular tokens might not. While UDPipe works better, it is also slower, so you might want to work with regular tokenization to speed up the analysis.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.42.25.png\",\n        alt: \"\",\n        width: \"1600\",\n        height: \"1366\"\n      }), \"\\nIn Preprocess Text, you turn on the Normalization button on the right, then select UDPipe Lemmatizer and select the language you wish to use. Finally, if you wish to go with the better albeit slower UDPipe tokenizer, tick the UDPipe tokenizer box.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, UDPipe does not remove punctuation, so you might end up with words like \", _jsx(_components.em, {\n        children: \"rose.\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"away.\"\n      }), \", with the full stop at the end. This you can fix with using regular tokenization and also by select the Regex option in Filtering, which will remove pure punctuation.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-10-orange-now-speaks-50-languages/__optimized-images__/Screen-Shot-2018-10-04-at-13.26.31.png\",\n        alt: \"\",\n        width: \"838\",\n        height: \"444\"\n      }), \"\\nFinal workflow, where we compared the results of no normalization and UDPipe normalization in a word cloud.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is it. UDPipe contains lemmatization models for 50 languages and only when you click on a particular language in the Language option, will the resource be loaded, so your computer won't be flooded with models for languages you won't ever use. The installation of UDPipe could also be a little tricky, but after some initial obstacles, we have managed to prepare packages for both pip (OSX and Linux) and conda (Windows).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We hope you enjoy the new possibilities of a freshly multilingual Orange!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-now-speaks-50-languages"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 <a href="/blog/2011-06-orange-t-shirts/__optimized-images__/img_0986_1.jpg__160x160_q95_crop.jpg" data-gallery="true"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2011-06-orange-t-shirts/__optimized-images__/img_0986_1.jpg__160x160_q95_crop.jpg"/></a>
 <p>We will be selling them on the website soon for $15 (shipping costs included), but if you want to have one (or more) in advance, <a href="/contact/">drop us a line</a> and we will see what we can do. We have them for all you brave girls and boys and in different sizes.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>competition, prediction</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange team wins JRS 2012 Data Mining Competition</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 25, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Lead by Jure Žbontar, the team from University of Ljubljana wins over 126 other entrants in an <a href="http://tunedit.org/challenge/JRS12Contest?m=summary">international competition in predictive data analytics</a>.</p>
 <p>Jure’s team consisted of several Orange developers and computer science students: Miha Zidar, Blaž Zupan, Gregor Majcen, Marinka Žitnik in Matic Potočnik. To win, the team had to predict topics for 10.000 MedLine documents that were represented with over 25.000 algorithmically derived numerical features. Given was training set of another 10.000 documents in the same representation but each labeled with a set of topics. From the training set the task was to develop a model to predict labels for documents in the test set. A particular challenge was guessing the right number of topics to be associated with the documents, as these, at least in the training set, varied from one to a dozen.</p>
 <p><a href="http://tunedit.org/challenge/JRS12Contest">JRS 2012</a> is just one in a series of competitions recently organized on servers such as <a href="http://tunedit.org/">TunedIT</a> and <a href="http://www.kaggle.com/">Kaggle</a>. The price for winning was $1000 and a trip to <a href="http://sist.swjtu.edu.cn/JRS2012/">Joint Rough Set Symposium</a> in Chengdu, China, to present a winning strategy and developed data mining techiques.</p>
-<a href="/blog/2012-04-orange-team-wins-jrs-2012-data-mining-competition/__optimized-images__/2012-jrs-leaderboard-final.png__600x415_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="600" height="415" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2012-04-orange-team-wins-jrs-2012-data-mining-competition/__optimized-images__/2012-jrs-leaderboard-final.png__600x415_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2012-04-25 13:08:00+00:00","draft":false,"title":"Orange team wins JRS 2012 Data Mining Competition","blog":["competition","prediction"],"oldUrl":"/blog/2012/04/25/orange-team-wins-jrs-2012-data-mining-competition/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Lead by Jure Žbontar, the team from University of Ljubljana wins over 126 other entrants in an \", _jsx(_components.a, {\n        href: \"http://tunedit.org/challenge/JRS12Contest?m=summary\",\n        children: \"international competition in predictive data analytics\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Jure’s team consisted of several Orange developers and computer science students: Miha Zidar, Blaž Zupan, Gregor Majcen, Marinka Žitnik in Matic Potočnik. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Webinar for Educators #1: Where to Start?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 26, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><strong>On Thursday, May 26, 2022, at 16:00 CET, we will organize a webinar for educators who either use or are interested in using <a href="http://orangedatamining.com">Orange Data Mining</a> in data science training.</strong></p>
 <p><strong>The webinar will feature a short demo on using Orange for training in machine learning.</strong> We will showcase several data visualization and exploration widgets and show you some tricks that you could use in your training sessions. We will also present our GitHub repository with our openly-available Orange training material. In the second half of the webinar, we will address selected questions from the audience submitted on this form.</p>
 <p><strong>To enroll in the webinar, please fill out <a href="https://forms.gle/9Q4uYKFgTKhAYJwy5">an enrollment form</a>. We may limit the enrollment so please fill out the form as soon as possible, preferably before May 3rd!</strong> Three days before the webinar, we will email you the reminder with the webinar link.</p>
-<p>This is the first webinar in the planned Orange Webinar for Educators series.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-04-26","draft":false,"title":"Orange Webinar for Educators #1: Where to Start?","thumbImage":"2022-04-26-hands-on-workshop.png","frontPageImage":"2022-04-26-hands-on-workshop.png","blog":["orange","workshop"],"shortExcerpt":"We are announcing a forthcoming webinar for educators and trainers.","longExcerpt":"","x2images":false,"oldUrl":"/blog/2022/2022-04-26-edu-webinar/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsxs(_components.strong, {\n        children: [\"On Thursday, May 26, 2022, at 16:00 CET, we will organize a webinar for educators who either use or are interested in using \", _jsx(_components.a, {\n          href: \"http://orangedatamining.com\",\n          children: \"Orange Data Mining\"\n        }), \" in data science training.\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"The webinar will feature a short demo on using Orange for training in machine learning.\"\n      }), \" We will showcase several data visualization and exploration widgets and show you some tricks that you could use in your training sessions. 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+<p>This is the first webinar in the planned Orange Webinar for Educators series.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-04-26","draft":false,"title":"Orange Webinar for Educators #1: Where to Start?","thumbImage":"2022-04-26-hands-on-workshop.png","frontPageImage":"2022-04-26-hands-on-workshop.png","blog":["orange","workshop"],"shortExcerpt":"We are announcing a forthcoming webinar for educators and trainers.","longExcerpt":"","x2images":false,"oldUrl":"/blog/2022/2022-04-26-edu-webinar/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsxs(_components.strong, {\n        children: [\"On Thursday, May 26, 2022, at 16:00 CET, we will organize a webinar for educators who either use or are interested in using \", _jsx(_components.a, {\n          href: \"http://orangedatamining.com\",\n          children: \"Orange Data Mining\"\n        }), \" in data science training.\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"The webinar will feature a short demo on using Orange for training in machine learning.\"\n      }), \" We will showcase several data visualization and exploration widgets and show you some tricks that you could use in your training sessions. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, education, examples, infrared spectra, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange with Spectroscopy Add-on Workshop</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 28, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We have just concluded our enhanced Introduction to Data Science workshop, which included several workflows for spectroscopy analysis. <a href="https://www.tandfonline.com/doi/full/10.1080/08940886.2017.1338424?src=recsys">Spectroscopy add-on</a> is intended for the analysis of spectral data and it is just as fun as our other add-ons (if not more!).</p>
 <a href="/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/delavnica-spectroscopy.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="674" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/delavnica-spectroscopy.jpg"/></a>
 <p>We will prove it with a simple classification workflow. First, install Spectroscopy add-on from Options - Add-ons menu in Orange. Restart Orange for the add-on to appear. Great, you are ready for some spectral analysis!</p>
@@ -169,4 +169,4 @@
 <p>We will select the misclassified DNA cells and feed them to Spectra to inspect what went wrong. Instead of coloring by type, we will color by prediction from Logistic Regression. Can you find out why these spectra were classified incorrectly?</p>
 <a href="/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.52.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="932" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.52.png"/></a>
 <p>Misclassified DNA spectra colored by the prediction made by Logistic Regression.</p>
-<p>This is one of the simplest examples with spectral data. It is basically the same procedure as with standard data - data is fed as data, learner (LR) as learner and preprocessor as preprocessor directly to Test &amp; Score to avoid overfitting. Play around with Spectroscopy add-on and let us know what you think! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-03-28 10:36:18+00:00","draft":false,"title":"Orange with Spectroscopy Add-on Workshop","blog":["addons","education","examples","infrared spectra","workshop"],"oldUrl":"/blog/2018/03/28/orange-with-spectroscopy-add-on-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have just concluded our enhanced Introduction to Data Science workshop, which included several workflows for spectroscopy analysis. \", _jsx(_components.a, {\n        href: \"https://www.tandfonline.com/doi/full/10.1080/08940886.2017.1338424?src=recsys\",\n        children: \"Spectroscopy add-on\"\n      }), \" is intended for the analysis of spectral data and it is just as fun as our other add-ons (if not more!).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/delavnica-spectroscopy.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"674\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will prove it with a simple classification workflow. First, install Spectroscopy add-on from Options - Add-ons menu in Orange. Restart Orange for the add-on to appear. Great, you are ready for some spectral analysis!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Use Datasets widget and load \", _jsx(_components.em, {\n        children: \"Collagen spectroscopy\"\n      }), \" data. This data contains cells measured with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Fourier-transform_infrared_spectroscopy\",\n        children: \"FTIR\"\n      }), \" and annotated with the major chemical compound at the imaged part of a cell. A quick glance in a Data Table will give us an idea how the data looks like. Seems like a very standard spectral data set.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.54.14.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"763\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Collagen data set from Datasets widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we want to determine, whether we can classify cells by type based on their spectral profiles. First, connect Datasets to Test \u0026 Score. We will use 10-fold cross-validation to score the performance of our model. Next, we will add Logistic Regression to model the data. One final thing. Spectral data often needs some preprocessing. Let us perform a simple preprocessing step by applying Cut (keep) filter and retaining only the wave numbers between 1500 and 1800. When we connect it to Test \u0026 Score, we need to keep in mind to connect the Preprocessor output of Preprocess Spectra.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.28.png\",\n        alt: \"\",\n        width: \"1528\",\n        height: \"1206\"\n      }), \"\\nPreprocessor that keeps a part of the spectra cut between 1500 and 1800. No data is shown here, since we are using only the preprocessing procedure as the input for Test \u0026 Score.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.06.png\",\n      alt: \"\",\n      width: \"1478\",\n      height: \"448\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us see how well our model performs. Not bad. A 0.99 AUC score. Seems like it is almost perfect. But is it really so?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.39.png\",\n      alt: \"\",\n      width: \"1412\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"10-fold cross-validation on spectral data. Our AUC and CA scores are quite impressive.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Confusion Matrix gives us a detailed picture. Our model fails almost exclusively on DNA cell type. Interesting.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.44.png\",\n      alt: \"\",\n      width: \"1500\",\n      height: \"724\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Confusion Matrix shows DNA is most often misclassified. By selecting the misclassified instances in the matrix, we can inspect why Logistic Regression couldn't model these spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will select the misclassified DNA cells and feed them to Spectra to inspect what went wrong. Instead of coloring by type, we will color by prediction from Logistic Regression. Can you find out why these spectra were classified incorrectly?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.52.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"932\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Misclassified DNA spectra colored by the prediction made by Logistic Regression.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is one of the simplest examples with spectral data. It is basically the same procedure as with standard data - data is fed as data, learner (LR) as learner and preprocessor as preprocessor directly to Test \u0026 Score to avoid overfitting. Play around with Spectroscopy add-on and let us know what you think! :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-with-spectroscopy-add-on-workshop"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>This is one of the simplest examples with spectral data. It is basically the same procedure as with standard data - data is fed as data, learner (LR) as learner and preprocessor as preprocessor directly to Test &amp; Score to avoid overfitting. Play around with Spectroscopy add-on and let us know what you think! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-03-28 10:36:18+00:00","draft":false,"title":"Orange with Spectroscopy Add-on Workshop","blog":["addons","education","examples","infrared spectra","workshop"],"oldUrl":"/blog/2018/03/28/orange-with-spectroscopy-add-on-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We have just concluded our enhanced Introduction to Data Science workshop, which included several workflows for spectroscopy analysis. \", _jsx(_components.a, {\n        href: \"https://www.tandfonline.com/doi/full/10.1080/08940886.2017.1338424?src=recsys\",\n        children: \"Spectroscopy add-on\"\n      }), \" is intended for the analysis of spectral data and it is just as fun as our other add-ons (if not more!).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/delavnica-spectroscopy.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"674\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will prove it with a simple classification workflow. First, install Spectroscopy add-on from Options - Add-ons menu in Orange. Restart Orange for the add-on to appear. Great, you are ready for some spectral analysis!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Use Datasets widget and load \", _jsx(_components.em, {\n        children: \"Collagen spectroscopy\"\n      }), \" data. This data contains cells measured with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Fourier-transform_infrared_spectroscopy\",\n        children: \"FTIR\"\n      }), \" and annotated with the major chemical compound at the imaged part of a cell. A quick glance in a Data Table will give us an idea how the data looks like. Seems like a very standard spectral data set.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.54.14.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"763\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Collagen data set from Datasets widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we want to determine, whether we can classify cells by type based on their spectral profiles. First, connect Datasets to Test \u0026 Score. We will use 10-fold cross-validation to score the performance of our model. Next, we will add Logistic Regression to model the data. One final thing. Spectral data often needs some preprocessing. Let us perform a simple preprocessing step by applying Cut (keep) filter and retaining only the wave numbers between 1500 and 1800. When we connect it to Test \u0026 Score, we need to keep in mind to connect the Preprocessor output of Preprocess Spectra.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.img, {\n        src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.28.png\",\n        alt: \"\",\n        width: \"1528\",\n        height: \"1206\"\n      }), \"\\nPreprocessor that keeps a part of the spectra cut between 1500 and 1800. No data is shown here, since we are using only the preprocessing procedure as the input for Test \u0026 Score.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.06.png\",\n      alt: \"\",\n      width: \"1478\",\n      height: \"448\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us see how well our model performs. Not bad. A 0.99 AUC score. Seems like it is almost perfect. But is it really so?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.39.png\",\n      alt: \"\",\n      width: \"1412\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"10-fold cross-validation on spectral data. Our AUC and CA scores are quite impressive.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Confusion Matrix gives us a detailed picture. Our model fails almost exclusively on DNA cell type. Interesting.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.44.png\",\n      alt: \"\",\n      width: \"1500\",\n      height: \"724\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Confusion Matrix shows DNA is most often misclassified. By selecting the misclassified instances in the matrix, we can inspect why Logistic Regression couldn't model these spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will select the misclassified DNA cells and feed them to Spectra to inspect what went wrong. Instead of coloring by type, we will color by prediction from Logistic Regression. Can you find out why these spectra were classified incorrectly?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-03-orange-with-spectroscopy-add-on-workshop/__optimized-images__/Screen-Shot-2018-03-28-at-09.47.52.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"932\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Misclassified DNA spectra colored by the prediction made by Logistic Regression.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is one of the simplest examples with spectral data. It is basically the same procedure as with standard data - data is fed as data, learner (LR) as learner and preprocessor as preprocessor directly to Test \u0026 Score to avoid overfitting. Play around with Spectroscopy add-on and let us know what you think! :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-with-spectroscopy-add-on-workshop"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
diff --git a/blog/orange-workshops-around-the-world/index.html b/blog/orange-workshops-around-the-world/index.html
index 6de15224d..2688f6bbf 100644
--- a/blog/orange-workshops-around-the-world/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, bioinformatics, conference, tutorial</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange workshops around the world</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 19, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Even though the summer is nigh, we are hardly going to catch a summer break this year. Orange team is busy holding workshops around the world to present the latest widgets and data mining tools to the public. Last week we had a very successful tutorial at <a href="http://www.bc2.ch/2015/">[BC]2</a> in Basel, Switzerland, where <a href="https://www.facebook.com/marinka.zitnik?fref=ts">Marinka</a> and <a href="https://www.facebook.com/blazzupan.si?fref=ts">Blaž</a> presented data fusion. A part of the tutorial was a hands-on workshop with <a href="/blog/2015/06/05/data-fusion-add-on-for-orange/">Orange’s new add-on for data fusion</a>. Marinka also got an award for the <a href="http://helikoid.si/bc215/zitnik-zupan-bc215.png">poster</a>, where data fusion was used to hunt for <a href="https://en.wikipedia.org/wiki/Dictyostelium_discoideum"><em>Dictyostelium</em></a> bacterial-response genes. This week, we are in <a href="http://www.unipv.eu/site/home.html">Pavia, Italy</a>, also for <a href="https://sites.google.com/site/mcbiworkshop15/">Matrix Computations in Biomedical Informatics Workshop</a> at <a href="http://aime15.aimedicine.info/">AIME 2015</a>, a Conference on Artificial Intelligence in Medicine. During the workshop, we are giving an invited talk on learning latent factor models by data fusion and we’ll also show Orange’s data fusion add-on. Thanks to the workshop organizers, <a href="http://www.labmedinfo.org/people/bellazzi">Riccardo Bellazzi</a>, <a href="http://www.cse.gatech.edu/people/jimeng-sun">Jimeng Sun</a> and <a href="http://researcher.ibm.com/researcher/view.php?person=us-pzhang">Ping Zhang</a>, the workshop program looks great.</p>
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-<p>Blaž with Riccardo and John in Pavia, Italy</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-06-19 10:40:43+00:00","draft":false,"title":"Orange workshops around the world","blog":["addons","bioinformatics","conference","tutorial"],"oldUrl":"/blog/2015/06/19/435/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Even though the summer is nigh, we are hardly going to catch a summer break this year. 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+<p>Blaž with Riccardo and John in Pavia, Italy</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-06-19 10:40:43+00:00","draft":false,"title":"Orange workshops around the world","blog":["addons","bioinformatics","conference","tutorial"],"oldUrl":"/blog/2015/06/19/435/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Even though the summer is nigh, we are hardly going to catch a summer break this year. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics, education, embedding, orange3, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange Workshops: Luxembourg, Pavia, Ljubljana</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 06, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>February was a month of <a href="http://orange.biolab.si">Orange</a> workshops.</p>
 <h2>Ljubljana: Biologists</h2>
 <a href="/blog/2017-03-luxembourg-pavia-ljubljana/__optimized-images__/cost-ljubljana.jpg" data-gallery="true"><img alt="" loading="lazy" width="1024" height="678" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-03-luxembourg-pavia-ljubljana/__optimized-images__/cost-ljubljana.jpg"/></a>
@@ -164,4 +164,4 @@ <h2>Pavia: Engineers</h2>
 <p>About fifty engineers of all kinds at <a href="http://cht.unipv.it/index.php/events/introduction-to-data-science">University of Pavia</a>. Few undergrads, then mostly graduate students, some postdocs and even quite a few of the faculty staff have joined this two day course. It was a bit lighter that the one in Luxembourg, but also covered essentials of machine learning: data management, visualization and classification with quite some emphasis on overfitting on the first day, and then clustering and data projection on the second day. We finished with a showcase on image embedding and analysis. I have in particular enjoyed this last part of the workshop, where attendees were asked to grab a set of images and use <a href="http://orange.biolab.si">Orange</a> to find if they can cluster or classify them correctly. They were all kinds of images that they have gathered, like flowers, racing cars, guitars, photos from nature, you name it, and it was great to find that deep learning networks can be such good embedders, as most students found that machine learning on their image sets works surprisingly well.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/12/16/bdtn-2016-workshop-introduction-to-data-science">BDTN 2016 Workshop on introduction to data science</a></p>
 <p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2">Data mining course at Baylor College of Medicine</a></p>
-<p>We thank <a href="http://www.labmedinfo.org/people/bellazzi">Riccardo Bellazzi</a>, an organizer of Pavia course, for inviting us. Oh, yeah, the pizza at <a href="http://www.rossopomodoro.it/ristoranti/PAVIA/pavia-citta">Rossopommodoro</a> was great as always, though Michella&#x27;s pasta al pesto e piselli back at Riccardo&#x27;s home was even better.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-03-06 21:35:49+00:00","draft":false,"title":"Orange Workshops: Luxembourg, Pavia, Ljubljana","blog":["bioinformatics","education","embedding","orange3","workshop"],"oldUrl":"/blog/2017/03/06/luxembourg-pavia-ljubljana/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"February was a month of \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" workshops.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Ljubljana: Biologists\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-03-luxembourg-pavia-ljubljana/__optimized-images__/cost-ljubljana.jpg\",\n      alt: \"\",\n      width: \"1024\",\n      height: \"678\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We (Tomaž, Martin and I) have started in Ljubljana with a hands-on course for the \", _jsx(_components.a, {\n        href: \"http://conferences.nib.si/camo2017/\",\n        children: \"COST Action FA1405 Systems Biology Training School\"\n      }), \". 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+<p>We thank <a href="http://www.labmedinfo.org/people/bellazzi">Riccardo Bellazzi</a>, an organizer of Pavia course, for inviting us. Oh, yeah, the pizza at <a href="http://www.rossopomodoro.it/ristoranti/PAVIA/pavia-citta">Rossopommodoro</a> was great as always, though Michella&#x27;s pasta al pesto e piselli back at Riccardo&#x27;s home was even better.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-03-06 21:35:49+00:00","draft":false,"title":"Orange Workshops: Luxembourg, Pavia, Ljubljana","blog":["bioinformatics","education","embedding","orange3","workshop"],"oldUrl":"/blog/2017/03/06/luxembourg-pavia-ljubljana/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"February was a month of \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" workshops.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Ljubljana: Biologists\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-03-luxembourg-pavia-ljubljana/__optimized-images__/cost-ljubljana.jpg\",\n      alt: \"\",\n      width: \"1024\",\n      height: \"678\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We (Tomaž, Martin and I) have started in Ljubljana with a hands-on course for the \", _jsx(_components.a, {\n        href: \"http://conferences.nib.si/camo2017/\",\n        children: \"COST Action FA1405 Systems Biology Training School\"\n      }), \". 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diff --git a/blog/orange-you-going-to-ask-about-dask/index.html b/blog/orange-you-going-to-ask-about-dask/index.html
index 0e0823d42..d0ac1bd34 100644
--- a/blog/orange-you-going-to-ask-about-dask/index.html
+++ b/blog/orange-you-going-to-ask-about-dask/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>dask, widgets</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange you going to ask about dask?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Noah Novšak</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 13, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>As great as Orange is at simplifying machine learning and bringing it closer to your <a href="https://www.youtube.com/watch?v=5PsnxDQvQpw">everyday regular normal guy</a>, it turns out, there are some major drawbacks that stem from it&#x27;s very inception. This is because Orange is meant to be run on a laptop. A device with just a handful of CPU cores, and maybe a couple of gigabytes of RAM. Due to this ideology we have so far reasoned that nobody in their right mind would want to throw a very large dataset at it. So most of Orange&#x27;s components assume they can fit all the data into memory at once, process it on a single core, and expect results fairly quickly.</p>
 <p>You might see an issue here. What happens if I give Orange more data than my poor old laptop can handle? Well I&#x27;ll just say I&#x27;m very grateful <em>force quit</em> exists.</p>
 <p>The solution to our problem is <a href="https://www.dask.org">dask</a>. Another open-source library that enables efficient scaling of python code. As such, a project is currently under way, that aims to implement dask into Orange. So here I am today, to illustrate the changes that are coming to Orange in the near future.</p>
@@ -160,4 +160,4 @@
 <a href="/blog/2022-12-dask-table/__optimized-images__/2022-12-13_dask_chunking.png" data-gallery="true"><img alt="reading in chunks" loading="lazy" width="2481" height="1754" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2022-12-dask-table/__optimized-images__/2022-12-13_dask_chunking.png"/></a>
 <p>This approach does have another drawback though. Previously each element took an equal amount of time to read and display. Now we can quickly display everything from one chunk, but when we scroll past its edge and need to load a new one we have to wait a little while. The problem here is that until now we&#x27;ve been assuming that our data is read and displayed instantly. So each time we have to wait for a new chunk our program gets stuck. From the users perspective this means that instead of a smooth scrolling experience, we get more of a <em>stuttery</em> one. And that&#x27;s just very unpleasant.</p>
 <p>Luckily, there&#x27;s a simple solution for this one too. In order to achieve a smoother scroll I have to let the table continue scrolling past the edge of my chunk, even though I don&#x27;t know what it&#x27;s supposed to display yet. Instead I let it temporarily show some empty cells while I ask dask to read the next chunk for me in the <em>background</em>. Once dask is finished preparing my data I can refresh my table with the actual values.</p>
-<p>This aproach actually ends up working surprisingly well. Now I can (in theory) use Orange to display arbitrarily large datasets without unwanted crashes or obscenely long wait times. It was also amazingly simple to implement, due to how well dask integrates with existing numpy code.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2022-12-13","draft":false,"title":"Orange you going to ask about dask?","thumbImage":"2022-12-13_dask_data.png","frontPageImage":"2022-12-13_dask_data.png","blog":["dask","widgets"],"shortExcerpt":"A brief glimpse into the future of Orange and how we can effectively process large datasets with dask.","longExcerpt":"What we are doing to improve Orange's data processing abilities, how we can tackle the huge amounts of data already available today, and what role dask plays in all this.","oldUrl":"/blog/2022/2022-12-13-dask-table/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"As great as Orange is at simplifying machine learning and bringing it closer to your \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=5PsnxDQvQpw\",\n        children: \"everyday regular normal guy\"\n      }), \", it turns out, there are some major drawbacks that stem from it's very inception. 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+<p>This aproach actually ends up working surprisingly well. Now I can (in theory) use Orange to display arbitrarily large datasets without unwanted crashes or obscenely long wait times. It was also amazingly simple to implement, due to how well dask integrates with existing numpy code.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2022-12-13","draft":false,"title":"Orange you going to ask about dask?","thumbImage":"2022-12-13_dask_data.png","frontPageImage":"2022-12-13_dask_data.png","blog":["dask","widgets"],"shortExcerpt":"A brief glimpse into the future of Orange and how we can effectively process large datasets with dask.","longExcerpt":"What we are doing to improve Orange's data processing abilities, how we can tackle the huge amounts of data already available today, and what role dask plays in all this.","oldUrl":"/blog/2022/2022-12-13-dask-table/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"As great as Orange is at simplifying machine learning and bringing it closer to your \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/watch?v=5PsnxDQvQpw\",\n        children: \"everyday regular normal guy\"\n      }), \", it turns out, there are some major drawbacks that stem from it's very inception. This is because Orange is meant to be run on a laptop. A device with just a handful of CPU cores, and maybe a couple of gigabytes of RAM. Due to this ideology we have so far reasoned that nobody in their right mind would want to throw a very large dataset at it. So most of Orange's components assume they can fit all the data into memory at once, process it on a single core, and expect results fairly quickly.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You might see an issue here. What happens if I give Orange more data than my poor old laptop can handle? Well I'll just say I'm very grateful \", _jsx(_components.em, {\n        children: \"force quit\"\n      }), \" exists.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The solution to our problem is \", _jsx(_components.a, {\n        href: \"https://www.dask.org\",\n        children: \"dask\"\n      }), \". Another open-source library that enables efficient scaling of python code. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, data, examples, orange3, tutorial, youtube</strong></p><h1 class="sc-c67a4900-0 loxVcf">Orange YouTube Tutorials</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 04, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It&#x27;s been a long time coming, but finally we&#x27;ve created out our first set of YouTube tutorials. In a series &#x27;Getting Started with Orange&#x27; we will walk through our software step-by-step. You will learn how to create a workflow, load your data in different formats, visualize and explore the data. These tutorials are meant for complete beginners in both Orange and data mining and come with some handy tricks that will make using Orange very easy. Below are the first three videos from this series, more are coming in the following weeks.</p>
 <div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/HXjnDIgGDuI" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div>
 <div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/lb-x36xqJ-E" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div>
 <div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/2xS6QjnG714" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div>
 <p>We are also preparing a series called &#x27;Data Science with Orange&#x27;, which will take you on a journey through the world of data mining and machine learning by explaining predictive modeling, classification, regression, model evaluation and much more.</p>
-<p>Feel free to let us know what tutorials you&#x27;d like to see and we&#x27;ll do our best to include it in one of the two series. :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-01-04 08:09:24+00:00","draft":false,"title":"Orange YouTube Tutorials","blog":["analysis","data","examples","orange3","tutorial","youtube"],"oldUrl":"/blog/2016/01/04/orange-youtube-tutorials/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It's been a long time coming, but finally we've created out our first set of YouTube tutorials. In a series 'Getting Started with Orange' we will walk through our software step-by-step. You will learn how to create a workflow, load your data in different formats, visualize and explore the data. These tutorials are meant for complete beginners in both Orange and data mining and come with some handy tricks that will make using Orange very easy. Below are the first three videos from this series, more are coming in the following weeks.\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"HXjnDIgGDuI\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"lb-x36xqJ-E\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"2xS6QjnG714\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are also preparing a series called 'Data Science with Orange', which will take you on a journey through the world of data mining and machine learning by explaining predictive modeling, classification, regression, model evaluation and much more.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Feel free to let us know what tutorials you'd like to see and we'll do our best to include it in one of the two series. :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-youtube-tutorials"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Feel free to let us know what tutorials you&#x27;d like to see and we&#x27;ll do our best to include it in one of the two series. :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-01-04 08:09:24+00:00","draft":false,"title":"Orange YouTube Tutorials","blog":["analysis","data","examples","orange3","tutorial","youtube"],"oldUrl":"/blog/2016/01/04/orange-youtube-tutorials/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It's been a long time coming, but finally we've created out our first set of YouTube tutorials. In a series 'Getting Started with Orange' we will walk through our software step-by-step. You will learn how to create a workflow, load your data in different formats, visualize and explore the data. These tutorials are meant for complete beginners in both Orange and data mining and come with some handy tricks that will make using Orange very easy. Below are the first three videos from this series, more are coming in the following weeks.\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"HXjnDIgGDuI\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"lb-x36xqJ-E\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"2xS6QjnG714\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are also preparing a series called 'Data Science with Orange', which will take you on a journey through the world of data mining and machine learning by explaining predictive modeling, classification, regression, model evaluation and much more.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Feel free to let us know what tutorials you'd like to see and we'll do our best to include it in one of the two series. :)\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"orange-youtube-tutorials"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>T-shirt, survival analysis, tutorial</strong></p><h1 class="sc-c67a4900-0 loxVcf">Our Christmas Present: Free Orange T-Shirt!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Jaka Kokošar</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 20, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Indeed, we are giving away free Orange T-shirts. The T-shirt comes with another free item, our tutorial for survival analysis. Actually, the right order of tasks for you, data enthusiast, is to:</p>
 <ul>
 <li>Enroll in the <a href="https://books.biolab.si/books/survival-analysis">tutorial</a>.</li>
@@ -166,4 +166,4 @@
 <p>By participating in this tutorial, you&#x27;ll not only improve your data science skills but also play an important role in helping us improve our educational materials. As you go through the tutorial, we invite you to share your thoughts via brief questionnaires.</p>
 <p>Ready to dive in? Access the tutorial <a href="https://books.biolab.si/books/survival-analysis">here</a> and join our community on <a href="https://discord.gg/PENnsUhp8k">Discord</a> for any questions or further discussions. Please use the <strong>tutorials channel</strong>.</p>
 <p>Happy solving the tutorial and quizzes, we hope to send you the T-shirt soon. 🙂</p>
-<a href="/blog/2023-12-survival-analysis-tutorial/__optimized-images__/t-shirt.png" data-gallery="true"><img alt="" loading="lazy" width="2000" height="2000" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-12-survival-analysis-tutorial/__optimized-images__/t-shirt.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Jaka Kokošar","date":"2023-12-20","draft":false,"title":"Our Christmas Present: Free Orange T-Shirt!","url":"our-christmas-present-free-orange-t-shirt","thumbImage":"t-shirt.png","frontPageImage":"t-shirt.png","blog":["T-shirt","survival analysis","tutorial"],"shortExcerpt":"Enroll in our survival analysis tutorial, and you might just unwrap a free Orange T-shirt along with a hefty dose of data science cheer!","longExcerpt":"Enroll in our survival analysis tutorial, and you might just unwrap a free Orange T-shirt along with a hefty dose of data science cheer!"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Indeed, we are giving away free Orange T-shirts. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, images, interactive data visualization, orange3, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Outliers in Traffic Signs</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 25, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Say I am given a collection of images of traffic signs, and would like to find which signs stick out. That is, which traffic signs look substantially different from the others. I would assume that the traffic signs are not equally important and that some were designed to be noted before the others.</p>
 <p>I have assembled a small set of regulatory and warning traffic signs and stored the references to their images in a <a href="http://file.biolab.si/datasets/traffic-signs-rw.tab">traffic-signs-w.tab</a> data set.</p>
 <p><strong>Related:</strong> <a href="/blog/2014/04/29/viewing-images/">Viewing images</a></p>
@@ -165,4 +165,4 @@
 <p><strong>Related:</strong> <a href="/blog/2016/03/23/all-i-see-is-silhouette/">All I see is silhouette</a></p>
 <p>Our final workflow, with selection of three biggest outliers (we used shift-click to select its corresponding silhouettes in the Silhouette Plot), is:</p>
 <a href="/blog/2017-04-outliers-in-traffic-signs/__optimized-images__/Screen-Shot-2017-04-25-at-10.08.56.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="871" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-04-outliers-in-traffic-signs/__optimized-images__/Screen-Shot-2017-04-25-at-10.08.56.png"/></a>
-<p>Isn&#x27;t this great? Turns out that traffic signs were carefully designed, such that the three outliers are indeed the signs we should never miss. It is great that we can now reconfirm this design choice by deep learning-based embedding and by using some straightforward machine learning tricks such as Silhouette Plot.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-04-25 07:35:38+00:00","draft":false,"title":"Outliers in Traffic Signs","blog":["addons","analysis","images","interactive data visualization","orange3","visualization"],"oldUrl":"/blog/2017/04/25/outliers-in-traffic-signs/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Say I am given a collection of images of traffic signs, and would like to find which signs stick out. 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+<p>Isn&#x27;t this great? Turns out that traffic signs were carefully designed, such that the three outliers are indeed the signs we should never miss. It is great that we can now reconfirm this design choice by deep learning-based embedding and by using some straightforward machine learning tricks such as Silhouette Plot.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2017-04-25 07:35:38+00:00","draft":false,"title":"Outliers in Traffic Signs","blog":["addons","analysis","images","interactive data visualization","orange3","visualization"],"oldUrl":"/blog/2017/04/25/outliers-in-traffic-signs/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Say I am given a collection of images of traffic signs, and would like to find which signs stick out. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Overfitting and Regularization</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Overfitting and Regularization"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-03-12 16:48:38+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="education"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="overfitting"/><meta property="article:tag" content="plot"/><meta property="article:tag" content="regression"/><meta property="article:tag" content="tutorial"/><meta name="next-head-count" content="24"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, education, examples, orange3, overfitting, plot, regression, tutorial</strong></p><h1 class="sc-c67a4900-0 loxVcf">Overfitting and Regularization</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 12, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>A week ago I used <a href="http://orange.biolab.si">Orange</a> to explain the effects of regularization. This was the second lecture in the Data Mining class, the first one was on linear regression. My introduction to the benefits of regularization used a simple data set with a single input attribute and a continuous class. I drew a data set in Orange, and then used Polynomial Regression widget (from Prototypes add-on) to plot the linear fit. This widget can also expand the data set by adding columns with powers of original attribute x, thereby augmenting the training set with x^p, where x is our original attribute and p an integer going from 2 to K. The polynomial expansion of data sets allows linear regression model to nicely fit the data, and with higher K to overfit it to extreme, especially if the number of data points in the training set is low.</p>
 <a href="/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-overfit.png" data-gallery="true"><img alt="" loading="lazy" width="899" height="711" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-overfit.png"/></a>
 <p>We have already <a href="/blog/2015/10/02/a-visit-from-the-tilburg-university/">blogged</a> about this experiment a while ago, showing that it is easy to see that linear regression coefficients blow out of proportion with increasing K. This leads to the idea that linear regression should not only minimize the squared error when predicting the value of dependent variable in the training set, but also keep model coefficients low, or better, penalize any high value of coefficients. This procedure is called regularization. Based on the type of penalty (sum of coefficient squared or sum of absolute values), the regularization is referred to L1 or L2, or, ridge and lasso regression.</p>
@@ -172,4 +172,4 @@
 		8, 		 0.001, 	 5.734
 		9, 		 0.000, 	 4.776
 </code></pre>
-<p>That&#x27;s it. For the class of computer scientists, one may do all this in scripting, but for any other audience, or for any introductory lesson, explaining of regularization with Orange widgets is a lot of fun.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-03-12 16:48:38+00:00","draft":false,"title":"Overfitting and Regularization","blog":["analysis","education","examples","orange3","overfitting","plot","regression","tutorial"],"oldUrl":"/blog/2016/03/12/overfitting-and-regularization/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"A week ago I used \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" to explain the effects of regularization. This was the second lecture in the Data Mining class, the first one was on linear regression. My introduction to the benefits of regularization used a simple data set with a single input attribute and a continuous class. I drew a data set in Orange, and then used Polynomial Regression widget (from Prototypes add-on) to plot the linear fit. This widget can also expand the data set by adding columns with powers of original attribute x, thereby augmenting the training set with x^p, where x is our original attribute and p an integer going from 2 to K. 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This workflow can be used to show that the regularized models less overfit the data, and that the overfitting depends on the regularization coefficient which governs the degree of penalty stemming from the value of coefficients of the linear model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-l2.png\",\n      alt: \"\",\n      width: \"1027\",\n      height: \"853\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I also use this workflow to show the difference between L1 and L2 regularization. The change of the type of regularization is most pronounced in the table of coefficients (Data Table widget), where with L1 regularization it is clear that this procedure results in many of those being 0. Try this with high value for degree of polynomial expansion, and a data set with about 10 data points. 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+<p>That&#x27;s it. For the class of computer scientists, one may do all this in scripting, but for any other audience, or for any introductory lesson, explaining of regularization with Orange widgets is a lot of fun.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-03-12 16:48:38+00:00","draft":false,"title":"Overfitting and Regularization","blog":["analysis","education","examples","orange3","overfitting","plot","regression","tutorial"],"oldUrl":"/blog/2016/03/12/overfitting-and-regularization/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"A week ago I used \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \" to explain the effects of regularization. This was the second lecture in the Data Mining class, the first one was on linear regression. My introduction to the benefits of regularization used a simple data set with a single input attribute and a continuous class. I drew a data set in Orange, and then used Polynomial Regression widget (from Prototypes add-on) to plot the linear fit. This widget can also expand the data set by adding columns with powers of original attribute x, thereby augmenting the training set with x^p, where x is our original attribute and p an integer going from 2 to K. The polynomial expansion of data sets allows linear regression model to nicely fit the data, and with higher K to overfit it to extreme, especially if the number of data points in the training set is low.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-overfit.png\",\n      alt: \"\",\n      width: \"899\",\n      height: \"711\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We have already \", _jsx(_components.a, {\n        href: \"/blog/2015/10/02/a-visit-from-the-tilburg-university/\",\n        children: \"blogged\"\n      }), \" about this experiment a while ago, showing that it is easy to see that linear regression coefficients blow out of proportion with increasing K. This leads to the idea that linear regression should not only minimize the squared error when predicting the value of dependent variable in the training set, but also keep model coefficients low, or better, penalize any high value of coefficients. This procedure is called regularization. Based on the type of penalty (sum of coefficient squared or sum of absolute values), the regularization is referred to L1 or L2, or, ridge and lasso regression.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It is quite easy to play with regularized models in Orange by attaching a Linear Regression widget to Polynomial Regression, in this way substituting the default model used in Polynomial Regression with the one designed in Linear Regression widget. This makes available different kinds of regularization. This workflow can be used to show that the regularized models less overfit the data, and that the overfitting depends on the regularization coefficient which governs the degree of penalty stemming from the value of coefficients of the linear model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-l2.png\",\n      alt: \"\",\n      width: \"1027\",\n      height: \"853\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I also use this workflow to show the difference between L1 and L2 regularization. The change of the type of regularization is most pronounced in the table of coefficients (Data Table widget), where with L1 regularization it is clear that this procedure results in many of those being 0. Try this with high value for degree of polynomial expansion, and a data set with about 10 data points. Also, try changing the regularization regularization strength (Linear Regression widget).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-l1.png\",\n      alt: \"\",\n      width: \"816\",\n      height: \"449\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"While the effects of overfitting and regularization are nicely visible in the plot in Polynomial Regression widget, machine learning models are really about predictions. And the quality of predictions should really be estimated on independent test set. So at this stage of the lecture I needed to introduce the model scoring, that is, a measure that tells me how well my model inferred on the training set performs on the test set. For simplicity, I chose to introduce root mean squared error (RMSE) and then crafted the following workflow.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-03-overfitting-and-regularization/__optimized-images__/poly-evaluate.png\",\n      alt: \"\",\n      width: \"736\",\n      height: \"829\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here, I draw the data set (Paint Data, about 20 data instances), assigned y as the target variable (Select Columns), split the data to training and test sets of approximately equal sizes (Data Sampler), and pass training and test data and linear model to the Test \u0026 Score widget. Then I can use linear regression with no regularization, and expect how RMSE changes with changing the degree of the polynomial. I can alternate between Test on train data and Test on test data (Test \u0026 Score widget). In the class I have used the blackboard to record this dependency. For the data from the figure, I got the following table:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"\\t\\tPoly K, RMSE Train, RMSE Test\\n\\t\\t0,       0.147,      0.138\\n\\t\\t1, \\t\\t 0.155, \\t 0.192\\n\\t\\t2, \\t\\t 0.049, \\t 0.063\\n\\t\\t3, \\t\\t 0.049, \\t 0.063\\n\\t\\t4, \\t\\t 0.049, \\t 0.067\\n\\t\\t5, \\t\\t 0.040, \\t 0.408\\n\\t\\t6, \\t\\t 0.040, \\t 0.574\\n\\t\\t7, \\t\\t 0.033, \\t 2.681\\n\\t\\t8, \\t\\t 0.001, \\t 5.734\\n\\t\\t9, \\t\\t 0.000, \\t 4.776\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"That's it. For the class of computer scientists, one may do all this in scripting, but for any other audience, or for any introductory lesson, explaining of regularization with Orange widgets is a lot of fun.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"overfitting-and-regularization"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Paint Your Data</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Paint Your Data"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2013-12-20 20:19:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="data"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Paint Your Data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 20, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>One of the widgets I enjoy very much when teaching introductory course in data mining is the <a href="/widget-catalog/data/paintdata/">Paint Data</a> widget. When painting in this widget I would intentionally include some clusters, or intentionally obscure them. Or draw them in any strange shape. Then I would discuss with students if these clusters are identified by <a href="/widget-catalog/unsupervised/kmeans/">k-means clustering</a> or by hierarchical clustering. We would also discuss automatic scoring of the quality of clusters, come up with the idea of a <a href="http://en.wikipedia.org/wiki/Silhouette_(clustering)">silhouette</a> (ok, already invented, but helps if you get this idea on your own as well). And then we would play with various data sets and clustering techniques and their parameters in Orange.</p>
 <p>Like in the following workflow where I drew three clusters which were indeed recognized by <a href="/widget-catalog/unsupervised/kmeans/">k-means clustering</a>. Notice that silhouette scoring correctly identified even the number of clusters. And I also drew the clustered data in the Scatterplot to check if the clusters are indeed where they should be.</p>
 <a href="/blog/2013-12-paint-your-data/__optimized-images__/paintdata-k-means-ok_1.png__600x1000_q95_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="600" height="559" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-12-paint-your-data/__optimized-images__/paintdata-k-means-ok_1.png__600x1000_q95_upscale.png"/></a>
@@ -157,4 +157,4 @@
 <a href="/blog/2013-12-paint-your-data/__optimized-images__/paintdata-k-means-notok.png__600x1000_q95_upscale.jpg" data-gallery="true"><img alt="" loading="lazy" width="600" height="559" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-12-paint-your-data/__optimized-images__/paintdata-k-means-notok.png__600x1000_q95_upscale.jpg"/></a>
 <p><a href="/widget-catalog/data/paintdata/">Paint Data</a> can also be used in supervised setting, for classification tasks. We can set the intended number of classes, and then chose any of these to paint the data. Below I have used it to create the datasets to check the behavior of several classifiers.</p>
 <a href="/blog/2013-12-paint-your-data/__optimized-images__/paintdata-supervised_1.png__600x1000_q95_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="600" height="690" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-12-paint-your-data/__optimized-images__/paintdata-supervised_1.png__600x1000_q95_upscale.png"/></a>
-<p>There are tons of other workflows where <a href="/widget-catalog/data/paintdata/">Paint Data</a> can be useful. Give it a try!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-12-20 20:19:00+00:00","draft":false,"title":"Paint Your Data","blog":["data","visualization"],"oldUrl":"/blog/2013/12/20/paint-your-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"One of the widgets I enjoy very much when teaching introductory course in data mining is the \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/paintdata/\",\n        children: \"Paint Data\"\n      }), \" widget. 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+<p>There are tons of other workflows where <a href="/widget-catalog/data/paintdata/">Paint Data</a> can be useful. Give it a try!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-12-20 20:19:00+00:00","draft":false,"title":"Paint Your Data","blog":["data","visualization"],"oldUrl":"/blog/2013/12/20/paint-your-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"One of the widgets I enjoy very much when teaching introductory course in data mining is the \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/paintdata/\",\n        children: \"Paint Data\"\n      }), \" widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>parallelization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Parallel Orange?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 03, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We attended a <a href="http://nips.cc/">NIPS 2011</a> workshop on <a href="http://biglearn.org/">processing and learning from large scale data</a>. Various presenters showed different tools and frameworks that can be used when developing algorithms suitable for dealing with large scale data, but none of them were written in Python and as such, not useful for Orange. We have been looking for a framework that would help us run code in parallel for some time, but so far with no luck.</p>
-<p>We would like to have a framework that is easy to use, can be used in C as well as in Python and supports multi-level map reduce (cross validation can be viewed as map reduce and random forest that is tested is another map-reduce). Prototypes we have created so far solve this problem by inspecting learners that are used in cross-validation and creating all &quot;subtasks&quot; at the same time. That results in really ugly code we don&#x27;t want to commit ;). If you know a framework that would suit our needs, want to implement support for parallel computation by yourself (we will apply to <a href="https://code.google.com/soc/">GSoC</a>) or have an idea how to solve this problem, feel free to contact us ;).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-01-03 08:59:00+00:00","draft":false,"title":"Parallel Orange?","blog":["parallelization"],"oldUrl":"/blog/2012/01/03/parallel-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We attended a \", _jsx(_components.a, {\n        href: \"http://nips.cc/\",\n        children: \"NIPS 2011\"\n      }), \" workshop on \", _jsx(_components.a, {\n        href: \"http://biglearn.org/\",\n        children: \"processing and learning from large scale data\"\n      }), \". 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+<p>We would like to have a framework that is easy to use, can be used in C as well as in Python and supports multi-level map reduce (cross validation can be viewed as map reduce and random forest that is tested is another map-reduce). Prototypes we have created so far solve this problem by inspecting learners that are used in cross-validation and creating all &quot;subtasks&quot; at the same time. That results in really ugly code we don&#x27;t want to commit ;). If you know a framework that would suit our needs, want to implement support for parallel computation by yourself (we will apply to <a href="https://code.google.com/soc/">GSoC</a>) or have an idea how to solve this problem, feel free to contact us ;).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-01-03 08:59:00+00:00","draft":false,"title":"Parallel Orange?","blog":["parallelization"],"oldUrl":"/blog/2012/01/03/parallel-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We attended a \", _jsx(_components.a, {\n        href: \"http://nips.cc/\",\n        children: \"NIPS 2011\"\n      }), \" workshop on \", _jsx(_components.a, {\n        href: \"http://biglearn.org/\",\n        children: \"processing and learning from large scale data\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>embeddding, PCA, dimensionality reduction, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">PCA vs. MDS vs. t-SNE</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaž Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 17, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>I recently enjoy studying and showing differences between t-SNE and other data embedding and projection techniques. In particular, in some recent hands-on courses, we often introduce data visualization by principal component analysis, multidimensional scaling, and t-SNE. We would start with the zoo data set, where the data set is smaller, and the difference are less pronounced. We then traverse through employee attrition data set with exciting clusters exposed in t-SNE. To finish, and especially for the academic audience, we then show and compare the three different dimensionality reduction techniques on data from single-cell gene expression data. There, t-SNE discovers clusters of same-type cells, while PCA and MDA fail to expose interesting data structures.</p>
 <a href="/blog/2021-06-pca-mds-tsne/__optimized-images__/2021-06-17-mds-and-tsne.png" data-gallery="true"><img alt="" loading="lazy" width="880" height="920" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-06-pca-mds-tsne/__optimized-images__/2021-06-17-mds-and-tsne.png"/></a>
 <p>All the data sets I have mentioned above are available in Orange through the Dataset widget.</p>
 <p>While all the three methods aim to reduce the dimensionality of the data, their goal is different. The principal component analysis seeks to preserve the variance in the data. When we use it to construct a two-dimensional projection, it finds the projection plane were the most spread data. Multidimensional scaling aims to preserve the distances between pairs of data points, focusing on pairs of distant points in the original space. Differently, t-SNE focuses on maintaining neighborhood data points. Data points that are close in the original data space will be tight in the t-SNE embeddings.</p>
-<p>Interestingly, MDS and PCA visualizations bear many similarities, while t-SNE embeddings are pretty different. We use t-SNE to expose the clustering structure, MDS when global relations matter, and PCA as a preprocessing technique to reduce dimensionality and remove noise.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2021-06-17","draft":false,"title":"PCA vs. MDS vs. t-SNE","thumbImage":"2021-06-17-tsne.png","frontPageImage":"2021-06-17-tsne.png","blog":["embeddding","PCA","dimensionality reduction","workshop"],"shortExcerpt":"Oh, the joy and variety of data embedding and projection techniques!","longExcerpt":"Oh, the joy and variety of data embedding and projection techniques!","x2images":false,"oldUrl":"/blog/2021/2021-06-17-pca-mds-tsne/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"I recently enjoy studying and showing differences between t-SNE and other data embedding and projection techniques. 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+<p>Interestingly, MDS and PCA visualizations bear many similarities, while t-SNE embeddings are pretty different. We use t-SNE to expose the clustering structure, MDS when global relations matter, and PCA as a preprocessing technique to reduce dimensionality and remove noise.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2021-06-17","draft":false,"title":"PCA vs. MDS vs. t-SNE","thumbImage":"2021-06-17-tsne.png","frontPageImage":"2021-06-17-tsne.png","blog":["embeddding","PCA","dimensionality reduction","workshop"],"shortExcerpt":"Oh, the joy and variety of data embedding and projection techniques!","longExcerpt":"Oh, the joy and variety of data embedding and projection techniques!","x2images":false,"oldUrl":"/blog/2021/2021-06-17-pca-mds-tsne/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"I recently enjoy studying and showing differences between t-SNE and other data embedding and projection techniques. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange, interaction, addons</strong></p><h1 class="sc-c67a4900-0 loxVcf">Predictive Modelling with Attribute Interactions</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Noah Novšak</strong></p><p class="sc-b9a2839b-4 ehbERY">May 13, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The <em>Interactions</em> widget is one of the newest additions to Orange. Previously only available in Orange 2, it has been rewritten and is accessible in the prototype add-on. This way, one need not go through the trouble of <a href="/blog/2022/2022-01-10-orange2/">compiling older versions</a> anymore.</p>
 <p>But what does it do?</p>
 <p>It computes and displays the <a href="http://stat.columbia.edu/~jakulin/Int/">interaction</a> between attributes by calculating the mutual information between them and a third target variable. Doing so provides insight into the data at hand and aids in the search for better visualizations and predictive models.</p>
@@ -163,4 +163,4 @@
 <p>While neither <code>a</code> nor <code>b</code> carries much information independently, their combination tells a great deal about our target variable. With this in mind, we can now use the <a href="/widget-catalog/transform/featureconstructor/">Feature Constructor</a> widget to combine attributes <code>a</code> and <code>b</code> into a single feature and retrain our model. Applying all these steps then yields a workflow resembling this.</p>
 <a href="/blog/2022-04-interactions/__optimized-images__/2022-05-06-interactions-updated-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="1058" height="458" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-04-interactions/__optimized-images__/2022-05-06-interactions-updated-workflow.png"/></a>
 <p>Lo and behold! It looks like the extra trouble has paid off. We have managed to improve our model&#x27;s performance and have ended up with an AUC score of <code>1.0</code> by accounting for the codependence of variables (something models such as NBC lack by definition).</p>
-<p>I hope this short display sheds some light on all the possibilities interaction analysis provides and urge you to try it out on some real-world data.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2022-05-13","draft":false,"title":"Predictive Modelling with Attribute Interactions","thumbImage":"2022-05-06-interactions.png","frontPageImage":"2022-05-06-interactions.png","blog":["orange","interaction","addons"],"shortExcerpt":"A quick introduction to using the new Interactions widget in Orange 3","longExcerpt":"The Interactions widget has been added back to Orange 3. Illustrating how to use attribute interactions to improve predictive models.","x2images":true,"oldUrl":"/blog/2022/2022-04-08-interactions/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.em, {\n        children: \"Interactions\"\n      }), \" widget is one of the newest additions to Orange. Previously only available in Orange 2, it has been rewritten and is accessible in the prototype add-on. This way, one need not go through the trouble of \", _jsx(_components.a, {\n        href: \"/blog/2022/2022-01-10-orange2/\",\n        children: \"compiling older versions\"\n      }), \" anymore.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But what does it do?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It computes and displays the \", _jsx(_components.a, {\n        href: \"http://stat.columbia.edu/~jakulin/Int/\",\n        children: \"interaction\"\n      }), \" between attributes by calculating the mutual information between them and a third target variable. Doing so provides insight into the data at hand and aids in the search for better visualizations and predictive models.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As far as visualizations go, consider \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/scatterplot/\",\n        children: \"Scatter Plot\"\n      }), \". Connecting Interactions to the \", _jsx(_components.em, {\n        children: \"Features\"\n      }), \" input allows the user to manually select the projection, similarly to the \", _jsx(_components.em, {\n        children: \"find informative projections\"\n      }), \" button, but with a little more oomph. Enabling sorting, filtering, attribute selection, and offering a clear view into, where the information is coming from, be it from one single feature or a specific combination.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Another, possibly even more intriguing application would be, as mentioned, improving the performance of prediction models. To illustrate, let's take a look at the \", _jsx(_components.em, {\n        children: \"MONK\"\n      }), \" dataset, readily available directly within Orange, in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/datasets/\",\n        children: \"Datasets\"\n      }), \" widget. 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\ No newline at end of file
+<p>I hope this short display sheds some light on all the possibilities interaction analysis provides and urge you to try it out on some real-world data.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Noah Novšak","date":"2022-05-13","draft":false,"title":"Predictive Modelling with Attribute Interactions","thumbImage":"2022-05-06-interactions.png","frontPageImage":"2022-05-06-interactions.png","blog":["orange","interaction","addons"],"shortExcerpt":"A quick introduction to using the new Interactions widget in Orange 3","longExcerpt":"The Interactions widget has been added back to Orange 3. Illustrating how to use attribute interactions to improve predictive models.","x2images":true,"oldUrl":"/blog/2022/2022-04-08-interactions/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.em, {\n        children: \"Interactions\"\n      }), \" widget is one of the newest additions to Orange. Previously only available in Orange 2, it has been rewritten and is accessible in the prototype add-on. This way, one need not go through the trouble of \", _jsx(_components.a, {\n        href: \"/blog/2022/2022-01-10-orange2/\",\n        children: \"compiling older versions\"\n      }), \" anymore.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But what does it do?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It computes and displays the \", _jsx(_components.a, {\n        href: \"http://stat.columbia.edu/~jakulin/Int/\",\n        children: \"interaction\"\n      }), \" between attributes by calculating the mutual information between them and a third target variable. Doing so provides insight into the data at hand and aids in the search for better visualizations and predictive models.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As far as visualizations go, consider \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/scatterplot/\",\n        children: \"Scatter Plot\"\n      }), \". Connecting Interactions to the \", _jsx(_components.em, {\n        children: \"Features\"\n      }), \" input allows the user to manually select the projection, similarly to the \", _jsx(_components.em, {\n        children: \"find informative projections\"\n      }), \" button, but with a little more oomph. Enabling sorting, filtering, attribute selection, and offering a clear view into, where the information is coming from, be it from one single feature or a specific combination.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Another, possibly even more intriguing application would be, as mentioned, improving the performance of prediction models. To illustrate, let's take a look at the \", _jsx(_components.em, {\n        children: \"MONK\"\n      }), \" dataset, readily available directly within Orange, in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/datasets/\",\n        children: \"Datasets\"\n      }), \" widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, data, dataloading, examples, python, scripting</strong></p><h1 class="sc-c67a4900-0 loxVcf">Preparing Scraped Data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 23, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>One of the key questions of every data analysis is how to get the data and put it in the right form(at). In this post I&#x27;ll show you how to easily get the data from the web and transfer it to a file Orange can read.</p>
 <p><strong>Related:</strong> <a href="/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/">Creating a new data table in Orange through Python</a></p>
 <p>First, we&#x27;ll have to do some scripting. We&#x27;ll use a couple of Python libraries - <a href="https://docs.python.org/3.5/library/urllib.request.html">urllib.requests</a> fetching the data, <a href="https://www.crummy.com/software/BeautifulSoup/bs4/doc/">BeautifulSoup</a> for reading it, <a href="https://docs.python.org/3.5/library/csv.html">csv</a> for writing it and <a href="https://docs.python.org/3.5/library/re.html">regular expressions</a> for extracting the right data.</p>
@@ -226,4 +226,4 @@
 <p>Everything looks ok. We can use <a href="https://github.com/biolab/orange3-timeseries">Timeseries add-on</a> to inspect how many blogs we&#x27;ve written each month since 2010. Connect <strong>As Timeseries</strong> widget to File. Orange will automatically suggest to use Date as our time variable. Finally, we&#x27;ll plot the data with <strong>Line Chart</strong>. This is the curve of our blog activity.</p>
 <a href="/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.54.53.png" data-gallery="true"><img alt="" loading="lazy" width="2222" height="1274" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.54.53.png"/></a>
 <a href="/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.55.14.png" data-gallery="true"><img alt="" loading="lazy" width="650" height="374" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.55.14.png"/></a>
-<p>The example is extremely simple. A somewhat proficient user can extract much more interesting data than a simple blog count, but one always needs to keep in mind the legal aspects of web scraping. Nevertheless, this is a popular and fruitful way to extract and explore the data!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-01-23 12:29:20+00:00","draft":false,"title":"Preparing Scraped Data","blog":["addons","analysis","data","dataloading","examples","python","scripting"],"oldUrl":"/blog/2017/01/23/preparing-scraped-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One of the key questions of every data analysis is how to get the data and put it in the right form(at). 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\"December 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(3)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/11/\\\"\"\n          }), \"\u003e\"]\n        }), \"November 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(4)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/10/\\\"\"\n          }), \"\u003e\"]\n        }), \"October 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(3)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n      \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/09/\\\"\"\n          }), \"\u003e\"]\n        }), \"September 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(2)\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/08/\\\"\"\n          }), \"\u003e\"]\n        }), \"August 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(5)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/07/\\\"\"\n          }), \"\u003e\"]\n        }), \"July 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(3)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    ....\\n  \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"ul\"\n          }), \"\u003e\"]\n        }), \"\\n\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"aside\"\n          }), \"\u003e\"]\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, html is nice, but we can't really do data analysis with this. We will have to transform this output into something sensible. How about .csv, a simple comma-demilited format Orange can recognize?\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"open\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'scraped.csv'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'w'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" csvfile:\\n    csvwriter = csv.writer(csvfile, delimiter=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"','\"\n        }), \")\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We created a new file called '\", _jsx(_components.em, {\n        children: \"scraped.csv\"\n      }), \"' to which we will write our content ('w' parameter means write). Then we defined the writer and set the delimiter to comma.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we need to add the header row, so Orange will know what are the column names. We add this just after csvwriter variable.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Date\\\"\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"No_of_Blogs\\\"\"\n        }), \"])\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we have two columns, one named 'Date' and the other 'No\", _jsx(_components.em, {\n        children: \"of_Blogs'. The final step is to extract the data. We have a bunch of lines in html, but the one we're interested in is in a section 'aside' and has an id '_archives-2\"\n      }), \"'. We will first extract only this section (\", _jsx(_components.em, {\n        children: \".find(id='archives-2'\"\n      }), \") and get all the lines of the archive with the tag 'li' (\", _jsx(_components.em, {\n        children: \".find_all('li')\"\n      }), \"):\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" item \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" soup.find(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"id\"\n        }), \"=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"archives-2\\\"\"\n        }), \").find_all(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'li'\"\n        }), \"):\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is the result of \", _jsx(_components.em, {\n        children: \"print(item)\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-html\",\n        children: [_jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2017/01/\\\"\"\n          }), \"\u003e\"]\n        }), \"January 2017\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), \" (1)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we need to get the actual content from each line. The first part we need is the date of the archived content. Orange can read dates, but they need to come in the right format. We will extract the date from \", _jsx(_components.em, {\n        children: \"href\"\n      }), \" part with \", _jsx(_components.em, {\n        children: \"item.a.get('href')\"\n      }), \". Then we need to extract only digits from it as we're not interested in the rest of the link. We do this with Regex for finding digits:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"date = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.a.get(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'href'\"\n        }), \"))\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Regex's \", _jsx(_components.em, {\n        children: \"findall\"\n      }), \" function returns a list, in our case containing two items - the year and month of archived content. The second part of our data is the number of blogs archived in a particular month. We will again extract this with a Regex digit search, but this time we will be extracting data from the actual content - '\", _jsx(_components.em, {\n        children: \"item.contents[1]\"\n      }), \"'.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"digits = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.contents[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, we need to write each line to a .csv file we created above.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"%s-%s-01\\\"\"\n        }), \" % (date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]), digits[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]])\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here, we formatted the date into an ISO-standard format Orange recognizes as time variable (\", _jsx(_components.em, {\n        children: \"\\\"%s-%s-01\\\" % (date[0], date[1])\"\n      }), \"), while the second part is simply a count of our blog posts.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is the entire code:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" urllib.request \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" urlopen\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" bs4 \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" BeautifulSoup\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" csv\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" re\\n\\nhtml = urlopen(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'http://blog.biolab.si'\"\n        }), \")\\nsoup = BeautifulSoup(html.read(), \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'lxml'\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"open\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'scraped.csv'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'w'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" csvfile:\\n    csvwriter = csv.writer(csvfile, delimiter=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"','\"\n        }), \")\\n    csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Date\\\"\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"No_of_Blogs\\\"\"\n        }), \"])\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" item \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" soup.find(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"id\"\n        }), \"=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"archives-2\\\"\"\n        }), \").find_all(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'li'\"\n        }), \"):\\n        date = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.a.get(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'href'\"\n        }), \"))\\n        digits = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.contents[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n        csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"%s-%s-01\\\"\"\n        }), \" % (date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]), digits[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]])\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016-06-10-scripting-with-time-variable/\",\n        children: \"Scripting with Time Variable\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now let's load this in Orange. File widget can easily read .csv formats and also correctly identifies the two column types, datetime and numeric. A quick glance into the Data Table...\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.54.12.png\",\n      alt: \"\",\n      width: \"1244\",\n      height: \"1048\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Everything looks ok. We can use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-timeseries\",\n        children: \"Timeseries add-on\"\n      }), \" to inspect how many blogs we've written each month since 2010. Connect \", _jsx(_components.strong, {\n        children: \"As Timeseries\"\n      }), \" widget to File. Orange will automatically suggest to use Date as our time variable. Finally, we'll plot the data with \", _jsx(_components.strong, {\n        children: \"Line Chart\"\n      }), \". This is the curve of our blog activity.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.54.53.png\",\n      alt: \"\",\n      width: \"2222\",\n      height: \"1274\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.55.14.png\",\n      alt: \"\",\n      width: \"650\",\n      height: \"374\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The example is extremely simple. A somewhat proficient user can extract much more interesting data than a simple blog count, but one always needs to keep in mind the legal aspects of web scraping. Nevertheless, this is a popular and fruitful way to extract and explore the data!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"preparing-scraped-data"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>The example is extremely simple. A somewhat proficient user can extract much more interesting data than a simple blog count, but one always needs to keep in mind the legal aspects of web scraping. Nevertheless, this is a popular and fruitful way to extract and explore the data!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-01-23 12:29:20+00:00","draft":false,"title":"Preparing Scraped Data","blog":["addons","analysis","data","dataloading","examples","python","scripting"],"oldUrl":"/blog/2017/01/23/preparing-scraped-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"One of the key questions of every data analysis is how to get the data and put it in the right form(at). In this post I'll show you how to easily get the data from the web and transfer it to a file Orange can read.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2015/08/07/creating-a-new-data-table-in-orange-through-python/\",\n        children: \"Creating a new data table in Orange through Python\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, we'll have to do some scripting. We'll use a couple of Python libraries - \", _jsx(_components.a, {\n        href: \"https://docs.python.org/3.5/library/urllib.request.html\",\n        children: \"urllib.requests\"\n      }), \" fetching the data, \", _jsx(_components.a, {\n        href: \"https://www.crummy.com/software/BeautifulSoup/bs4/doc/\",\n        children: \"BeautifulSoup\"\n      }), \" for reading it, \", _jsx(_components.a, {\n        href: \"https://docs.python.org/3.5/library/csv.html\",\n        children: \"csv\"\n      }), \" for writing it and \", _jsx(_components.a, {\n        href: \"https://docs.python.org/3.5/library/re.html\",\n        children: \"regular expressions\"\n      }), \" for extracting the right data.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" urllib.request \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" urlopen\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" bs4 \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" BeautifulSoup\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" csv\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" re\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, we've imported the all the libraries we'll need. Now we will scrape the data from our own blog to see how many posts we've written throughout the years.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"html = urlopen(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'http://blog.biolab.si'\"\n        }), \")\\nsoup = BeautifulSoup(html.read(), \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'lxml'\"\n        }), \")\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The first line opens the address of the site we want to scrape. In our case this is our blog. The second line retrieves a html response from the site, which is our raw text. It looks like this:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-html\",\n        children: [_jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"aside\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"id\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"archives-2\\\"\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"class\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"widget widget_archive\\\"\"\n          }), \"\u003e\"]\n        }), \"\\n  \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"h3\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"class\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"widget-title\\\"\"\n          }), \"\u003e\"]\n        }), \"Archives\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"h3\"\n          }), \"\u003e\"]\n        }), \"\\n  \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"ul\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: 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        className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/10/\\\"\"\n          }), \"\u003e\"]\n        }), \"October 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: 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         children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/08/\\\"\"\n          }), \"\u003e\"]\n        }), \"August 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(5)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2016/07/\\\"\"\n          }), \"\u003e\"]\n        }), \"July 2016\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), _jsx(_components.span, {\n          className: \"hljs-symbol\",\n          children: \"\u0026nbsp;\"\n        }), \"(3)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n    ....\\n  \", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"ul\"\n          }), \"\u003e\"]\n        }), \"\\n\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"aside\"\n          }), \"\u003e\"]\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ok, html is nice, but we can't really do data analysis with this. We will have to transform this output into something sensible. How about .csv, a simple comma-demilited format Orange can recognize?\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"open\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'scraped.csv'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'w'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" csvfile:\\n    csvwriter = csv.writer(csvfile, delimiter=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"','\"\n        }), \")\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We created a new file called '\", _jsx(_components.em, {\n        children: \"scraped.csv\"\n      }), \"' to which we will write our content ('w' parameter means write). Then we defined the writer and set the delimiter to comma.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we need to add the header row, so Orange will know what are the column names. We add this just after csvwriter variable.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Date\\\"\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"No_of_Blogs\\\"\"\n        }), \"])\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we have two columns, one named 'Date' and the other 'No\", _jsx(_components.em, {\n        children: \"of_Blogs'. The final step is to extract the data. We have a bunch of lines in html, but the one we're interested in is in a section 'aside' and has an id '_archives-2\"\n      }), \"'. We will first extract only this section (\", _jsx(_components.em, {\n        children: \".find(id='archives-2'\"\n      }), \") and get all the lines of the archive with the tag 'li' (\", _jsx(_components.em, {\n        children: \".find_all('li')\"\n      }), \"):\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" item \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" soup.find(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"id\"\n        }), \"=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"archives-2\\\"\"\n        }), \").find_all(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'li'\"\n        }), \"):\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is the result of \", _jsx(_components.em, {\n        children: \"print(item)\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-html\",\n        children: [_jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \" \", _jsx(_components.span, {\n            className: \"hljs-attr\",\n            children: \"href\"\n          }), \"=\", _jsx(_components.span, {\n            className: \"hljs-string\",\n            children: \"\\\"http://blog.biolab.si/2017/01/\\\"\"\n          }), \"\u003e\"]\n        }), \"January 2017\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"a\"\n          }), \"\u003e\"]\n        }), \" (1)\", _jsxs(_components.span, {\n          className: \"hljs-tag\",\n          children: [\"\u003c/\", _jsx(_components.span, {\n            className: \"hljs-name\",\n            children: \"li\"\n          }), \"\u003e\"]\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we need to get the actual content from each line. The first part we need is the date of the archived content. Orange can read dates, but they need to come in the right format. We will extract the date from \", _jsx(_components.em, {\n        children: \"href\"\n      }), \" part with \", _jsx(_components.em, {\n        children: \"item.a.get('href')\"\n      }), \". Then we need to extract only digits from it as we're not interested in the rest of the link. We do this with Regex for finding digits:\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"date = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.a.get(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'href'\"\n        }), \"))\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Regex's \", _jsx(_components.em, {\n        children: \"findall\"\n      }), \" function returns a list, in our case containing two items - the year and month of archived content. The second part of our data is the number of blogs archived in a particular month. We will again extract this with a Regex digit search, but this time we will be extracting data from the actual content - '\", _jsx(_components.em, {\n        children: \"item.contents[1]\"\n      }), \"'.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"digits = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.contents[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, we need to write each line to a .csv file we created above.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"%s-%s-01\\\"\"\n        }), \" % (date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]), digits[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]])\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here, we formatted the date into an ISO-standard format Orange recognizes as time variable (\", _jsx(_components.em, {\n        children: \"\\\"%s-%s-01\\\" % (date[0], date[1])\"\n      }), \"), while the second part is simply a count of our blog posts.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is the entire code:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" urllib.request \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" urlopen\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"from\"\n        }), \" bs4 \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" BeautifulSoup\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" csv\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" re\\n\\nhtml = urlopen(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'http://blog.biolab.si'\"\n        }), \")\\nsoup = BeautifulSoup(html.read(), \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'lxml'\"\n        }), \")\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"with\"\n        }), \" \", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"open\"\n        }), \"(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'scraped.csv'\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'w'\"\n        }), \") \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"as\"\n        }), \" csvfile:\\n    csvwriter = csv.writer(csvfile, delimiter=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"','\"\n        }), \")\\n    csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"Date\\\"\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"No_of_Blogs\\\"\"\n        }), \"])\\n    \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" item \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" soup.find(\", _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"id\"\n        }), \"=\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"archives-2\\\"\"\n        }), \").find_all(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'li'\"\n        }), \"):\\n        date = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.a.get(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'href'\"\n        }), \"))\\n        digits = re.findall(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"r'\\\\d+'\"\n        }), \", item.contents[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"])\\n        csvwriter.writerow([\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"\\\"%s-%s-01\\\"\"\n        }), \" % (date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"], date[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1\"\n        }), \"]), digits[\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]])\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016-06-10-scripting-with-time-variable/\",\n        children: \"Scripting with Time Variable\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now let's load this in Orange. File widget can easily read .csv formats and also correctly identifies the two column types, datetime and numeric. A quick glance into the Data Table...\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.54.12.png\",\n      alt: \"\",\n      width: \"1244\",\n      height: \"1048\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Everything looks ok. We can use \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-timeseries\",\n        children: \"Timeseries add-on\"\n      }), \" to inspect how many blogs we've written each month since 2010. Connect \", _jsx(_components.strong, {\n        children: \"As Timeseries\"\n      }), \" widget to File. Orange will automatically suggest to use Date as our time variable. Finally, we'll plot the data with \", _jsx(_components.strong, {\n        children: \"Line Chart\"\n      }), \". This is the curve of our blog activity.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.54.53.png\",\n      alt: \"\",\n      width: \"2222\",\n      height: \"1274\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-01-preparing-scraped-data/__optimized-images__/Screen-Shot-2017-01-23-at-12.55.14.png\",\n      alt: \"\",\n      width: \"650\",\n      height: \"374\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The example is extremely simple. A somewhat proficient user can extract much more interesting data than a simple blog count, but one always needs to keep in mind the legal aspects of web scraping. Nevertheless, this is a popular and fruitful way to extract and explore the data!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"preparing-scraped-data"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Problems With Orange Website</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Problems With Orange Website"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2013-03-04 11:50:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="website"/><meta name="next-head-count" content="17"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>website</strong></p><h1 class="sc-c67a4900-0 loxVcf">Problems With Orange Website</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 04, 2013</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Our servers crashed on Friday, March 1st due to technical problems. The Orange website was offline for several hours and Mac bundle was unaccessible until today.</p>
 <p>We are still reviewing if our other services work. If you notice some problems, please ping us.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education</strong></p><h1 class="sc-c67a4900-0 loxVcf">Pumice challenge</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Anja Mejač, Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">May 26, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><a href="/blog/2023-03-pumice-challenge/__optimized-images__/2023-05-26-majice-pumice.jpg" data-gallery="true"><img alt="" loading="lazy" width="4499" height="3456" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-03-pumice-challenge/__optimized-images__/2023-05-26-majice-pumice.jpg"/></a>
 <p>What does it take to convince almost 1000 Slovenian students in K6-12 to learn about box plots, hierarchical clustering and the choropleth, and use them to explore a part of Slovenian geography and history?</p>
 <p>Answer: it takes an interesting challenge. Plus, a couple of videos and some material for teachers. T-shirts don&#x27;t hurt, either.</p>
@@ -165,4 +165,4 @@
 <p>Results and experiences? Great.</p>
 <p>First, we were happy about the attendance. 50 teachers from 30 different schools from all over Slovenia participated in the challenge, resulting in almost one thousand students completing the challenge. While preparing an event like this takes a lot of time (and printing and sending 1000 T-shirts, which all participants received as memento, is not something we&#x27;re used to doing), we learned that such challenges can indeed grab a lot of attention.</p>
 <p>Second, Orange proved to be a great tool for organizing such events. Today&#x27;s students know their way around digital device, so using Orange for - a non-trivial - data mining task was no hassle. <a href="https://orangedatamining.com/blog/2023/2021-03-31-zivjo-orange/">Translating it to Slovenian</a> also helped.</p>
-<p>And, most important for our project, participating teachers came from diverse range of subjects, and their feedback was generally very positive. Although this particular task was mostly related to geography and history, it shows the potential of using this approach to popularize and include data mining and artificial intelligence in different schools&#x27; subjects.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Anja Mejač, Janez Demšar","date":"2023-05-26","draft":false,"title":"Pumice challenge","thumbImage":"2023-05-26-majice-pumice.jpg","frontPageImage":"2023-05-26-majice-pumice.jpg","blog":["education"],"shortExcerpt":"One thousand Slovenian students took part in a data mining challenge","longExcerpt":"One thousand Slovenian students took part in a data mining challenge","oldUrl":"/blog/2023/2023-03-10-pumice-challenge/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-pumice-challenge/2023-05-26-majice-pumice.jpg\",\n      width: \"4499\",\n      height: \"3456\",\n      src: \"/blog/2023-03-pumice-challenge/__optimized-images__/2023-05-26-majice-pumice.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What does it take to convince almost 1000 Slovenian students in K6-12 to learn about box plots, hierarchical clustering and the choropleth, and use them to explore a part of Slovenian geography and history?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Answer: it takes an interesting challenge. 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+<p>And, most important for our project, participating teachers came from diverse range of subjects, and their feedback was generally very positive. Although this particular task was mostly related to geography and history, it shows the potential of using this approach to popularize and include data mining and artificial intelligence in different schools&#x27; subjects.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Anja Mejač, Janez Demšar","date":"2023-05-26","draft":false,"title":"Pumice challenge","thumbImage":"2023-05-26-majice-pumice.jpg","frontPageImage":"2023-05-26-majice-pumice.jpg","blog":["education"],"shortExcerpt":"One thousand Slovenian students took part in a data mining challenge","longExcerpt":"One thousand Slovenian students took part in a data mining challenge","oldUrl":"/blog/2023/2023-03-10-pumice-challenge/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-pumice-challenge/2023-05-26-majice-pumice.jpg\",\n      width: \"4499\",\n      height: \"3456\",\n      src: \"/blog/2023-03-pumice-challenge/__optimized-images__/2023-05-26-majice-pumice.jpg\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What does it take to convince almost 1000 Slovenian students in K6-12 to learn about box plots, hierarchical clustering and the choropleth, and use them to explore a part of Slovenian geography and history?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Answer: it takes an interesting challenge. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, examples, interactive data visualization, orange3, plot, tree, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Pythagorean Trees and Forests</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 29, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Classification Trees are great, but how about when they overgrow even your 27&#x27;&#x27; screen? Can we make the tree fit snugly onto the screen and still tell the whole story? Well, yes we can.</p>
 <p>Pythagorean Tree widget will show you the same information as Classification Tree, but way more concisely. Pythagorean Trees represent nodes with squares whose size is proportionate to the number of covered training instances. Once the data is split into two subsets, the corresponding new squares form a right triangle on top of the parent square. Hence Pythagorean Tree. Every square has the color of the prevalent, with opacity indicating the relative proportion of the majority class in the subset. Details are shown in hover balloons.</p>
 <a href="/blog/2016-07-pythagorean-trees-and-forests/__optimized-images__/ClassificationTree.png" data-gallery="true"><img alt="" loading="lazy" width="934" height="567" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-07-pythagorean-trees-and-forests/__optimized-images__/ClassificationTree.png"/></a>
@@ -164,4 +164,4 @@
 <p>Different trees are grown side by side. Parameters for the algorithm are set in Random Forest widget, then the whole forest is sent to Pythagorean Forest for visualization.</p>
 <p>This makes Pythagorean Forest a great tool to explain how Random Forest works or to further explore each tree in Pythagorean Tree widget.</p>
 <a href="/blog/2016-07-pythagorean-trees-and-forests/__optimized-images__/schema-pythagora.png" data-gallery="true"><img alt="" loading="lazy" width="940" height="518" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-07-pythagorean-trees-and-forests/__optimized-images__/schema-pythagora.png"/></a>
-<p>Pythagorean trees are a new addition to Orange. Their implementation has been inspired by a recent paper on <a href="http://publications.fbeck.com/ivapp14-pythagoras.pdf">Generalized Pythagoras Trees for Visualizing Hierarchies</a> by Fabian Beck, Michael Burch, Tanja Munz, Lorenzo Di Silvestro and Daniel Weiskopf that was presented in at the 5th International Conference on Information Visualization Theory and Applications in 2014.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-07-29 11:52:54+00:00","draft":false,"title":"Pythagorean Trees and Forests","blog":["classification","examples","interactive data visualization","orange3","plot","tree","visualization"],"oldUrl":"/blog/2016/07/29/pythagorean-trees-and-forests/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Classification Trees are great, but how about when they overgrow even your 27'' screen? 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+<p>Pythagorean trees are a new addition to Orange. Their implementation has been inspired by a recent paper on <a href="http://publications.fbeck.com/ivapp14-pythagoras.pdf">Generalized Pythagoras Trees for Visualizing Hierarchies</a> by Fabian Beck, Michael Burch, Tanja Munz, Lorenzo Di Silvestro and Daniel Weiskopf that was presented in at the 5th International Conference on Information Visualization Theory and Applications in 2014.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-07-29 11:52:54+00:00","draft":false,"title":"Pythagorean Trees and Forests","blog":["classification","examples","interactive data visualization","orange3","plot","tree","visualization"],"oldUrl":"/blog/2016/07/29/pythagorean-trees-and-forests/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Classification Trees are great, but how about when they overgrow even your 27'' screen? 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index 31e4d02d0..94cd245e2 100644
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Python Script: Managing Data on the Fly</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Python Script: Managing Data on the Fly"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-05-15 09:15:58+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="python"/><meta property="article:tag" content="scripting"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, python, scripting</strong></p><h1 class="sc-c67a4900-0 loxVcf">Python Script: Managing Data on the Fly</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">May 15, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Python Script is this mysterious widget most people don&#x27;t know how to use, even those versed in Python. Python Script is the widget that supplements Orange functionalities with (almost) everything that Python can offer. And it&#x27;s time we unveil some of its functionalities with a simple example.</p>
 <h3>Example: Batch Transform the Data</h3>
 <p>There might be a time when you need to apply a function to all your attributes. Say you wish to log-transform their values, as it is common in gene expression data. In theory, you could do this with Feature Constructor, where you would log-transform every attribute individually. Sounds laborious? It&#x27;s because it is. Why else we have computers if not to reduce manual labor for certain tasks? Let&#x27;s do it the fast way - with Python Script.</p>
@@ -173,4 +173,4 @@ <h3>Example: Batch Transform the Data</h3>
 <p>This is it. Now we can do our standard analysis on the transformed data. Even better! We can save our script and use it in Python Script widget any time we want.</p>
 <a href="/blog/2018-05-python-script-managing-data-on-the-fly/__optimized-images__/Screen-Shot-2018-05-15-at-10.13.26.png" data-gallery="true"><img alt="" loading="lazy" width="1596" height="1344" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-05-python-script-managing-data-on-the-fly/__optimized-images__/Screen-Shot-2018-05-15-at-10.13.26.png"/></a>
 <p>For your convenience we have created a repository of <a href="https://github.com/biolab/orange-scripts">Orange scripts</a>, so you can download and use it instantly!</p>
-<p>Have a more interesting example with Python Script? We&#x27;d love to hear about it!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-15 09:15:58+00:00","draft":false,"title":"Python Script: Managing Data on the Fly","blog":["orange3","python","scripting"],"oldUrl":"/blog/2018/05/15/python-script-managing-data-on-the-fly/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    em: \"em\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Python Script is this mysterious widget most people don't know how to use, even those versed in Python. 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This data set has 9485 features, so imagine having to transform each feature individually.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-python-script-managing-data-on-the-fly/__optimized-images__/Screen-Shot-2018-05-11-at-12.18.56.png\",\n      alt: \"\",\n      width: \"872\",\n      height: \"982\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead, we will connect Python Script to File and use a simple script to apply the same transformation to all attributes.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003ccode\u003eimport numpy as np\\n    from Orange.data import Table\\n    \\n    new_X = np.log(in_data.X)\\n    out_data = Table(in_data.domain, new_X, in_data.Y, in_data.metas)\\n    \u003c/code\u003e\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is really simple. 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+<p>Have a more interesting example with Python Script? We&#x27;d love to hear about it!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-15 09:15:58+00:00","draft":false,"title":"Python Script: Managing Data on the Fly","blog":["orange3","python","scripting"],"oldUrl":"/blog/2018/05/15/python-script-managing-data-on-the-fly/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h3: \"h3\",\n    em: \"em\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Python Script is this mysterious widget most people don't know how to use, even those versed in Python. 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Let's do it the fast way - with Python Script.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, open File widget and load \", _jsx(_components.em, {\n        children: \"geo-gds360.tab\"\n      }), \" from \", _jsx(_components.em, {\n        children: \"Browse documentation data sets\"\n      }), \". This data set has 9485 features, so imagine having to transform each feature individually.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-python-script-managing-data-on-the-fly/__optimized-images__/Screen-Shot-2018-05-11-at-12.18.56.png\",\n      alt: \"\",\n      width: \"872\",\n      height: \"982\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead, we will connect Python Script to File and use a simple script to apply the same transformation to all attributes.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    \u003ccode\u003eimport numpy as np\\n    from Orange.data import Table\\n    \\n    new_X = np.log(in_data.X)\\n    out_data = Table(in_data.domain, new_X, in_data.Y, in_data.metas)\\n    \u003c/code\u003e\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is really simple. Use in_data.X, which accesses all features in the data set, to transform the data with np.log (or any other numpy function). Set out_data to new_X and, voila, the transformed data is on the output. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>widgets</strong></p><h1 class="sc-c67a4900-0 loxVcf">Quick previews in Orange widgets</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Janez Demšar</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 10, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange 3.33 comes with a simple new feature that we adore.</p>
 <p>See those numbers in the status bar of most widgets? They briefly summarize the output, like the number of rows in output tables. Hovering there gives more details.</p>
 <p>From Orange version 3.33, clicking these numbers shows a data preview. Currently, this only works for output tables, but we&#x27;ll also add this functionality for other types of data.</p>
 <p>My favorite use: select some data in visualization and see it without connecting any widget to the output.</p>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/WUhvpYk3gwY?playlist=WUhvpYk3gwY&amp;loop=1&amp;autoplay=1" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2022-10-10","draft":false,"title":"Quick previews in Orange widgets","thumbImage":"2022_10_10_quick_preview.png","frontPageImage":"2022_10_10_quick_preview.png","blog":["widgets"],"shortExcerpt":"An exciting new feature in widgets.","longExcerpt":"Widgets can now show a quick preview of their output data.","oldUrl":"/blog/2022/2022-10-10-quick-preview/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange 3.33 comes with a simple new feature that we adore.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"See those numbers in the status bar of most widgets? They briefly summarize the output, like the number of rows in output tables. Hovering there gives more details.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"From Orange version 3.33, clicking these numbers shows a data preview. Currently, this only works for output tables, but we'll also add this functionality for other types of data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"My favorite use: select some data in visualization and see it without connecting any widget to the output.\"\n    }), \"\\n\", _jsx(\"iframe\", {\n      width: \"560\",\n      height: \"315\",\n      src: \"https://www.youtube.com/embed/WUhvpYk3gwY?playlist=WUhvpYk3gwY\u0026loop=1\u0026autoplay=1\",\n      title: \"YouTube video player\",\n      frameborder: \"0\",\n      allow: \"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\",\n      allowFullScreen: true\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":{"width":766,"height":478,"src":"/blog/2022-10-quick-preview/__optimized-images__/2022_10_10_quick_preview.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"quick-previews-in-orange-widgets"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<iframe width="560" height="315" src="https://www.youtube.com/embed/WUhvpYk3gwY?playlist=WUhvpYk3gwY&amp;loop=1&amp;autoplay=1" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2022-10-10","draft":false,"title":"Quick previews in Orange widgets","thumbImage":"2022_10_10_quick_preview.png","frontPageImage":"2022_10_10_quick_preview.png","blog":["widgets"],"shortExcerpt":"An exciting new feature in widgets.","longExcerpt":"Widgets can now show a quick preview of their output data.","oldUrl":"/blog/2022/2022-10-10-quick-preview/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange 3.33 comes with a simple new feature that we adore.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"See those numbers in the status bar of most widgets? They briefly summarize the output, like the number of rows in output tables. Hovering there gives more details.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"From Orange version 3.33, clicking these numbers shows a data preview. Currently, this only works for output tables, but we'll also add this functionality for other types of data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"My favorite use: select some data in visualization and see it without connecting any widget to the output.\"\n    }), \"\\n\", _jsx(\"iframe\", {\n      width: \"560\",\n      height: \"315\",\n      src: \"https://www.youtube.com/embed/WUhvpYk3gwY?playlist=WUhvpYk3gwY\u0026loop=1\u0026autoplay=1\",\n      title: \"YouTube video player\",\n      frameborder: \"0\",\n      allow: \"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\",\n      allowFullScreen: true\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":{"width":766,"height":478,"src":"/blog/2022-10-quick-preview/__optimized-images__/2022_10_10_quick_preview.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"quick-previews-in-orange-widgets"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>tree</strong></p><h1 class="sc-c67a4900-0 loxVcf">Random decisions behind your back</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 05, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>When Orange builds a decision tree, candidate attributes are evaluated and the best candidate is chosen. But what if two or more share the first place? Most machine learning systems don&#x27;t care about it and always take the first, which is unfair and, besides, has strange effects: the induced model and, consequentially, its accuracy depends upon the order of attributes. Which shouldn&#x27;t be.</p>
 <p>This is not an isolated problem. Another instance is when a classifier has to choose between two equally probable classes when there is no additional information (such as classification costs) to help make the prediction. Or selecting random reference examples when computing ReliefF. Returning a modus of a distribution with two or more competing values...</p>
 <p>The old solution was to make a random selection in such cases. Take a random class (out of the most probable, of course), random attribute, random examples... Although theoretically correct, it comes with a price: the only way to ensure repeatability of experiments is by setting the global random generator, which is not a good practice in component-based systems.</p>
 <p>What Orange does now is more cunning. When, for instance, choosing between n equally probable classes, Orange computes something like a hash value from the example to be classified. Its remainder at division by n is then used to select the class. Thus, the class will be random, but always the same for same example.</p>
 <p>A similar trick is used elsewhere. To choose an attribute when building a tree, it simply divides the number of learning examples at that node by the number of candidate attributes and the remainder is used again.</p>
 <p>When more random numbers are needed, for instance for selecting m random reference examples for computing ReliefF, the number of examples is used for a random seed for a temporary random generator.</p>
-<p>To conclude: Orange will sometimes make decisions that will look random. The reason for this is that it is more fair than most of machine learning systems that pick the first (or the last) candidate. But whatever decision is taken, it will be the same if you run the program twice. The message is thus: be aware that this is happenning, but don&#x27;t care about it.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-02-05 20:30:00+00:00","draft":false,"title":"Random decisions behind your back","blog":["tree"],"oldUrl":"/blog/2012/02/05/random-decisions-behind-your-back/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"When Orange builds a decision tree, candidate attributes are evaluated and the best candidate is chosen. But what if two or more share the first place? Most machine learning systems don't care about it and always take the first, which is unfair and, besides, has strange effects: the induced model and, consequentially, its accuracy depends upon the order of attributes. Which shouldn't be.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is not an isolated problem. Another instance is when a classifier has to choose between two equally probable classes when there is no additional information (such as classification costs) to help make the prediction. Or selecting random reference examples when computing ReliefF. Returning a modus of a distribution with two or more competing values...\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The old solution was to make a random selection in such cases. Take a random class (out of the most probable, of course), random attribute, random examples... Although theoretically correct, it comes with a price: the only way to ensure repeatability of experiments is by setting the global random generator, which is not a good practice in component-based systems.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"What Orange does now is more cunning. When, for instance, choosing between n equally probable classes, Orange computes something like a hash value from the example to be classified. Its remainder at division by n is then used to select the class. Thus, the class will be random, but always the same for same example.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A similar trick is used elsewhere. To choose an attribute when building a tree, it simply divides the number of learning examples at that node by the number of candidate attributes and the remainder is used again.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When more random numbers are needed, for instance for selecting m random reference examples for computing ReliefF, the number of examples is used for a random seed for a temporary random generator.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To conclude: Orange will sometimes make decisions that will look random. The reason for this is that it is more fair than most of machine learning systems that pick the first (or the last) candidate. But whatever decision is taken, it will be the same if you run the program twice. 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\ No newline at end of file
+<p>To conclude: Orange will sometimes make decisions that will look random. The reason for this is that it is more fair than most of machine learning systems that pick the first (or the last) candidate. But whatever decision is taken, it will be the same if you run the program twice. The message is thus: be aware that this is happenning, but don&#x27;t care about it.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-02-05 20:30:00+00:00","draft":false,"title":"Random decisions behind your back","blog":["tree"],"oldUrl":"/blog/2012/02/05/random-decisions-behind-your-back/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"When Orange builds a decision tree, candidate attributes are evaluated and the best candidate is chosen. But what if two or more share the first place? Most machine learning systems don't care about it and always take the first, which is unfair and, besides, has strange effects: the induced model and, consequentially, its accuracy depends upon the order of attributes. Which shouldn't be.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is not an isolated problem. Another instance is when a classifier has to choose between two equally probable classes when there is no additional information (such as classification costs) to help make the prediction. Or selecting random reference examples when computing ReliefF. Returning a modus of a distribution with two or more competing values...\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The old solution was to make a random selection in such cases. Take a random class (out of the most probable, of course), random attribute, random examples... 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To choose an attribute when building a tree, it simply divides the number of learning examples at that node by the number of candidate attributes and the remainder is used again.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When more random numbers are needed, for instance for selecting m random reference examples for computing ReliefF, the number of examples is used for a random seed for a temporary random generator.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To conclude: Orange will sometimes make decisions that will look random. The reason for this is that it is more fair than most of machine learning systems that pick the first (or the last) candidate. But whatever decision is taken, it will be the same if you run the program twice. The message is thus: be aware that this is happenning, but don't care about it.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"random-decisions-behind-your-back"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/random-forest-switches-to-simple-tree-learner-by-default/index.html b/blog/random-forest-switches-to-simple-tree-learner-by-default/index.html
index 026955c7d..1fc5970d8 100644
--- a/blog/random-forest-switches-to-simple-tree-learner-by-default/index.html
+++ b/blog/random-forest-switches-to-simple-tree-learner-by-default/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Random forest switches to Simple tree learner by default</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Random forest switches to Simple tree learner by default"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2011-12-08 15:28:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="forestlearner"/><meta property="article:tag" content="simpletreelearner"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>forestlearner, simpletreelearner</strong></p><h1 class="sc-c67a4900-0 loxVcf">Random forest switches to Simple tree learner by default</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 08, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Random forest classifiers now use <strong>Orange.classification.tree.SimpleTreeLearner</strong>by default, which considerably shortens their construction times.</p>
 <p>Using a random forest classifier is easy.</p>
 <pre><code>	import Orange
@@ -165,4 +165,4 @@
 <p>By setting the base_learner parameter to <strong>TreeLearer</strong> it is possible to revert to the original behaviour:</p>
 <pre><code>	tree_learner = Orange.classification.tree.TreeLearner()
 	forest_orig = Orange.ensemble.forest.RandomForestLearner(base_learner=tree_learner)
-</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-12-08 15:28:00+00:00","draft":false,"title":"Random forest switches to Simple tree learner by default","blog":["forestlearner","simpletreelearner"],"oldUrl":"/blog/2011/12/08/random-forest-switches-to-simple-tree-learner-by-default/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Random forest classifiers now use \", _jsx(_components.strong, {\n        children: \"Orange.classification.tree.SimpleTreeLearner\"\n      }), \"by default, which considerably shortens their construction times.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using a random forest classifier is easy.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"\\timport Orange\\n\\n\\tiris = Orange.data.Table('iris')\\n\\tforest = Orange.ensemble.forest.RandomForestLearner(iris, trees=200)\\n\\tfor instance in iris:\\n\\t    print forest(instance), instance.get_class()\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The example above loads the iris dataset and trains a random forest classifier with 200 trees. 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+</code></pre></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-12-08 15:28:00+00:00","draft":false,"title":"Random forest switches to Simple tree learner by default","blog":["forestlearner","simpletreelearner"],"oldUrl":"/blog/2011/12/08/random-forest-switches-to-simple-tree-learner-by-default/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    pre: \"pre\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Random forest classifiers now use \", _jsx(_components.strong, {\n        children: \"Orange.classification.tree.SimpleTreeLearner\"\n      }), \"by default, which considerably shortens their construction times.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using a random forest classifier is easy.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"\\timport Orange\\n\\n\\tiris = Orange.data.Table('iris')\\n\\tforest = Orange.ensemble.forest.RandomForestLearner(iris, trees=200)\\n\\tfor instance in iris:\\n\\t    print forest(instance), instance.get_class()\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The example above loads the iris dataset and trains a random forest classifier with 200 trees. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>conference, research</strong></p><h1 class="sc-c67a4900-0 loxVcf">Recap of 26th International Conference on Discovery Science</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Martin Špendl</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 20, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We are excited to share that two of our PhD students, <strong>Pavlin Poličar</strong> and <strong>Jaka Kokošar</strong>, presented their papers describing our lab&#x27;s ongoing research into biomarker discovery and temporal visualizations at the recent Discovery Science 2023 conference held in Porto, Portugal. Their research is greatly contributing to the science behind the tools in Orange, especially in the field of visualizations, bioinformatics and survival analysis. As a team, we want to make our work accessible to everyone, so we invite you to check our open-access papers down below.</p>
 <a href="/blog/2023-10-porto-conference/__optimized-images__/2023-10-20-Porto.jpg" data-gallery="true"><img alt="" loading="lazy" width="1000" height="1202" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-10-porto-conference/__optimized-images__/2023-10-20-Porto.jpg"/></a>
 <h2>1. Refining Temporal Visualizations Using the Directional Coherence Loss</h2>
@@ -161,4 +161,4 @@ <h2>2. Gene Interactions in Survival Data Analysis: A Data-Driven Approach Using
 <p>The vast landscape of survival-related gene interactions is complex. Our approach, which combines biology-inspired algorithms with in-depth literature mining, offers a streamlined and efficient way to interpret this data. This research contributes to an evolving toolbox for survival analysis in Orange.
 Check out the <a href="https://link.springer.com/chapter/10.1007/978-3-031-45275-8_20">open-access paper</a>.</p>
 <p>We are thrilled to be a part of the Discovery Science community, where outstanding research is brought to light.</p>
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-<a href="/blog/2012-04-redesign-of-gui-icons/__optimized-images__/orange-icons-new-sets.jpg" data-gallery="true"><img alt="" loading="lazy" width="600" height="800" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2012-04-redesign-of-gui-icons/__optimized-images__/orange-icons-new-sets.jpg"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2012-04-09 11:25:00+00:00","draft":false,"title":"Redesign of GUI icons","blog":["icons"],"oldUrl":"/blog/2012/04/09/redesign-of-gui-icons/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange GUI is being redesigned. Expect a welcome screen with selection of preloaded widget schemes, simpler access to computational components, and integration with intelligent interface (widget suggestions). For the project we have engaged a designer \", _jsx(_components.a, {\n        href: \"http://yukaii.com/\",\n        children: \"Peter Čuhalev\"\n      }), \". To give you a taste of what is going on, here are some icons for widget sets that are being redesigned. There are in B/W, the color will be decided on and added in later stages. Below are just the icons - widget symbols with no frames. Current frames are rounded squares, while it looks like the widget frames for the new GUI will be circles. New icons are designed in a vector format.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2012-04-redesign-of-gui-icons/__optimized-images__/orange-icons-new-sets.jpg\",\n      alt: \"\",\n      width: \"600\",\n      height: \"800\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"redesign-of-gui-icons"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, analysis, business intelligence, classification, examples, orange3, preprocessing, text mining</strong></p><h1 class="sc-c67a4900-0 loxVcf">Rehaul of Text Mining Add-On</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 05, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Google Summer of Code is progressing nicely and some major improvements are already live! Our students have been working hard and today we&#x27;re thanking Alexey for his work on Text Mining add-on. Two major tasks before the midterms were to introduce Twitter widget and rehaul Preprocess Text. Twitter widget was designed to be a part of our summer school program and it worked beautifully. We&#x27;ve introduced youngsters to the world of data mining through social networks and one of the most exciting things was to see whether we can predict the author from the tweet content.</p>
 <p>Twitter widget offers many functionalities. Since we wanted to get tweets from specific authors, we entered their Twitter handles as queries and set &#x27;Search by Author&#x27;. We only included Author, Content and Date in the query parameters, as we want to predict the author only on the basis of text.</p>
 <a href="/blog/2016-07-rehaul-of-text-mining-add-on/__optimized-images__/Twitter1-stamped-1.png" data-gallery="true"><img alt="" loading="lazy" width="376" height="622" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-07-rehaul-of-text-mining-add-on/__optimized-images__/Twitter1-stamped-1.png"/></a>
@@ -181,4 +181,4 @@
 <p>Do these make any sense? You be the judge. :)</p>
 <p>We checked classification results in Test&amp;Score, counted misclassifications in Confusion Matrix and finally observed them in Corpus Viewer. k-NN seems to perform moderately well, while Classification Tree fails miserably. Still, this was trained on barely 200 tweets. Perhaps accumulating results over time might give us much better results. You can now certainly try it on your own! Update your Orange3-Text add-on or install it via &#x27;pip install Orange3-Text&#x27;!</p>
 <a href="/blog/2016-07-rehaul-of-text-mining-add-on/__optimized-images__/schema-twittet-preprocess.png" data-gallery="true"><img alt="" loading="lazy" width="778" height="416" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-07-rehaul-of-text-mining-add-on/__optimized-images__/schema-twittet-preprocess.png"/></a>
-<p>Above is the final workflow. Preprocessing on the left. Testing and scoring on the right bottom. Construction of classification tree right and above.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-07-05 06:51:55+00:00","draft":false,"title":"Rehaul of Text Mining Add-On","blog":["addons","analysis","business intelligence","classification","examples","orange3","preprocessing","text mining"],"oldUrl":"/blog/2016/07/05/rehaul-of-text-mining-add-on/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Google Summer of Code is progressing nicely and some major improvements are already live! Our students have been working hard and today we're thanking Alexey for his work on Text Mining add-on. Two major tasks before the midterms were to introduce Twitter widget and rehaul Preprocess Text. Twitter widget was designed to be a part of our summer school program and it worked beautifully. We've introduced youngsters to the world of data mining through social networks and one of the most exciting things was to see whether we can predict the author from the tweet content.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Twitter widget offers many functionalities. Since we wanted to get tweets from specific authors, we entered their Twitter handles as queries and set 'Search by Author'. 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\ No newline at end of file
+<p>Above is the final workflow. Preprocessing on the left. Testing and scoring on the right bottom. Construction of classification tree right and above.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-07-05 06:51:55+00:00","draft":false,"title":"Rehaul of Text Mining Add-On","blog":["addons","analysis","business intelligence","classification","examples","orange3","preprocessing","text mining"],"oldUrl":"/blog/2016/07/05/rehaul-of-text-mining-add-on/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Google Summer of Code is progressing nicely and some major improvements are already live! 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diff --git a/blog/report-is-back-and-better-than-ever/index.html b/blog/report-is-back-and-better-than-ever/index.html
index 54d6b9c25..3bb710760 100644
--- a/blog/report-is-back-and-better-than-ever/index.html
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 <p>Save Graph just got company - a <strong>Report button</strong>! Report works in most widgets, apart from the very obvious ones that simply transmit or display the data (Python Scripting, Edit Domain, Image Viewer, Predictions…).</p>
 <p>Why is Report so great?</p>
@@ -172,4 +172,4 @@
 <li>
 <p>Open report. To open a saved report file go to File → Open Report. To view the report you’re working on, go to Options → Show report view or click Shift+R.</p>
 </li>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, gsoc2016, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Rule Induction (Part I - Scripting)</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MATEVZKREN</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 05, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><em>This is a guest blog from the Google Summer of Code project.</em></p>
 <p>We’ve all heard the saying, “Rules are meant to be broken.” Regardless of how you might feel about the idea, one thing is certain. Rules must first be learnt. My 2016 <a href="https://summerofcode.withgoogle.com">Google Summer of Code</a> project revolves around doing just that. I am developing classification rule induction techniques for <a href="http://orange.biolab.si">Orange</a>, and here describing the code currently available in the <a href="https://github.com/biolab/orange3/pull/1397">pull request</a> and that will become part of official distribution in an upcoming <a href="https://github.com/biolab/orange3/blob/master/CHANGELOG.md">release 3.3.8</a>.</p>
 <p>Rule induction from examples is recognised as a fundamental component of many machine learning systems. My goal was foremost to implement supervised rule induction algorithms and rule-based classification methods, but also to devise a more general framework of replaceable individual components that users could fine-tune to their needs. To this purpose, <a href="http://dx.doi.org/10.1023/A:1006524209794">separate-and-conquer strategy</a> was applied. In essence, learning instances are covered and removed following a chosen rule. The process is repeated while learning set examples remain. To evaluate found hypotheses and to choose the best rule in each iteration, search heuristics are used (primarily, rule class distribution is the decisive determinant).</p>
@@ -189,4 +189,4 @@
 </code></pre>
 <p>If no other rules fire, default rule (majority classification) is used. Specific to each individual rule inducer, the application of the default rule varies.</p>
 <p>Though rule learning is most frequently used in the context of predictive induction, it can be adapted to subgroup discovery. In contrast, subgroup discovery aims at learning individual patterns or interesting population subgroups, rather than to maximise classification accuracy. Induced rules prove very valuable in terms of their descriptive power. To this end, <a href="http://www.jmlr.org/papers/volume5/lavrac04a/lavrac04a.pdf">CN2-SD</a> algorithms were also implemented.</p>
-<p>Hopefully, the addition to the Orange software suite will benefit both novice and expert users looking advance their knowledge in a particular area of study, through a better understanding of given predictions and underlying argumentation.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MATEVZKREN","date":"2016-08-05 11:52:39+00:00","draft":false,"title":"Rule Induction (Part I - Scripting)","blog":["classification","gsoc2016","orange3"],"post_format":["Quote"],"oldUrl":"/blog/2016/08/05/rule-induction-part-i-scripting/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We’ve all heard the saying, “Rules are meant to be broken.” Regardless of how you might feel about the idea, one thing is certain. Rules must first be learnt. My 2016 \", _jsx(_components.a, {\n        href: \"https://summerofcode.withgoogle.com\",\n        children: \"Google Summer of Code\"\n      }), \" project revolves around doing just that. I am developing classification rule induction techniques for \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \", and here describing the code currently available in the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/pull/1397\",\n        children: \"pull request\"\n      }), \" and that will become part of official distribution in an upcoming \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/master/CHANGELOG.md\",\n        children: \"release 3.3.8\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Rule induction from examples is recognised as a fundamental component of many machine learning systems. My goal was foremost to implement supervised rule induction algorithms and rule-based classification methods, but also to devise a more general framework of replaceable individual components that users could fine-tune to their needs. To this purpose, \", _jsx(_components.a, {\n        href: \"http://dx.doi.org/10.1023/A:1006524209794\",\n        children: \"separate-and-conquer strategy\"\n      }), \" was applied. In essence, learning instances are covered and removed following a chosen rule. The process is repeated while learning set examples remain. To evaluate found hypotheses and to choose the best rule in each iteration, search heuristics are used (primarily, rule class distribution is the decisive determinant).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The use of the created module is straightforward. New rule induction algorithms can be easily introduced, by either utilising predefined components or developing new ones (these include various search algorithms, search strategies, evaluators, and others). Several well-known rule induction algorithms have already been included. Let’s see how they perform!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://dx.doi.org/10.1023/A:1022641700528\",\n        children: \"Classic CN2\"\n      }), \" inducer constructs a list of ordered rules (decision list). Here, we load the \", _jsx(_components.em, {\n        children: \"titanic\"\n      }), \" data set and create a simple classifier, which can already be used to predict data.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'titanic'\"\n        }), \")\\nlearner = Orange.classification.CN2Learner()\\nclassifier = learner(data)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Similarly, a set of unordered rules can be constructed using \", _jsx(_components.a, {\n        href: \"http://dx.doi.org/10.1007/BFb0017011\",\n        children: \"Unordered CN2\"\n      }), \" inducer. Rules are learnt for each class individually, in regard to the original learning data. To evaluate found hypotheses, Laplace accuracy measure is used. Having first initialised the learner, we then control the algorithm by modifying its parameters. The underlying components are available to us by accessing the rule finder.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"data = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'iris.tab'\"\n        }), \")\\nlearner = CN2UnorderedLearner()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# consider up to 10 solution streams at one time\"\n        }), \"\\nlearner.rule_finder.search_algorithm.beam_width = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"10\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# continuous value space is constrained to reduce computation time\"\n        }), \"\\nlearner.rule_finder.search_strategy.bound_continuous = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"True\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# found rules must cover at least 15 examples\"\n        }), \"\\nlearner.rule_finder.general_validator.min_covered_examples = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"15\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# found rules must combine at most 2 selectors (conditions)\"\n        }), \"\\nlearner.rule_finder.general_validator.max_rule_length = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \"\\n\\nclassifier = learner(data)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Induced rules can be quickly reviewed and interpreted. They are each of the form ‘\", _jsx(_components.em, {\n        children: \"if\"\n      }), \" cond \", _jsx(_components.em, {\n        children: \"then\"\n      }), \" predict class”. That is, a conjunction of selectors followed by the predicted class.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" rule \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" classifier.rule_list:\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"... \"\n        }), _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \"(rule, rule.curr_class_dist.tolist())\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF petal length\u003c=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3.0\"\n        }), \" AND sepal width\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2.9\"\n        }), \" THEN iris=Iris-setosa [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"49\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF petal length\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3.0\"\n        }), \" AND petal length\u003c=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4.8\"\n        }), \" THEN iris=Iris-versicolor [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"46\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF petal width\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1.8\"\n        }), \" AND petal length\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4.9\"\n        }), \" THEN iris=Iris-virginica [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"43\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF TRUE THEN iris=Iris-virginica [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"50\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"50\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"50\"\n        }), \"]  \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# the default rule\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If no other rules fire, default rule (majority classification) is used. Specific to each individual rule inducer, the application of the default rule varies.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Though rule learning is most frequently used in the context of predictive induction, it can be adapted to subgroup discovery. In contrast, subgroup discovery aims at learning individual patterns or interesting population subgroups, rather than to maximise classification accuracy. Induced rules prove very valuable in terms of their descriptive power. To this end, \", _jsx(_components.a, {\n        href: \"http://www.jmlr.org/papers/volume5/lavrac04a/lavrac04a.pdf\",\n        children: \"CN2-SD\"\n      }), \" algorithms were also implemented.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hopefully, the addition to the Orange software suite will benefit both novice and expert users looking advance their knowledge in a particular area of study, through a better understanding of given predictions and underlying argumentation.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"rule-induction-part-i-scripting"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Hopefully, the addition to the Orange software suite will benefit both novice and expert users looking advance their knowledge in a particular area of study, through a better understanding of given predictions and underlying argumentation.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MATEVZKREN","date":"2016-08-05 11:52:39+00:00","draft":false,"title":"Rule Induction (Part I - Scripting)","blog":["classification","gsoc2016","orange3"],"post_format":["Quote"],"oldUrl":"/blog/2016/08/05/rule-induction-part-i-scripting/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\",\n    span: \"span\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We’ve all heard the saying, “Rules are meant to be broken.” Regardless of how you might feel about the idea, one thing is certain. Rules must first be learnt. My 2016 \", _jsx(_components.a, {\n        href: \"https://summerofcode.withgoogle.com\",\n        children: \"Google Summer of Code\"\n      }), \" project revolves around doing just that. I am developing classification rule induction techniques for \", _jsx(_components.a, {\n        href: \"http://orange.biolab.si\",\n        children: \"Orange\"\n      }), \", and here describing the code currently available in the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/pull/1397\",\n        children: \"pull request\"\n      }), \" and that will become part of official distribution in an upcoming \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3/blob/master/CHANGELOG.md\",\n        children: \"release 3.3.8\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Rule induction from examples is recognised as a fundamental component of many machine learning systems. My goal was foremost to implement supervised rule induction algorithms and rule-based classification methods, but also to devise a more general framework of replaceable individual components that users could fine-tune to their needs. To this purpose, \", _jsx(_components.a, {\n        href: \"http://dx.doi.org/10.1023/A:1006524209794\",\n        children: \"separate-and-conquer strategy\"\n      }), \" was applied. In essence, learning instances are covered and removed following a chosen rule. The process is repeated while learning set examples remain. To evaluate found hypotheses and to choose the best rule in each iteration, search heuristics are used (primarily, rule class distribution is the decisive determinant).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The use of the created module is straightforward. New rule induction algorithms can be easily introduced, by either utilising predefined components or developing new ones (these include various search algorithms, search strategies, evaluators, and others). Several well-known rule induction algorithms have already been included. Let’s see how they perform!\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://dx.doi.org/10.1023/A:1022641700528\",\n        children: \"Classic CN2\"\n      }), \" inducer constructs a list of ordered rules (decision list). Here, we load the \", _jsx(_components.em, {\n        children: \"titanic\"\n      }), \" data set and create a simple classifier, which can already be used to predict data.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"import\"\n        }), \" Orange\\ndata = Orange.data.Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'titanic'\"\n        }), \")\\nlearner = Orange.classification.CN2Learner()\\nclassifier = learner(data)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Similarly, a set of unordered rules can be constructed using \", _jsx(_components.a, {\n        href: \"http://dx.doi.org/10.1007/BFb0017011\",\n        children: \"Unordered CN2\"\n      }), \" inducer. Rules are learnt for each class individually, in regard to the original learning data. To evaluate found hypotheses, Laplace accuracy measure is used. Having first initialised the learner, we then control the algorithm by modifying its parameters. The underlying components are available to us by accessing the rule finder.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [\"data = Table(\", _jsx(_components.span, {\n          className: \"hljs-string\",\n          children: \"'iris.tab'\"\n        }), \")\\nlearner = CN2UnorderedLearner()\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# consider up to 10 solution streams at one time\"\n        }), \"\\nlearner.rule_finder.search_algorithm.beam_width = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"10\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# continuous value space is constrained to reduce computation time\"\n        }), \"\\nlearner.rule_finder.search_strategy.bound_continuous = \", _jsx(_components.span, {\n          className: \"hljs-literal\",\n          children: \"True\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# found rules must cover at least 15 examples\"\n        }), \"\\nlearner.rule_finder.general_validator.min_covered_examples = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"15\"\n        }), \"\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# found rules must combine at most 2 selectors (conditions)\"\n        }), \"\\nlearner.rule_finder.general_validator.max_rule_length = \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2\"\n        }), \"\\n\\nclassifier = learner(data)\\n\"]\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Induced rules can be quickly reviewed and interpreted. They are each of the form ‘\", _jsx(_components.em, {\n        children: \"if\"\n      }), \" cond \", _jsx(_components.em, {\n        children: \"then\"\n      }), \" predict class”. That is, a conjunction of selectors followed by the predicted class.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsxs(_components.code, {\n        className: \"hljs language-python\",\n        children: [_jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"for\"\n        }), \" rule \", _jsx(_components.span, {\n          className: \"hljs-keyword\",\n          children: \"in\"\n        }), \" classifier.rule_list:\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"... \"\n        }), _jsx(_components.span, {\n          className: \"hljs-built_in\",\n          children: \"print\"\n        }), \"(rule, rule.curr_class_dist.tolist())\\n\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF petal length\u003c=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3.0\"\n        }), \" AND sepal width\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"2.9\"\n        }), \" THEN iris=Iris-setosa [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"49\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF petal length\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3.0\"\n        }), \" AND petal length\u003c=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4.8\"\n        }), \" THEN iris=Iris-versicolor [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"46\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"3\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF petal width\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"1.8\"\n        }), \" AND petal length\u003e=\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"4.9\"\n        }), \" THEN iris=Iris-virginica [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"0\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"43\"\n        }), \"]\\n\", _jsx(_components.span, {\n          className: \"hljs-meta\",\n          children: \"\u003e\u003e\u003e \"\n        }), \"IF TRUE THEN iris=Iris-virginica [\", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"50\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"50\"\n        }), \", \", _jsx(_components.span, {\n          className: \"hljs-number\",\n          children: \"50\"\n        }), \"]  \", _jsx(_components.span, {\n          className: \"hljs-comment\",\n          children: \"# the default rule\"\n        }), \"\\n\"]\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If no other rules fire, default rule (majority classification) is used. Specific to each individual rule inducer, the application of the default rule varies.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Though rule learning is most frequently used in the context of predictive induction, it can be adapted to subgroup discovery. In contrast, subgroup discovery aims at learning individual patterns or interesting population subgroups, rather than to maximise classification accuracy. Induced rules prove very valuable in terms of their descriptive power. To this end, \", _jsx(_components.a, {\n        href: \"http://www.jmlr.org/papers/volume5/lavrac04a/lavrac04a.pdf\",\n        children: \"CN2-SD\"\n      }), \" algorithms were also implemented.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hopefully, the addition to the Orange software suite will benefit both novice and expert users looking advance their knowledge in a particular area of study, through a better understanding of given predictions and underlying argumentation.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"rule-induction-part-i-scripting"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/save-your-graphs/index.html b/blog/save-your-graphs/index.html
index 4a41d111b..1b65f9478 100644
--- a/blog/save-your-graphs/index.html
+++ b/blog/save-your-graphs/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, images, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Save your graphs!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 25, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>If you are often working with Orange, you probably have noticed a small button at the bottom of most visualization widgets. “Save Graph” now enables you to export graphs, charts, and hierarchical trees to your computer and use them in your reports. Because people need to see it to believe it!</p>
 <a href="/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph.png" data-gallery="true"><img alt="" loading="lazy" width="978" height="604" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph.png"/></a>
 <p>&quot;Save Graph&quot; will save visualizations to your computer.</p>
 <p>Save Graph function is available in Paint Data, Image Viewer, all visualization widgets, and a few others (list is below).</p>
 <a href="/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph2.png" data-gallery="true"><img alt="" loading="lazy" width="836" height="453" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph2.png"/></a>
 <p>Widgets with the &quot;Save Graph&quot; option.</p>
-<p>You can save visualizations in .png, .dot or .svg format. However - brace yourselves - our team is working on something even better, which will be announced in the following weeks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-09-25 08:00:15+00:00","draft":false,"title":"Save your graphs!","blog":["analysis","images","visualization"],"oldUrl":"/blog/2015/09/25/save-your-graphs/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"If you are often working with Orange, you probably have noticed a small button at the bottom of most visualization widgets. “Save Graph” now enables you to export graphs, charts, and hierarchical trees to your computer and use them in your reports. Because people need to see it to believe it!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph.png\",\n      alt: \"\",\n      width: \"978\",\n      height: \"604\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\"Save Graph\\\" will save visualizations to your computer.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Save Graph function is available in Paint Data, Image Viewer, all visualization widgets, and a few others (list is below).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph2.png\",\n      alt: \"\",\n      width: \"836\",\n      height: \"453\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Widgets with the \\\"Save Graph\\\" option.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can save visualizations in .png, .dot or .svg format. However - brace yourselves - our team is working on something even better, which will be announced in the following weeks.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"save-your-graphs"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>You can save visualizations in .png, .dot or .svg format. However - brace yourselves - our team is working on something even better, which will be announced in the following weeks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-09-25 08:00:15+00:00","draft":false,"title":"Save your graphs!","blog":["analysis","images","visualization"],"oldUrl":"/blog/2015/09/25/save-your-graphs/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"If you are often working with Orange, you probably have noticed a small button at the bottom of most visualization widgets. “Save Graph” now enables you to export graphs, charts, and hierarchical trees to your computer and use them in your reports. Because people need to see it to believe it!\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph.png\",\n      alt: \"\",\n      width: \"978\",\n      height: \"604\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\"Save Graph\\\" will save visualizations to your computer.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Save Graph function is available in Paint Data, Image Viewer, all visualization widgets, and a few others (list is below).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-save-your-graphs/__optimized-images__/blog-save-graph2.png\",\n      alt: \"\",\n      width: \"836\",\n      height: \"453\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Widgets with the \\\"Save Graph\\\" option.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can save visualizations in .png, .dot or .svg format. However - brace yourselves - our team is working on something even better, which will be announced in the following weeks.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"save-your-graphs"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, visualization, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Scatter Plot Projection Rank</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 28, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>One of the nicest and surely most useful visualization widgets in Orange is <strong>Scatter Plot</strong>. The widget displays a 2-D plot, where x and y-axes are two attributes from the data.</p>
 <a href="/blog/2015-08-scatter-plot-projection-rank/__optimized-images__/ScatterPlot1.png" data-gallery="true"><img alt="" loading="lazy" width="777" height="708" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-08-scatter-plot-projection-rank/__optimized-images__/ScatterPlot1.png"/></a>
 <p>2-dimensional scatter plot visualization</p>
@@ -162,4 +162,4 @@
 <p>Rank suggested <em>petal length</em> and <em>petal width</em> as the best pair and indeed, the visualization below is much clearer (better separated).</p>
 <a href="/blog/2015-08-scatter-plot-projection-rank/__optimized-images__/ScatterPlot4.png" data-gallery="true"><img alt="" loading="lazy" width="777" height="708" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-08-scatter-plot-projection-rank/__optimized-images__/ScatterPlot4.png"/></a>
 <p>Scatter Plot once the visualization is optimized.</p>
-<p>Have fun trying out this and other visualization widgets!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-08-28 14:05:07+00:00","draft":false,"title":"Scatter Plot Projection Rank","blog":["orange3","visualization","widget"],"oldUrl":"/blog/2015/08/28/scatter-plot-projection-rank/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"One of the nicest and surely most useful visualization widgets in Orange is \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \". 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+<p>Have fun trying out this and other visualization widgets!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-08-28 14:05:07+00:00","draft":false,"title":"Scatter Plot Projection Rank","blog":["orange3","visualization","widget"],"oldUrl":"/blog/2015/08/28/scatter-plot-projection-rank/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"One of the nicest and surely most useful visualization widgets in Orange is \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \". 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Scatter Plots: the Tour</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Scatter Plots: the Tour"/><meta property="og:description" content="Scatter Plot has recently been renovated and it is time to present some essential tricks for working with the widget!"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-12-21"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="interactive visualization"/><meta property="article:tag" content="scatter plot"/><meta property="article:tag" content="visualization"/><meta property="og:image" content="/blog/2018-12-scatter-plots-the-tour/__optimized-images__/scatter-blog.png"/><meta property="og:image:width" content="500"/><meta property="og:image:height" content="300"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>interactive visualization, scatter plot, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Scatter Plots: the Tour</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 21, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Scatter plots are surely one of the best loved visualizations in Orange. Very often, when we teach, people go back to scatter plots over and over again to see their data. We took people’s love for scatter plots to the heart and we redesigned them a bit to make them even more friendly.</p>
 <p>Our favorite still remains the Informative Projections button. This button helps you find interesting visualizations from all the combinations of your data variables. But what does interesting mean? Well, let us look at an example. Which of the two visualizations tells you more about the data?</p>
 <a href="/blog/2018-12-scatter-plots-the-tour/__optimized-images__/iris1.png" data-gallery="true"><img alt="" loading="lazy" width="523" height="590" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-12-scatter-plots-the-tour/__optimized-images__/iris1.png"/></a>
@@ -167,4 +167,4 @@
 <p>You can see there are many great thing you can do with Scatter Plot. Finally, we have added a nice touch to the visualization.</p>
 <a href="/blog/2018-12-scatter-plots-the-tour/iris5.gif" data-gallery="true"><img alt="" loading="lazy" width="1392" height="1080" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-12-scatter-plots-the-tour/iris5.gif"/></a>
 <p>Yes, setting the size of the attribute is now animated! 🙂</p>
-<p>Happy holidays, everyone!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2018-12-21","draft":false,"title":"Scatter Plots: the Tour","thumbImage":"scatter-blog.png","blog":["interactive visualization","scatter plot","visualization"],"shortExcerpt":"Scatter Plot has recently been renovated and it is time to present some essential tricks for working with the widget!","longExcerpt":"Scatter Plot has recently been renovated (under the hood and in GUI), so now it is time to present some essential tricks for working with the cool visualization!","oldUrl":"/blog/2018/12/21/scatter-plots-the-tour/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Scatter plots are surely one of the best loved visualizations in Orange. Very often, when we teach, people go back to scatter plots over and over again to see their data. We took people’s love for scatter plots to the heart and we redesigned them a bit to make them even more friendly.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our favorite still remains the Informative Projections button. This button helps you find interesting visualizations from all the combinations of your data variables. But what does interesting mean? Well, let us look at an example. Which of the two visualizations tells you more about the data?\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-12-scatter-plots-the-tour/iris1.png\",\n      width: \"523\",\n      height: \"590\",\n      src: \"/blog/2018-12-scatter-plots-the-tour/__optimized-images__/iris1.png\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-12-scatter-plots-the-tour/iris2.png\",\n      width: \"523\",\n      height: \"590\",\n      src: \"/blog/2018-12-scatter-plots-the-tour/__optimized-images__/iris2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We’d say it is the right one. Why? Because now we know that the combination of petal length and petal width nicely separates the classes!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course, Informative Projections button will only work when you have set a class (target) variable.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In scatter plot, you can set also the color of the data points (class is selected by default), the size of the points and the shape. This means you can add three new layers of information to your data, but we warn you not to overuse them. This usually looks very incomprehensible, even though it packs a lot of information.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-12-scatter-plots-the-tour/heart-disease.png\",\n      src: \"70%\",\n      width: \"1600\",\n      height: \"1299\",\n      src: \"/blog/2018-12-scatter-plots-the-tour/__optimized-images__/heart-disease.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You might notice, that in the current version of Orange, you can no longer select discrete attributes in Scatter Plot. This is entirely intentional. Scatter plots are best at showing the relationship between two numeric variables, such as in the two examples above. Categorical variables are much better represented with Box Plots, histograms (in Distributions) or in Mosaic Display.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-12-scatter-plots-the-tour/titanic.png\",\n      src: \"70%\",\n      width: \"1524\",\n      height: \"1022\",\n      src: \"/blog/2018-12-scatter-plots-the-tour/__optimized-images__/titanic.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Above, we have presented the same information for titanic data set in different visualizations, that are particularly suitable for categorical variables.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot also enables so cool tricks. Just like in most visualizations in Orange, I can select a part of the data and observe the subset downstream. Or the other way around. I have a particular subset I wish to observe and I can pass it to Scatter Plot widget, which will highlight selected data instances.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-12-scatter-plots-the-tour/iris4.png\",\n      src: \"70%\",\n      width: \"1600\",\n      height: \"797\",\n      src: \"/blog/2018-12-scatter-plots-the-tour/__optimized-images__/iris4.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is also true for all other point-based visualizations in Orange, namely t-SNE, MDS, Radviz, Freeviz, and Linear Projection.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can see there are many great thing you can do with Scatter Plot. 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+<p>Happy holidays, everyone!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2018-12-21","draft":false,"title":"Scatter Plots: the Tour","thumbImage":"scatter-blog.png","blog":["interactive visualization","scatter plot","visualization"],"shortExcerpt":"Scatter Plot has recently been renovated and it is time to present some essential tricks for working with the widget!","longExcerpt":"Scatter Plot has recently been renovated (under the hood and in GUI), so now it is time to present some essential tricks for working with the cool visualization!","oldUrl":"/blog/2018/12/21/scatter-plots-the-tour/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Scatter plots are surely one of the best loved visualizations in Orange. Very often, when we teach, people go back to scatter plots over and over again to see their data. We took people’s love for scatter plots to the heart and we redesigned them a bit to make them even more friendly.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our favorite still remains the Informative Projections button. This button helps you find interesting visualizations from all the combinations of your data variables. But what does interesting mean? Well, let us look at an example. 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Because now we know that the combination of petal length and petal width nicely separates the classes!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course, Informative Projections button will only work when you have set a class (target) variable.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In scatter plot, you can set also the color of the data points (class is selected by default), the size of the points and the shape. This means you can add three new layers of information to your data, but we warn you not to overuse them. 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I have a particular subset I wish to observe and I can pass it to Scatter Plot widget, which will highlight selected data instances.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2018-12-scatter-plots-the-tour/iris4.png\",\n      src: \"70%\",\n      width: \"1600\",\n      height: \"797\",\n      src: \"/blog/2018-12-scatter-plots-the-tour/__optimized-images__/iris4.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is also true for all other point-based visualizations in Orange, namely t-SNE, MDS, Radviz, Freeviz, and Linear Projection.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can see there are many great thing you can do with Scatter Plot. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, examples, scripting, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Scripting with Time Variable</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 10, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It&#x27;s always fun to play around with data. And since Orange can, as of a few months ago, read temporal data, we decided to parse some data we had and put it into Orange.</p>
 <p>TimeVariable is an extended class of continuous variable and it works with properly formated ISO standard datetime (Y-M-D h:m:s). Oftentimes our original data is not in the right format and needs to be edited first, so Orange can read it. Python&#x27;s own datetime module is of great help. You can give it any date format and tell it how to interpret it in the argument.</p>
 <pre><code>    import datetime
@@ -184,4 +184,4 @@
 <p>Remember, if your data has string variables, they will always be in meta attributes.</p>
 <pre><code>    domain = Domain([&quot;some_attribute1&quot;, &quot;other_attribute2&quot;], metas=[&quot;some_string_variable&quot;])
 </code></pre>
-<p>Have fun scripting!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-06-10 12:37:50+00:00","draft":false,"title":"Scripting with Time Variable","blog":["data","examples","scripting","orange3"],"oldUrl":"/blog/2016/06/10/scripting-with-time-variable/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It's always fun to play around with data. And since Orange can, as of a few months ago, read temporal data, we decided to parse some data we had and put it into Orange.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"TimeVariable is an extended class of continuous variable and it works with properly formated ISO standard datetime (Y-M-D h:m:s). Oftentimes our original data is not in the right format and needs to be edited first, so Orange can read it. Python's own datetime module is of great help. You can give it any date format and tell it how to interpret it in the argument.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    import datetime\\n    date = \\\"13.03.2013 13:13:31\\\"\\n    new_date = str(datetime.datetime.strptime(date, \\\"%d.%m.%Y %H:%M:%S\\\"))\\n    \u003e\u003e\u003e '2013-03-13 13:13:31'\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Do this for all your datetime attributes. 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+<p>Have fun scripting!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-06-10 12:37:50+00:00","draft":false,"title":"Scripting with Time Variable","blog":["data","examples","scripting","orange3"],"oldUrl":"/blog/2016/06/10/scripting-with-time-variable/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It's always fun to play around with data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>semantic analysis, text mining, corpus, keywords</strong></p><h1 class="sc-c67a4900-0 loxVcf">Semantic Analysis of Documents</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 17, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Our recent project with the Ministry of Public Administration comprises building a <a href="https://nio.gov.si/nio/asset/semanticni+analizator+besedil?lang=en">semantic analysis pipeline in Orange</a>, enabling the users to quickly and efficiently explore the content of documents, compare a subset against the corpus, extract keywords, and semantically explore document maps. If this sounds too vague, don&#x27;t worry, here&#x27;s a quick demo on how to perform semantic analysis in Orange.</p>
 <p>First, we will use the pre-prepared corpus of <a href="https://predlagam.vladi.si/">proposals to the government</a>, which you can download <a href="http://file.biolab.si/text-semantics/data/predlogi-vladi-1k.tab">here</a>. These are the initiatives which the citizens of Slovenia propose to the current government for consideration. The present corpus contains 1093 such proposals.</p>
 <a href="/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf1.png" data-gallery="true"><img alt="" loading="lazy" width="256" height="113" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf1.png"/></a>
@@ -175,4 +175,4 @@
 <p><strong>Score Documents</strong> returns keyword scores for each document. Let us pass the scored documents to another <strong>t-SNE</strong> widget. If we set the color and the size of the points to &quot;Word Count&quot; variable, t-SNE plot will expose the documents with the highest scores. These documents talk the most about students and work. A great thing is that we can see documents with high scores that were not a part of our selection, which means the general bottom-right area contains documents relating to this topic.</p>
 <a href="/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-tsne2.png" data-gallery="true"><img alt="" loading="lazy" width="1832" height="1160" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-tsne2.png"/></a>
 <a href="/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="1734" height="562" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-workflow.png"/></a>
-<p>Now try selecting a different subset yourself and see what the documents are about. You can use any corpus you want, even the ones that come with Orange.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-09-17","draft":false,"title":"Semantic Analysis of Documents","thumbImage":"2021-09-17-seman.png","frontPageImage":"2021-09-17-seman.png","blog":["semantic analysis","text mining","corpus","keywords"],"shortExcerpt":"How to use Text add-on for semantic analysis of documents.","longExcerpt":"How to use Text add-on to extract keywords from documents, score documents on keywords, and display semantic content in a map.","x2images":true,"oldUrl":"/blog/2021/2021-09-17-semantic-analysis/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Our recent project with the Ministry of Public Administration comprises building a \", _jsx(_components.a, {\n        href: \"https://nio.gov.si/nio/asset/semanticni+analizator+besedil?lang=en\",\n        children: \"semantic analysis pipeline in Orange\"\n      }), \", enabling the users to quickly and efficiently explore the content of documents, compare a subset against the corpus, extract keywords, and semantically explore document maps. If this sounds too vague, don't worry, here's a quick demo on how to perform semantic analysis in Orange.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"First, we will use the pre-prepared corpus of \", _jsx(_components.a, {\n        href: \"https://predlagam.vladi.si/\",\n        children: \"proposals to the government\"\n      }), \", which you can download \", _jsx(_components.a, {\n        href: \"http://file.biolab.si/text-semantics/data/predlogi-vladi-1k.tab\",\n        children: \"here\"\n      }), \". These are the initiatives which the citizens of Slovenia propose to the current government for consideration. The present corpus contains 1093 such proposals.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf1.png\",\n      width: \"256\",\n      height: \"113\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf1.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-corpus.png\",\n      width: \"605\",\n      height: \"431\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-corpus.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Each proposal contains a title, the content of the proposal, the author, the date when it was published, number of upvotes, and so on. But for a thousand proposals, it would take a long time to read all of them and see which policy areas they cover. Instead, we will use two new Orange widgets to determine the content (main keywords) of a subset of documents.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-corpus-viewer.png\",\n      width: \"1207\",\n      height: \"628\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-corpus-viewer.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As always, we will first preprocess the corpus to create tokens, the core units of our analysis. The preprocessing pipeline is sequential; first, we lowercase the text, then we split the text into words (this is what the regular expression \", _jsx(_components.code, {\n        children: \"\\\\w+.\"\n      }), \" does), transform the words into lemmas with UDPipe, and finally remove stopwords from the list (such as \\\"in\\\", \\\"da\\\", \\\"če\\\"). Since we are working with a Slovenian language text, we have to select the corresponding models and stopword lists.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf2.png\",\n      width: \"254\",\n      height: \"114\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf2.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-preprocess-text.png\",\n      width: \"984\",\n      height: \"1030\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-preprocess-text.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"After preprocessing, we build a document-term matrix using the \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" widget with the Count+IDF setting. Next, we pass the data to \", _jsx(_components.strong, {\n        children: \"t-SNE\"\n      }), \" to observe the document map. t-SNE takes the document-term matrix and finds a 2D projection, where similar documents lie close together.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf3.png\",\n      width: \"282\",\n      height: \"112\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf3.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-tsne1.png\",\n      width: \"2240\",\n      height: \"1456\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-tsne1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we will select a small subset of the document, say in the lower right corner, where we have a nice cluster of points. The question is, what are these documents talking about?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use \", _jsx(_components.strong, {\n        children: \"Extract Keywords\"\n      }), \" widget to find the most significant keywords in the selection. There are several methods we can use, even the popuar \", _jsx(_components.a, {\n        href: \"https://repositorio.inesctec.pt/bitstream/123456789/7623/1/P-00N-NF5.pdf\",\n        children: \"YAKE!\"\n      }), \", but we will go with a simple TF-IDF method, which takes the words with the highest TF-IDF score. Note that the vectorizer uses the default sklearn's \", _jsx(_components.a, {\n        href: \"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\",\n        children: \"TfidfVectorizer\"\n      }), \" settings, that is the tf-idf transform with L2 norm, keeping the passed tokens as they are.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf4.png\",\n      width: \"288\",\n      height: \"118\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf4.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-extract-keywords.png\",\n      width: \"643\",\n      height: \"548\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-extract-keywords.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It looks like the top words characterizing the selected subcorpus are \\\"študent\\\" (\", _jsx(_components.em, {\n        children: \"student\"\n      }), \"), \\\"delati\\\" (\", _jsx(_components.em, {\n        children: \"to work\"\n      }), \"), and \\\"delo\\\" (\", _jsx(_components.em, {\n        children: \"the work\"\n      }), \"). Apparently, the documents talk mostly about student work. Let us explore this a bit further. It would be nice to have a certain score attached to the documents, which would correspond to how much a document talks about student work. In other words, we would like to score the documents based on how many of the selected words they contain (and in what proportion).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To achieve this, we will use \", _jsx(_components.strong, {\n        children: \"Score Documents\"\n      }), \". We will pass it the document-term matrix and the list of selected keywords from \", _jsx(_components.strong, {\n        children: \"Extract Keywords\"\n      }), \". The widget again offers several different ways of scoring documents. A simple way to score them is to compute how often selected words appear in each document, which corresponds to the \\\"Word Count\\\" method.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf5.png\",\n      width: \"566\",\n      height: \"218\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf5.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-score-documents.png\",\n      width: \"1007\",\n      height: \"634\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-score-documents.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Score Documents\"\n      }), \" returns keyword scores for each document. Let us pass the scored documents to another \", _jsx(_components.strong, {\n        children: \"t-SNE\"\n      }), \" widget. If we set the color and the size of the points to \\\"Word Count\\\" variable, t-SNE plot will expose the documents with the highest scores. These documents talk the most about students and work. A great thing is that we can see documents with high scores that were not a part of our selection, which means the general bottom-right area contains documents relating to this topic.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-tsne2.png\",\n      width: \"1832\",\n      height: \"1160\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-tsne2.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-workflow.png\",\n      width: \"1734\",\n      height: \"562\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now try selecting a different subset yourself and see what the documents are about. You can use any corpus you want, even the ones that come with Orange.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-seman.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"semantic-analysis-of-documents"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
\ No newline at end of file
+<p>Now try selecting a different subset yourself and see what the documents are about. You can use any corpus you want, even the ones that come with Orange.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-09-17","draft":false,"title":"Semantic Analysis of Documents","thumbImage":"2021-09-17-seman.png","frontPageImage":"2021-09-17-seman.png","blog":["semantic analysis","text mining","corpus","keywords"],"shortExcerpt":"How to use Text add-on for semantic analysis of documents.","longExcerpt":"How to use Text add-on to extract keywords from documents, score documents on keywords, and display semantic content in a map.","x2images":true,"oldUrl":"/blog/2021/2021-09-17-semantic-analysis/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    code: \"code\",\n    strong: \"strong\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Our recent project with the Ministry of Public Administration comprises building a \", _jsx(_components.a, {\n        href: \"https://nio.gov.si/nio/asset/semanticni+analizator+besedil?lang=en\",\n        children: \"semantic analysis pipeline in Orange\"\n      }), \", enabling the users to quickly and efficiently explore the content of documents, compare a subset against the corpus, extract keywords, and semantically explore document maps. 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The present corpus contains 1093 such proposals.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf1.png\",\n      width: \"256\",\n      height: \"113\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf1.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-corpus.png\",\n      width: \"605\",\n      height: \"431\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-corpus.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Each proposal contains a title, the content of the proposal, the author, the date when it was published, number of upvotes, and so on. But for a thousand proposals, it would take a long time to read all of them and see which policy areas they cover. Instead, we will use two new Orange widgets to determine the content (main keywords) of a subset of documents.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-corpus-viewer.png\",\n      width: \"1207\",\n      height: \"628\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-corpus-viewer.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"As always, we will first preprocess the corpus to create tokens, the core units of our analysis. The preprocessing pipeline is sequential; first, we lowercase the text, then we split the text into words (this is what the regular expression \", _jsx(_components.code, {\n        children: \"\\\\w+.\"\n      }), \" does), transform the words into lemmas with UDPipe, and finally remove stopwords from the list (such as \\\"in\\\", \\\"da\\\", \\\"če\\\"). Since we are working with a Slovenian language text, we have to select the corresponding models and stopword lists.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf2.png\",\n      width: \"254\",\n      height: \"114\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf2.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-preprocess-text.png\",\n      width: \"984\",\n      height: \"1030\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-preprocess-text.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"After preprocessing, we build a document-term matrix using the \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" widget with the Count+IDF setting. Next, we pass the data to \", _jsx(_components.strong, {\n        children: \"t-SNE\"\n      }), \" to observe the document map. t-SNE takes the document-term matrix and finds a 2D projection, where similar documents lie close together.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf3.png\",\n      width: \"282\",\n      height: \"112\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf3.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-tsne1.png\",\n      width: \"2240\",\n      height: \"1456\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-tsne1.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we will select a small subset of the document, say in the lower right corner, where we have a nice cluster of points. The question is, what are these documents talking about?\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use \", _jsx(_components.strong, {\n        children: \"Extract Keywords\"\n      }), \" widget to find the most significant keywords in the selection. There are several methods we can use, even the popuar \", _jsx(_components.a, {\n        href: \"https://repositorio.inesctec.pt/bitstream/123456789/7623/1/P-00N-NF5.pdf\",\n        children: \"YAKE!\"\n      }), \", but we will go with a simple TF-IDF method, which takes the words with the highest TF-IDF score. Note that the vectorizer uses the default sklearn's \", _jsx(_components.a, {\n        href: \"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\",\n        children: \"TfidfVectorizer\"\n      }), \" settings, that is the tf-idf transform with L2 norm, keeping the passed tokens as they are.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf4.png\",\n      width: \"288\",\n      height: \"118\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf4.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-extract-keywords.png\",\n      width: \"643\",\n      height: \"548\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-extract-keywords.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It looks like the top words characterizing the selected subcorpus are \\\"študent\\\" (\", _jsx(_components.em, {\n        children: \"student\"\n      }), \"), \\\"delati\\\" (\", _jsx(_components.em, {\n        children: \"to work\"\n      }), \"), and \\\"delo\\\" (\", _jsx(_components.em, {\n        children: \"the work\"\n      }), \"). Apparently, the documents talk mostly about student work. Let us explore this a bit further. It would be nice to have a certain score attached to the documents, which would correspond to how much a document talks about student work. In other words, we would like to score the documents based on how many of the selected words they contain (and in what proportion).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To achieve this, we will use \", _jsx(_components.strong, {\n        children: \"Score Documents\"\n      }), \". We will pass it the document-term matrix and the list of selected keywords from \", _jsx(_components.strong, {\n        children: \"Extract Keywords\"\n      }), \". The widget again offers several different ways of scoring documents. A simple way to score them is to compute how often selected words appear in each document, which corresponds to the \\\"Word Count\\\" method.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-wf5.png\",\n      width: \"566\",\n      height: \"218\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-wf5.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-score-documents.png\",\n      width: \"1007\",\n      height: \"634\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-score-documents.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Score Documents\"\n      }), \" returns keyword scores for each document. Let us pass the scored documents to another \", _jsx(_components.strong, {\n        children: \"t-SNE\"\n      }), \" widget. If we set the color and the size of the points to \\\"Word Count\\\" variable, t-SNE plot will expose the documents with the highest scores. These documents talk the most about students and work. A great thing is that we can see documents with high scores that were not a part of our selection, which means the general bottom-right area contains documents relating to this topic.\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-tsne2.png\",\n      width: \"1832\",\n      height: \"1160\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-tsne2.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-09-semantic-analysis/2021-09-17-workflow.png\",\n      width: \"1734\",\n      height: \"562\",\n      src: \"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-workflow.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now try selecting a different subset yourself and see what the documents are about. You can use any corpus you want, even the ones that come with Orange.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2021-09-semantic-analysis/__optimized-images__/2021-09-17-seman.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"semantic-analysis-of-documents"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>future, history</strong></p><h1 class="sc-c67a4900-0 loxVcf">Short history of Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 23, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Few weeks back we celebrated <a href="http://ailab.ijs.si/dunja/TuringSLAIS-2012/">20 years of Slovene Artificial Intelligence Society</a>. I have much enjoyed Ivan Bratko&#x27;s talk on AI history, and his account of events as triggered by late Donald Michie. Many interesting talks followed, including highlights by Stephen Muggleton and Claude Sammut.</p>
-<p>The last talk of the event was on Orange. Janez talked about its birth, history and future prospects. You can see his presentation on <a href="http://videolectures.net/is2012_demsar_orange/">videolectures</a> and check out the <a href="http://ailab.ijs.si/dunja/TuringSLAIS-2012/Papers/Demsar_Orange.pdf">paper</a> with lecture&#x27;s notes.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2012-10-23 08:58:00+00:00","draft":false,"title":"Short history of Orange","blog":["future","history"],"oldUrl":"/blog/2012/10/23/short-history-of-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Few weeks back we celebrated \", _jsx(_components.a, {\n        href: \"http://ailab.ijs.si/dunja/TuringSLAIS-2012/\",\n        children: \"20 years of Slovene Artificial Intelligence Society\"\n      }), \". I have much enjoyed Ivan Bratko's talk on AI history, and his account of events as triggered by late Donald Michie. Many interesting talks followed, including highlights by Stephen Muggleton and Claude Sammut.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The last talk of the event was on Orange. Janez talked about its birth, history and future prospects. You can see his presentation on \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_demsar_orange/\",\n        children: \"videolectures\"\n      }), \" and check out the \", _jsx(_components.a, {\n        href: \"http://ailab.ijs.si/dunja/TuringSLAIS-2012/Papers/Demsar_Orange.pdf\",\n        children: \"paper\"\n      }), \" with lecture's notes.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"short-history-of-orange"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>The last talk of the event was on Orange. Janez talked about its birth, history and future prospects. You can see his presentation on <a href="http://videolectures.net/is2012_demsar_orange/">videolectures</a> and check out the <a href="http://ailab.ijs.si/dunja/TuringSLAIS-2012/Papers/Demsar_Orange.pdf">paper</a> with lecture&#x27;s notes.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2012-10-23 08:58:00+00:00","draft":false,"title":"Short history of Orange","blog":["future","history"],"oldUrl":"/blog/2012/10/23/short-history-of-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Few weeks back we celebrated \", _jsx(_components.a, {\n        href: \"http://ailab.ijs.si/dunja/TuringSLAIS-2012/\",\n        children: \"20 years of Slovene Artificial Intelligence Society\"\n      }), \". I have much enjoyed Ivan Bratko's talk on AI history, and his account of events as triggered by late Donald Michie. Many interesting talks followed, including highlights by Stephen Muggleton and Claude Sammut.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The last talk of the event was on Orange. Janez talked about its birth, history and future prospects. You can see his presentation on \", _jsx(_components.a, {\n        href: \"http://videolectures.net/is2012_demsar_orange/\",\n        children: \"videolectures\"\n      }), \" and check out the \", _jsx(_components.a, {\n        href: \"http://ailab.ijs.si/dunja/TuringSLAIS-2012/Papers/Demsar_Orange.pdf\",\n        children: \"paper\"\n      }), \" with lecture's notes.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"short-history-of-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Single cell analytics workshop at HHMI | Janelia</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 05, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="https://www.janelia.org">HHMI | Janelia</a> is one of the prettiest researcher campuses I have ever visited. Located in Ashburn, VA, about 20 minutes from Washington Dulles airport, it is conveniently located yet, in a way, secluded from the buzz of the capital. We adored the guest house with a view of the lake, tasty Janelia-style breakfast (hash-browns with two eggs and sausage, plus a bagel with cream cheese) in the on-campus pub, beautifully-designed interiors to foster collaborations and interactions, and late-evening discussions in the in-house pub.</p>
 <p>All these thanks to the invitation of Andrew Lemire, a manager of a shared high-throughput genomics resource, and Dr. Vilas Menon, a mathematician specializing in quantitative genomics. With Andy and Vilas, we have been collaborating in the past few months on trying to devise a simple and intuitive tool for analysis of single-cell gene expression data. Single cell high-throughput technology is one of the latest approaches that allow us to see what is happening within a single cell, and it does that by simultaneously scanning through potentially thousands of cells. That generates loads of data, and apparently, we have been trying to fit Orange for single-cell data analysis task.</p>
 <a href="/blog/2018-03-single-cell-analytics-workshop-at-hhmi-janelia/__optimized-images__/hhmi-annoucement.jpg" data-gallery="true"><img alt="" loading="lazy" width="1200" height="736" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-03-single-cell-analytics-workshop-at-hhmi-janelia/__optimized-images__/hhmi-annoucement.jpg"/></a>
@@ -158,4 +158,4 @@
 <a href="/blog/2018-03-single-cell-analytics-workshop-at-hhmi-janelia/__optimized-images__/hhmi-workshop.jpg" data-gallery="true"><img alt="" loading="lazy" width="1200" height="539" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-03-single-cell-analytics-workshop-at-hhmi-janelia/__optimized-images__/hhmi-workshop.jpg"/></a>
 <p>Orange, or rather, <a href="http://singlecell.biolab.si">scOrange</a>, worked as expected, and hands-on workshop was smooth, despite testing the software on some rather large data sets. Our Orange add-on for single-cell analytics is still in early stage of development, but already has some advanced features like biomarker discovery and tools for characterization of cell clusters that may help in revealing hidden relations between genes and phenotypes. Thanks to Andy and Vilas, Janelia proved an excellent proving ground for <a href="http://singlecell.biolab.si">scOrange</a>, and we are looking forward to our next hands-on single-cell analytics workshop in Houston.</p>
 <a href="/blog/2018-03-single-cell-analytics-workshop-at-hhmi-janelia/__optimized-images__/hhmi-tsne-visualisation.png" data-gallery="true"><img alt="" loading="lazy" width="940" height="694" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-03-single-cell-analytics-workshop-at-hhmi-janelia/__optimized-images__/hhmi-tsne-visualisation.png"/></a>
-<p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2/">Hands-On Data Mining Course in Houston</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2018-03-05 22:22:49+00:00","draft":false,"title":"Single cell analytics workshop at HHMI | Janelia","blog":["orange3"],"oldUrl":"/blog/2018/03/05/single-cell-analytics-workshop-at-hhmi-janelia/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://www.janelia.org\",\n        children: \"HHMI | Janelia\"\n      }), \" is one of the prettiest researcher campuses I have ever visited. 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+<p><strong>Related:</strong> <a href="/blog/2016/09/15/data-mining-in-houston-2/">Hands-On Data Mining Course in Houston</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2018-03-05 22:22:49+00:00","draft":false,"title":"Single cell analytics workshop at HHMI | Janelia","blog":["orange3"],"oldUrl":"/blog/2018/03/05/single-cell-analytics-workshop-at-hhmi-janelia/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://www.janelia.org\",\n        children: \"HHMI | Janelia\"\n      }), \" is one of the prettiest researcher campuses I have ever visited. 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+++ b/blog/single-cell-data-science-for-everyone/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gene ontology, genomics, RNA-seq, scOrange, single cell</strong></p><h1 class="sc-c67a4900-0 loxVcf">Single-Cell Data Science for Everyone</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaz Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 01, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Molecular biologists have in the past twenty years invented technologies that can collect abundant experimental data. One such technique is single-cell RNA-seq, which, very simplified, can measure the activity of genes in possibly large collections of cells. The interpretation of such data can tell us about the heterogeneity of cells, cell types, or provide information on their development.</p>
 <p>Typical analysis toolboxes for single-cell data are available in R and Python and, most notably, include <a href="https://satijalab.org/seurat/" target="_blank" rel="noreferrer">Seurat</a> and <a href="https://scanpy.readthedocs.io/en/stable/" target="_blank" rel="noreferrer">scanpy</a>, but they lack interactive visualizations and simplicity of Orange. Since the fall of 2017, we have been developing an extension of Orange, which is now (almost) ready. It has even been packed into its own installer. The first real test of the software was in early 2018 through <a href="/blog/2018/03/05/single-cell-analytics-workshop-at-hhmi-janelia/" target="_blank" rel="noreferrer">a one day workshop at Janelia Research Campus</a>. On March 6, and with a much more refined version of the software, we have now repeated the hands-on workshop at the University of Pavia.</p>
 <p>\</p>
@@ -161,4 +161,4 @@
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On March 6, and with a much more refined version of the software, we have now repeated the hands-on workshop at the University of Pavia.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2019-3-single-cell-data-science-for-everyone/2019-pavia-group.jpg\",\n      src: \"80%\",\n      width: \"640\",\n      height: \"480\",\n      src: \"/blog/2019-3-single-cell-data-science-for-everyone/__optimized-images__/2019-pavia-group.jpg\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The five-hour workshop covered both the essentials of data analysis and single cell analytics. The topics included data preprocessing, clustering, and two-dimensional embedding, as well as working with marker genes, differential expression analysis, and interpretation of clusters through gene ontology analysis.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I want to thank \", _jsx(LinkNew, {\n        url: \"http://www.labmedinfo.org/people/bellazzi\",\n        name: \"Prof. Dr. Riccardo Bellazzi\"\n      }), \" and his team for the organization, and Erasmus program for financial support. I have been a frequent guest to Pavia, and learn something new every time I am there. Besides meeting new students and colleagues that attended the workshop and hearing about their data analysis challenges, this time I have also learned about a dish I had never had before in all my Italian travels. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics</strong></p><h1 class="sc-c67a4900-0 loxVcf">Single-sample GSEA is now in Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Martin Špendl</strong></p><p class="sc-b9a2839b-4 ehbERY">Feb 23, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the world of biology, everything revolves around genes. They carry information about the essential functions of cells and create mRNA molecules that execute these functions. Biologists like to extract information about gene expression by simply counting the number of mRNA molecules produced by genes. There are more than 20,000 genes in the human genome, which makes the expression matrix very wide. Biologists like identifying important genes in different processes and grouping them into gene sets. By comparing their expression over different samples, they can gain insight into the mechanism of change.</p>
 <p>One of the widely used methods for assigning a value to the expression of a gene set is the single-sample extension of Gene Set Enrichment Analysis (ssGSEA), proposed by <a href="https://www.nature.com/articles/nature08460">Barbie et al.</a>. The ssGSEA method gives a score related to the overexpression of genes in an individual sample, contrary to GSEA, which calculates the change across multiple samples. The algorithm sums the contributions of genes related to their rank in an ordered expression matrix. High expression values contribute positively to the score, while lower values contribute negatively.</p>
 <p>The method was first introduced as a signature projection method and quickly gained popularity for assigning gene set scores to samples. It’s most widely used in cancer biology to discover novel tumour subtypes, search for new prognostic markers and uncover the underlying tumour-driving process. It’s now available in Orange as a <strong>Single sample scoring</strong> widget from the Bioinformatics <a href="https://github.com/biolab/orange3-bioinformatics">add-on</a>.</p>
 <a href="/blog/2023-02-ssgsea/__optimized-images__/2023-02-23-ssgsea.png" data-gallery="true"><img alt="" loading="lazy" width="1940" height="978" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-02-ssgsea/__optimized-images__/2023-02-23-ssgsea.png"/></a>
-<p>The illustration above demonstrates how to perform single-sample GSEA analysis on the TCGA Cervical carcinoma data (TCGA-CESC) from the <strong>Datasets</strong> widget. ssGSEA method is the default option for scoring in the <strong>Single sample scoring</strong> widget, which takes two parameters: expression data through the <strong>Genes</strong> widget and gene sets through the <strong>Gene Sets</strong> widget. As seen in the <strong>Data Table</strong> widget, sample scores are added as metadata parameters to the original data. Users can now easily compute gene set scores and enrich their analysis.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Martin Špendl","date":"2023-02-23","draft":false,"title":"Single-sample GSEA is now in Orange","thumbImage":"2023-02-23-ssgsea.png","frontPageImage":"2023-02-23-ssgsea.png","blog":["bioinformatics"],"shortExcerpt":"Single-sample extension of Gene Set Enrichment Analysis now in Bioinformatics add-on.","longExcerpt":"Single-sample extension of Gene Set Enrichment Analysis now in Bioinformatics add-on.","oldUrl":"/blog/2023/2023-02-23-ssgsea/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In the world of biology, everything revolves around genes. 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It’s now available in Orange as a \", _jsx(_components.strong, {\n        children: \"Single sample scoring\"\n      }), \" widget from the Bioinformatics \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-bioinformatics\",\n        children: \"add-on\"\n      }), \".\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-02-ssgsea/2023-02-23-ssgsea.png\",\n      width: \"1940\",\n      height: \"978\",\n      src: \"/blog/2023-02-ssgsea/__optimized-images__/2023-02-23-ssgsea.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The illustration above demonstrates how to perform single-sample GSEA analysis on the TCGA Cervical carcinoma data (TCGA-CESC) from the \", _jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget. ssGSEA method is the default option for scoring in the \", _jsx(_components.strong, {\n        children: \"Single sample scoring\"\n      }), \" widget, which takes two parameters: expression data through the \", _jsx(_components.strong, {\n        children: \"Genes\"\n      }), \" widget and gene sets through the \", _jsx(_components.strong, {\n        children: \"Gene Sets\"\n      }), \" widget. 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+<p>The illustration above demonstrates how to perform single-sample GSEA analysis on the TCGA Cervical carcinoma data (TCGA-CESC) from the <strong>Datasets</strong> widget. ssGSEA method is the default option for scoring in the <strong>Single sample scoring</strong> widget, which takes two parameters: expression data through the <strong>Genes</strong> widget and gene sets through the <strong>Gene Sets</strong> widget. As seen in the <strong>Data Table</strong> widget, sample scores are added as metadata parameters to the original data. Users can now easily compute gene set scores and enrich their analysis.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Martin Špendl","date":"2023-02-23","draft":false,"title":"Single-sample GSEA is now in Orange","thumbImage":"2023-02-23-ssgsea.png","frontPageImage":"2023-02-23-ssgsea.png","blog":["bioinformatics"],"shortExcerpt":"Single-sample extension of Gene Set Enrichment Analysis now in Bioinformatics add-on.","longExcerpt":"Single-sample extension of Gene Set Enrichment Analysis now in Bioinformatics add-on.","oldUrl":"/blog/2023/2023-02-23-ssgsea/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In the world of biology, everything revolves around genes. 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diff --git a/blog/socio-economic-data-at-your-fingertips/index.html b/blog/socio-economic-data-at-your-fingertips/index.html
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@@ -167,4 +167,4 @@
 <p>I can observe the data in the Data Table. Each row represents a country and each column an indicator. Now I can sort the data in the Data Table, observe means and medians in Box Plot, cluster countries based on given indicators and so on.</p>
 <a href="/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_data-table.png" data-gallery="true"><img alt="" loading="lazy" width="1270" height="713" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_data-table.png"/></a>
 <p>Enjoy exploring the data and let us know if you discover something interesting!</p>
-<a href="/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="337" height="143" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-08-31","draft":false,"title":"Socio-economic data at your fingertips","thumbImage":"2022-08-31_happy.png","frontPageImage":"2022-08-31_happy.png","blog":["data","add-on","widgets","economy"],"shortExcerpt":"New World Happiness add-on joins the Orange family","longExcerpt":"New World Happiness add-on for retrieving socio-economic data from the OECD database joins the Orange family","oldUrl":"/blog/2022/2022-08-31-world-happiness/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"A new add-on has joined the Orange family! It is called World Happiness and it is intended for retrieving socio-econimic data from global databases. Go to Options --\u003e Add-ons and select World Happiness from the list to install it.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_add-on.png\",\n      width: \"744\",\n      height: \"682\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_add-on.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Currently, it contains a single wigdet - Socioeconomic Indices. But fret not! This one widget can do many things.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_widget.png\",\n      width: \"1345\",\n      height: \"860\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_widget.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Say I wish to retrieve data for Europe and Central Asia, which I can select from the Countries list. I can go even further and filter by a specific country, but for now, let us go with the general region.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_country.png\",\n      width: \"670\",\n      height: \"416\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_country.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, I can set which period I wish to retrieve the data for. Say the past five years, from 2017 to 2021. On the left side, you will see some parameters. They define how many missing values we permit in the data. The first is the percentage of missing values for the indicator, say forest area in square kilometers. If more than 60% of the indicator values are missing, the indicator will not appear on the output. Similarly for the country - if more than 90% of the values are missing, the country will not appear on the output. Finally, the values will be aggregated by mean, meaning each year for each country will display an average indicator value.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_period.png\",\n      width: \"666\",\n      height: \"414\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_period.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, I will select the indicator I wish on the output. Say I am interested in forestry. I type \\\"forest\\\" in the filter. Only the indicators containing the word \\\"forest\\\" will be displayed below. Now, I select the desired indicator to send to the output. I will select all of them.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_filter.png\",\n      width: \"996\",\n      height: \"551\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_filter.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To actually send the indicators to the output, I have to move them to the selected indicators section, which is the final box in the lower right corner. If the retrieval was successful, you will see a number in the status bar at the bottom of the widget. I have retrieved 47 rows of data.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_indicators.png\",\n      width: \"994\",\n      height: \"367\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_indicators.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I can observe the data in the Data Table. Each row represents a country and each column an indicator. 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+<a href="/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="337" height="143" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-08-31","draft":false,"title":"Socio-economic data at your fingertips","thumbImage":"2022-08-31_happy.png","frontPageImage":"2022-08-31_happy.png","blog":["data","add-on","widgets","economy"],"shortExcerpt":"New World Happiness add-on joins the Orange family","longExcerpt":"New World Happiness add-on for retrieving socio-economic data from the OECD database joins the Orange family","oldUrl":"/blog/2022/2022-08-31-world-happiness/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"A new add-on has joined the Orange family! It is called World Happiness and it is intended for retrieving socio-econimic data from global databases. Go to Options --\u003e Add-ons and select World Happiness from the list to install it.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_add-on.png\",\n      width: \"744\",\n      height: \"682\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_add-on.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Currently, it contains a single wigdet - Socioeconomic Indices. But fret not! This one widget can do many things.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_widget.png\",\n      width: \"1345\",\n      height: \"860\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_widget.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Say I wish to retrieve data for Europe and Central Asia, which I can select from the Countries list. I can go even further and filter by a specific country, but for now, let us go with the general region.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_country.png\",\n      width: \"670\",\n      height: \"416\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_country.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, I can set which period I wish to retrieve the data for. Say the past five years, from 2017 to 2021. On the left side, you will see some parameters. They define how many missing values we permit in the data. The first is the percentage of missing values for the indicator, say forest area in square kilometers. If more than 60% of the indicator values are missing, the indicator will not appear on the output. Similarly for the country - if more than 90% of the values are missing, the country will not appear on the output. Finally, the values will be aggregated by mean, meaning each year for each country will display an average indicator value.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_period.png\",\n      width: \"666\",\n      height: \"414\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_period.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, I will select the indicator I wish on the output. Say I am interested in forestry. I type \\\"forest\\\" in the filter. 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I have retrieved 47 rows of data.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-08-world-happiness/2022-08-31_indicators.png\",\n      width: \"994\",\n      height: \"367\",\n      src: \"/blog/2022-08-world-happiness/__optimized-images__/2022-08-31_indicators.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I can observe the data in the Data Table. Each row represents a country and each column an indicator. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Spectroscopy Workshop at BioSpec and How to Merge Data</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Spectroscopy Workshop at BioSpec and How to Merge Data"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-05-30 09:02:59+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="bioinformatics"/><meta property="article:tag" content="data"/><meta property="article:tag" content="education"/><meta property="article:tag" content="spectroscopy"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, bioinformatics, data, education, spectroscopy, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Spectroscopy Workshop at BioSpec and How to Merge Data</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">May 30, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Last week Marko and I visited the land of the midnight sun - Norway! We held a two-day workshop on spectroscopy data analysis in Orange at the <a href="https://www.nmbu.no/en">Norwegian University of Life Sciences</a>. The students from <a href="https://www.nmbu.no/en/faculty/realtek/research/groups/biospectroscopy">BioSpec</a> lab were yet again incredible and we really dug deep into Orange.</p>
 <p><strong>Related:</strong> <a href="/blog/2018/03/28/orange-with-spectroscopy-add-on-workshop/">Orange with Spectroscopy Add-on</a></p>
 <a href="/blog/2018-05-spectroscopy-workshop-at-biospec-and-how-to-merge-data/__optimized-images__/nbmu.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="701" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-05-spectroscopy-workshop-at-biospec-and-how-to-merge-data/__optimized-images__/nbmu.jpg"/></a>
@@ -170,4 +170,4 @@
 <p>Merged data with a new column.</p>
 <p>This is the final workflow. Merge Data now contains a single data table on the output and you can continue your analysis from there.</p>
 <a href="/blog/2018-05-spectroscopy-workshop-at-biospec-and-how-to-merge-data/__optimized-images__/Screen-Shot-2018-05-30-at-10.30.01.png" data-gallery="true"><img alt="" loading="lazy" width="910" height="674" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-05-spectroscopy-workshop-at-biospec-and-how-to-merge-data/__optimized-images__/Screen-Shot-2018-05-30-at-10.30.01.png"/></a>
-<p>Find out more about spectroscopy for Orange on our <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-bPWjDJvJBPZJ6us_KTAD5T">YouTube channel</a> or contribute to the project on <a href="https://github.com/Quasars/orange-spectroscopy">Github</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-30 09:02:59+00:00","draft":false,"title":"Spectroscopy Workshop at BioSpec and How to Merge Data","blog":["addons","bioinformatics","data","education","spectroscopy","workshop"],"oldUrl":"/blog/2018/05/30/spectroscopy-workshop-at-biospec-and-how-to-merge-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week Marko and I visited the land of the midnight sun - Norway! 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\ No newline at end of file
+<p>Find out more about spectroscopy for Orange on our <a href="https://www.youtube.com/playlist?list=PLmNPvQr9Tf-bPWjDJvJBPZJ6us_KTAD5T">YouTube channel</a> or contribute to the project on <a href="https://github.com/Quasars/orange-spectroscopy">Github</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-05-30 09:02:59+00:00","draft":false,"title":"Spectroscopy Workshop at BioSpec and How to Merge Data","blog":["addons","bioinformatics","data","education","spectroscopy","workshop"],"oldUrl":"/blog/2018/05/30/spectroscopy-workshop-at-biospec-and-how-to-merge-data/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Last week Marko and I visited the land of the midnight sun - Norway! 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Under the image column add \", _jsx(_components.em, {\n        children: \"string\"\n      }), \" in the second and \", _jsx(_components.em, {\n        children: \"type=image\"\n      }), \" in the third row. This will tell Orange where to look for images. Now, we can check our animals in Image Viewer.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-spectroscopy-workshop-at-biospec-and-how-to-merge-data/__optimized-images__/Screen-Shot-2018-05-30-at-10.25.42.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1005\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A quick glance at an Image Viewer will tell us whether our images got loaded correctly.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, it is time to bring in the images to the existing \", _jsx(_components.em, {\n        children: \"zoo\"\n      }), \" data set. Connect the original File to Merge Data. 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Merge Data now contains a single data table on the output and you can continue your analysis from there.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-05-spectroscopy-workshop-at-biospec-and-how-to-merge-data/__optimized-images__/Screen-Shot-2018-05-30-at-10.30.01.png\",\n      alt: \"\",\n      width: \"910\",\n      height: \"674\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Find out more about spectroscopy for Orange on our \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-bPWjDJvJBPZJ6us_KTAD5T\",\n        children: \"YouTube channel\"\n      }), \" or contribute to the project on \", _jsx(_components.a, {\n        href: \"https://github.com/Quasars/orange-spectroscopy\",\n        children: \"Github\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"spectroscopy-workshop-at-biospec-and-how-to-merge-data"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/blog/speeding-up-network-visualization/index.html b/blog/speeding-up-network-visualization/index.html
index 30810d197..998c61376 100644
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Speeding Up Network Visualization</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Speeding Up Network Visualization"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-12-23 17:05:52+00:00"/><meta property="article:author" content="THOCEVAR"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="network"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, network, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Speeding Up Network Visualization</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>THOCEVAR</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 23, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The Orange3 Network add-on contains a convenient Network Explorer widget for network visualization. Orange uses an iterative force-directed method (a variation of the Fruchterman-Reingold Algorithm) to layout the nodes on the 2D plane.</p>
 <a href="/blog/2017-12-speeding-up-network-visualization/__optimized-images__/network-schema.png" data-gallery="true"><img alt="" loading="lazy" width="261" height="116" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-12-speeding-up-network-visualization/__optimized-images__/network-schema.png"/></a>
 <p>The goal of force-directed methods is to draw connected nodes close to each other as if the edges that connect the nodes were acting as springs. We also don&#x27;t want all nodes crowded in a single point, but would rather have them spaced evenly. This is achieved by simulating a repulsive force, which decreases with the distance between nodes.</p>
@@ -162,4 +162,4 @@
 <p>Fortunately, we found a simple hack to speed things up. When computing the repulsive force acting on some node, we only consider a 10% sample of other nodes to obtain an estimate. We multiply the result by 10 and hope it&#x27;s not off by too much. By choosing a different sample in every iteration we also avoid favoring some set of nodes.</p>
 <p>The left layout is obtained without sampling while the right one uses a 10% sampling. The results are pretty similar, but the sampling method is 10 times faster!</p>
 <a href="/blog/2017-12-speeding-up-network-visualization/__optimized-images__/layout-compare.png" data-gallery="true"><img alt="" loading="lazy" width="1500" height="721" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-12-speeding-up-network-visualization/__optimized-images__/layout-compare.png"/></a>
-<p>Now that the computation is fast enough, it is time to also speed-up the drawing. But that&#x27;s a task for 2018.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"THOCEVAR","date":"2017-12-23 17:05:52+00:00","draft":false,"title":"Speeding Up Network Visualization","blog":["addons","network","visualization"],"oldUrl":"/blog/2017/12/23/speeding-up-network-visualization/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The Orange3 Network add-on contains a convenient Network Explorer widget for network visualization. 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This is achieved by simulating a repulsive force, which decreases with the distance between nodes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There are two types of forces acting on each node:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"the attractive force towards connected adjacent nodes,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"the repulsive force that is directed away from all other nodes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We could say that such network visualization as a whole is rather repulsive. Let's take for example the \", _jsx(_components.em, {\n        children: \"lastfm.net\"\n      }), \" network that comes with Orange's network add-on and which has around 1.000 nodes and 4.000 edges. 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+<p>Now that the computation is fast enough, it is time to also speed-up the drawing. But that&#x27;s a task for 2018.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"THOCEVAR","date":"2017-12-23 17:05:52+00:00","draft":false,"title":"Speeding Up Network Visualization","blog":["addons","network","visualization"],"oldUrl":"/blog/2017/12/23/speeding-up-network-visualization/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"The Orange3 Network add-on contains a convenient Network Explorer widget for network visualization. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - SQL for Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - SQL for Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2015-10-19 12:36:04+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="data"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="sql"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 <p>The key to SQL files is installation of &#x27;<em>psycopg2</em>&#x27; library in Python.</p>
 <p><strong>WINDOWS</strong></p>
@@ -157,4 +157,4 @@
 <p><strong>MAC OS X, LINUX</strong></p>
 <p>If you’re on Mac or Linux, install <em>psycopg2</em> with <a href="http://initd.org/psycopg/docs/install.html">this</a>.</p>
 <a href="/blog/2015-10-sql-for-orange/__optimized-images__/SQLTable.jpg" data-gallery="true"><img alt="" loading="lazy" width="83" height="94" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-10-sql-for-orange/__optimized-images__/SQLTable.jpg"/></a>
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+<p>Upon opening Orange, you will be able to see a lovely new icon - <strong>SQL Table</strong>. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, examples, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Stack Everything!</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 05, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>We all know that sometimes many is better than few. Therefore we are happy to introduce the Stack widget. It is available in <a href="https://github.com/biolab/orange3-prototypes">Prototypes add-on</a> for now.</p>
 <p><a href="https://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html">Stacking</a> enables you to combine several trained models into one meta model and use it in Test&amp;Score just like any other model. This comes in handy with complex problems, where one classifier might fail, but many could come up with something that works. Let&#x27;s see an example.</p>
 <p>We start with something as complex as this. We used Paint Data to create a complex data set, where classes somewhat overlap. This is naturally an artificial example, but you can try the same on your own, real life data.</p>
@@ -163,4 +163,4 @@
 <p>Scores with stacking. Stack reports on improved performance.</p>
 <p>And indeed they have. It might not be anything dramatic, but in real life, say medical context, even small improvements count. Now go and try the procedure on your own data. In Orange, this requires only a couple of minutes.</p>
 <a href="/blog/2018-01-stack-everything/__optimized-images__/Screen-Shot-2018-01-05-at-16.31.01.png" data-gallery="true"><img alt="" loading="lazy" width="1074" height="994" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-01-stack-everything/__optimized-images__/Screen-Shot-2018-01-05-at-16.31.01.png"/></a>
-<p>Final workflow with channel names. Notice that Logistic Regression is used as Aggregate, not a Learner.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-01-05 15:37:34+00:00","draft":false,"title":"Stack Everything!","blog":["classification","examples","widget"],"oldUrl":"/blog/2018/01/05/stack-everything/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We all know that sometimes many is better than few. Therefore we are happy to introduce the Stack widget. It is available in \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-prototypes\",\n        children: \"Prototypes add-on\"\n      }), \" for now.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html\",\n        children: \"Stacking\"\n      }), \" enables you to combine several trained models into one meta model and use it in Test\u0026Score just like any other model. This comes in handy with complex problems, where one classifier might fail, but many could come up with something that works. Let's see an example.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We start with something as complex as this. We used Paint Data to create a complex data set, where classes somewhat overlap. This is naturally an artificial example, but you can try the same on your own, real life data.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-01-stack-everything/__optimized-images__/Screen-Shot-2018-01-05-at-16.19.58.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1271\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We used 4 classes and painted a complex, 2-dimensional data set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Then we add several kNN models with different parameters, say 5, 10 and 15 neighbors. We connect them to Test\u0026Score and use cross validation to evaluate their performance. Not bad, but can we do even better?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-01-stack-everything/__optimized-images__/Screen-Shot-2018-01-05-at-16.23.08.png\",\n      alt: \"\",\n      width: \"1314\",\n      height: \"870\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scores without staking, using only 3 different kNN classifiers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Let us try stacking. We will connect all three classifiers to the Stacking widget and use Logistic Regression as an aggregate, a method that aggregates the three models into a single meta model. 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+<p>Final workflow with channel names. Notice that Logistic Regression is used as Aggregate, not a Learner.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-01-05 15:37:34+00:00","draft":false,"title":"Stack Everything!","blog":["classification","examples","widget"],"oldUrl":"/blog/2018/01/05/stack-everything/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We all know that sometimes many is better than few. Therefore we are happy to introduce the Stack widget. It is available in \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-prototypes\",\n        children: \"Prototypes add-on\"\n      }), \" for now.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html\",\n        children: \"Stacking\"\n      }), \" enables you to combine several trained models into one meta model and use it in Test\u0026Score just like any other model. This comes in handy with complex problems, where one classifier might fail, but many could come up with something that works. Let's see an example.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We start with something as complex as this. We used Paint Data to create a complex data set, where classes somewhat overlap. 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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">Student application period for GSoC 2011 has ended</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 08, 2011</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Student application period for <a href="http://socghop.appspot.com/gsoc/homepage/google/gsoc2011">Google Summer of Code 2011</a> has ended. We got 47 proposals from students all around the world. Now it is time for us to evaluate them and select the best proposals and the best students to work this year on Orange.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-04-08 07:53:00+00:00","draft":false,"title":"Student application period for GSoC 2011 has ended","blog":["gsoc"],"oldUrl":"/blog/2011/04/08/student-application-period-for-gsoc-2011-has-ended/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"Student application period for \", _jsx(_components.a, {\n      href: \"http://socghop.appspot.com/gsoc/homepage/google/gsoc2011\",\n      children: \"Google Summer of Code 2011\"\n    }), \" has ended. 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We got 47 proposals from students all around the world. Now it is time for us to evaluate them and select the best proposals and the best students to work this year on Orange.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2011-04-08 07:53:00+00:00","draft":false,"title":"Student application period for GSoC 2011 has ended","blog":["gsoc"],"oldUrl":"/blog/2011/04/08/student-application-period-for-gsoc-2011-has-ended/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"Student application period for \", _jsx(_components.a, {\n      href: \"http://socghop.appspot.com/gsoc/homepage/google/gsoc2011\",\n      children: \"Google Summer of Code 2011\"\n    }), \" has ended. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, orange3, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Support vectors output in SVM widget</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 03, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Did you know that the widget for <strong>support vector machines</strong> (SVM) classifier can output support vectors? And that you can visualise these in any other Orange widget? In the context of all other data sets, this could provide some extra insight into how this popular classification algorithm works and what it actually does.</p>
 <p>Ideally, that is, in the case of linear seperability, support vector machines (SVM) find a **hyperplane <strong>with the</strong> largest margin **to any data instance. This margin touches a small number of data instances that are called support vectors.</p>
 <p>In Orange 3.0 you can set the SVM classification widget to output also the support vectors and visualize them. We used <em>Iris</em> data set in the <em>File</em> widget and classified data instances with SVM classifier. Then we connected both widgets with <em>Scatterplot</em> and selected <em>Support Vectors</em> in the SVM output channel. This allows us to see support vectors in the <em>Scatterplot</em> widget - they are represented by the bold dots in the graph.</p>
 <p>Now feel free to try it with your own data set!</p>
 <a href="/blog/2015-07-support-vectors-output-in-svm-widget/__optimized-images__/svm-with-support-vectors.png" data-gallery="true"><img alt="" loading="lazy" width="800" height="640" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-support-vectors-output-in-svm-widget/__optimized-images__/svm-with-support-vectors.png"/></a>
-<p>Support vectors output of SVM widget with Iris data set.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-03 06:46:50+00:00","draft":false,"title":"Support vectors output in SVM widget","blog":["classification","orange3","visualization"],"oldUrl":"/blog/2015/07/03/support-vectors-output-in-svm-widget/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Did you know that the widget for \", _jsx(_components.strong, {\n        children: \"support vector machines\"\n      }), \" (SVM) classifier can output support vectors? 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\ No newline at end of file
+<p>Support vectors output of SVM widget with Iris data set.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-03 06:46:50+00:00","draft":false,"title":"Support vectors output in SVM widget","blog":["classification","orange3","visualization"],"oldUrl":"/blog/2015/07/03/support-vectors-output-in-svm-widget/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Did you know that the widget for \", _jsx(_components.strong, {\n        children: \"support vector machines\"\n      }), \" (SVM) classifier can output support vectors? 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diff --git a/blog/text-analysis-new-features/index.html b/blog/text-analysis-new-features/index.html
index 5d5453130..63bc16bf5 100644
--- a/blog/text-analysis-new-features/index.html
+++ b/blog/text-analysis-new-features/index.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Text Analysis: New Features</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Text Analysis: New Features"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-08-04 11:41:33+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="dataloading"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="features"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="release"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="version"/><meta property="article:tag" content="widget"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="26"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-b68feeb067c0683d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/EvXyAVV-yObR_-FUjGdn6/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Text Analysis: New Features</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Text Analysis: New Features"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-08-04 11:41:33+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="dataloading"/><meta property="article:tag" content="examples"/><meta property="article:tag" content="features"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="release"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="version"/><meta property="article:tag" content="widget"/><meta property="article:tag" content="workshop"/><meta name="next-head-count" content="26"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -179,4 +179,4 @@
 <a href="/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-12.23.24.png" data-gallery="true"><img alt="" loading="lazy" width="1556" height="388" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-12.23.24.png"/></a>
 <p>If there are many kings in the text and no birds, then the text belongs to Topic 2. If there are many children and no foxes, then it belongs to Topic 3.</p>
 <p>Take some time, explore these improvements and let us know if you are happy with the changes! You can also submit new feature requests to our <a href="https://github.com/biolab/orange3-text/issues">issue tracker</a>.</p>
-<p>Thank you for working with Orange! 🍊</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-08-04 11:41:33+00:00","draft":false,"title":"Text Analysis: New Features","blog":["analysis","dataloading","examples","features","orange3","release","text mining","version","widget","workshop"],"oldUrl":"/blog/2017/08/04/text-analysis-new-features/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"As always, we've been working hard to bring you new functionalities and improvements. Recently, we've released Orange version 3.4.5 and Orange3-Text version 0.2.5. We focused on the Text add-on since we are lately holding a lot of text mining workshops. The next one will be at Digital Humanities 2017 in Montreal, QC, Canada in a couple of days and we simply could not resist introducing some sexy new features_._\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2017/06/19/text-preprocessing/\",\n        children: \"Text Preprocessing\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/07/05/rehaul-of-text-mining-add-on/\",\n        children: \"Rehaul of Text Mining Add-On\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, Orange 3.4.5 offers better support for Text add-on. What do we mean by this? Now, every core Orange widget works with Text smoothly so you can mix-and-match the widgets as you like. Before, one could not pass the output of Select Columns (data table) to Preprocess Text (corpus), but now this is no longer a problem.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-13.33.28.png\",\n      alt: \"\",\n      width: \"1068\",\n      height: \"196\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course, one still needs to keep in mind that Corpus is a sparse data format, which does not work with some widgets by design. For example, Manifold Learning supports only t-SNE projection.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-10.37.03.png\",\n      alt: \"\",\n      width: \"1010\",\n      height: \"184\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Second, we've introduced two new widgets, which have been long overdue. One is Sentiment Analysis, which enables basic sentiment analysis of corpora. So far it works for English and uses two nltk-supported techniques - \", _jsx(_components.a, {\n        href: \"https://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf\",\n        children: \"Liu Hu\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf\",\n        children: \"Vader\"\n      }), \". Both techniques are lexicon-based. Liu Hu computes a single normalized score of sentiment in the text (negative score for negative sentiment, positive for positive, 0 is neutral), while Vader outputs scores for each category (positive, negative, neutral) and appends a total sentiment score called a compound.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.00.25.png\",\n      alt: \"\",\n      width: \"1282\",\n      height: \"888\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Liu Hu score.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-10.59.57.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"796\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Vader scores.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Try it with Heat Map to visualize the scores.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.05.23.png\",\n      alt: \"\",\n      width: \"638\",\n      height: \"190\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.05.19.png\",\n      alt: \"\",\n      width: \"1276\",\n      height: \"1344\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Yellow represent a high, positive score, while blue represent a low, negative score. Seems like Animal Tales are generally much more negative than Tales of Magic.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The second widget we've introduced is Import Documents. This widget enables you to import your own documents into Orange and outputs a corpus on which you can perform the analysis. The widget supports .txt, .docx, .odt, .pdf and .xml files and loads an entire folder. If the folder contains subfolders, they will be considered as class values. Here's an example.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.11.17.png\",\n      alt: \"\",\n      width: \"1592\",\n      height: \"338\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is the structure of my Kennedy folder. I will load the folder with Import Documents. Observe, how Orange creates a class variable \", _jsx(_components.em, {\n        children: \"category\"\n      }), \" with \", _jsx(_components.em, {\n        children: \"post-1962\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"pre-1962\"\n      }), \" as class values.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.15.01.png\",\n      alt: \"\",\n      width: \"790\",\n      height: \"316\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.15.14.png\",\n      alt: \"\",\n      width: \"1342\",\n      height: \"888\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Subfolders are considered as class in the category column.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now you can perform your analysis as usual.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.15.44.png\",\n      alt: \"\",\n      width: \"662\",\n      height: \"360\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, some widgets have cool new updates. Topic Modelling, for example, colors words by their weights - positive weights are colored green and negative red. Coloring only works with LSI, since it's the only method that outputs both positive and negative weights.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.31.51.png\",\n      alt: \"\",\n      width: \"576\",\n      height: \"196\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-12.23.24.png\",\n      alt: \"\",\n      width: \"1556\",\n      height: \"388\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If there are many kings in the text and no birds, then the text belongs to Topic 2. If there are many children and no foxes, then it belongs to Topic 3.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Take some time, explore these improvements and let us know if you are happy with the changes! 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+<p>Thank you for working with Orange! 🍊</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-08-04 11:41:33+00:00","draft":false,"title":"Text Analysis: New Features","blog":["analysis","dataloading","examples","features","orange3","release","text mining","version","widget","workshop"],"oldUrl":"/blog/2017/08/04/text-analysis-new-features/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"As always, we've been working hard to bring you new functionalities and improvements. Recently, we've released Orange version 3.4.5 and Orange3-Text version 0.2.5. We focused on the Text add-on since we are lately holding a lot of text mining workshops. The next one will be at Digital Humanities 2017 in Montreal, QC, Canada in a couple of days and we simply could not resist introducing some sexy new features_._\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2017/06/19/text-preprocessing/\",\n        children: \"Text Preprocessing\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/07/05/rehaul-of-text-mining-add-on/\",\n        children: \"Rehaul of Text Mining Add-On\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, Orange 3.4.5 offers better support for Text add-on. What do we mean by this? Now, every core Orange widget works with Text smoothly so you can mix-and-match the widgets as you like. Before, one could not pass the output of Select Columns (data table) to Preprocess Text (corpus), but now this is no longer a problem.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-13.33.28.png\",\n      alt: \"\",\n      width: \"1068\",\n      height: \"196\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course, one still needs to keep in mind that Corpus is a sparse data format, which does not work with some widgets by design. For example, Manifold Learning supports only t-SNE projection.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-10.37.03.png\",\n      alt: \"\",\n      width: \"1010\",\n      height: \"184\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Second, we've introduced two new widgets, which have been long overdue. One is Sentiment Analysis, which enables basic sentiment analysis of corpora. So far it works for English and uses two nltk-supported techniques - \", _jsx(_components.a, {\n        href: \"https://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf\",\n        children: \"Liu Hu\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf\",\n        children: \"Vader\"\n      }), \". Both techniques are lexicon-based. Liu Hu computes a single normalized score of sentiment in the text (negative score for negative sentiment, positive for positive, 0 is neutral), while Vader outputs scores for each category (positive, negative, neutral) and appends a total sentiment score called a compound.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.00.25.png\",\n      alt: \"\",\n      width: \"1282\",\n      height: \"888\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Liu Hu score.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-10.59.57.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"796\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Vader scores.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Try it with Heat Map to visualize the scores.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.05.23.png\",\n      alt: \"\",\n      width: \"638\",\n      height: \"190\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.05.19.png\",\n      alt: \"\",\n      width: \"1276\",\n      height: \"1344\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Yellow represent a high, positive score, while blue represent a low, negative score. Seems like Animal Tales are generally much more negative than Tales of Magic.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The second widget we've introduced is Import Documents. This widget enables you to import your own documents into Orange and outputs a corpus on which you can perform the analysis. The widget supports .txt, .docx, .odt, .pdf and .xml files and loads an entire folder. If the folder contains subfolders, they will be considered as class values. Here's an example.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.11.17.png\",\n      alt: \"\",\n      width: \"1592\",\n      height: \"338\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is the structure of my Kennedy folder. I will load the folder with Import Documents. Observe, how Orange creates a class variable \", _jsx(_components.em, {\n        children: \"category\"\n      }), \" with \", _jsx(_components.em, {\n        children: \"post-1962\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"pre-1962\"\n      }), \" as class values.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.15.01.png\",\n      alt: \"\",\n      width: \"790\",\n      height: \"316\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.15.14.png\",\n      alt: \"\",\n      width: \"1342\",\n      height: \"888\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Subfolders are considered as class in the category column.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now you can perform your analysis as usual.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.15.44.png\",\n      alt: \"\",\n      width: \"662\",\n      height: \"360\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, some widgets have cool new updates. Topic Modelling, for example, colors words by their weights - positive weights are colored green and negative red. Coloring only works with LSI, since it's the only method that outputs both positive and negative weights.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-11.31.51.png\",\n      alt: \"\",\n      width: \"576\",\n      height: \"196\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-08-text-analysis-new-features/__optimized-images__/Screen-Shot-2017-08-04-at-12.23.24.png\",\n      alt: \"\",\n      width: \"1556\",\n      height: \"388\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If there are many kings in the text and no birds, then the text belongs to Topic 2. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, conference, education, interactive data visualization, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Text Analysis Workshop at Digital Humanities 2017</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 08, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>How do you explain text mining in 3 hours? Is it even possible? Can someone be ready to build predictive models and perform clustering in a single afternoon?</p>
 <p>It seems so, especially when Orange is involved.</p>
 <p>Yesterday, on August 7, we held a 3-hour workshop on text mining and text analysis for a large crowd of esteemed researchers at <a href="https://dh2017.adho.org/">Digital Humanities 2017</a> in Montreal, Canada. Surely, after 3 hours everyone was exhausted, both the audience and the lecturers. But at the same time, everyone was also excited. The audience about the possibilities Orange offers for their future projects and the lecturers about the fantastic participants who even during the workshop were already experimenting with their own data.</p>
@@ -162,4 +162,4 @@
 <p>At the end, we were experimenting with explorative data analysis, where we had Hierarchical Clustering, Corpus Viewer, Image Viewer and Geo Map opened at the same time. This is how a researcher can interactively explore the dendrogram, read the documents from selected clusters, observe the corresponding images and locate them on a map.</p>
 <p><a href="/blog/2017-08-text-analysis-workshop-at-digital-humanities-2017/__optimized-images__/blog1-1.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="918" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-08-text-analysis-workshop-at-digital-humanities-2017/__optimized-images__/blog1-1.png"/></a>
 Hierarchical Clustering, Image Viewer, Geo Map and Corpus Viewer opened at the same time create an interactive data browser.</p>
-<p>The workshop was a nice kick-off to an exciting week full of interesting lectures and presentations at Digital Humanities 2017 conference. So much to learn and see!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-08-08 14:56:43+00:00","draft":false,"title":"Text Analysis Workshop at Digital Humanities 2017","blog":["classification","conference","education","interactive data visualization","workshop"],"oldUrl":"/blog/2017/08/08/text-analysis-workshop-at-digital-humanities-2017/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"How do you explain text mining in 3 hours? 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+<p>The workshop was a nice kick-off to an exciting week full of interesting lectures and presentations at Digital Humanities 2017 conference. So much to learn and see!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-08-08 14:56:43+00:00","draft":false,"title":"Text Analysis Workshop at Digital Humanities 2017","blog":["classification","conference","education","interactive data visualization","workshop"],"oldUrl":"/blog/2017/08/08/text-analysis-workshop-at-digital-humanities-2017/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"How do you explain text mining in 3 hours? 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, clustering, examples, orange3, preprocessing, release, text mining, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Text Mining: version 0.2.0</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 23, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="https://github.com/biolab/orange3-text">Orange3-Text</a> has just recently been polished, updated and enhanced! Our GSoC student Alexey has <a href="/blog/2016/07/05/rehaul-of-text-mining-add-on/">helped us greatly</a> to achieve another milestone in Orange development and release the latest 0.2.0 version of our text mining add-on. The new release, which is already available on PyPi, includes Wikipedia and SimHash widgets and a rehaul of Bag of Words, Topic Modeling and Corpus Viewer.</p>
 <p>Wikipedia widget allows retrieving sources from Wikipedia API and can handle multiple queries. It serves as an easy data gathering source and it&#x27;s great for exploring text mining techniques. Here we&#x27;ve simply queried Wikipedia for articles on Slovenia and Germany and displayed them in Corpus Viewer.</p>
 <a href="/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/wiki1-1.png" data-gallery="true"><img alt="" loading="lazy" width="1341" height="726" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/wiki1-1.png"/></a>
@@ -166,4 +166,4 @@
 <p>Now we connect Corpus Viewer to Preprocess Text. This is nothing new, but Corpus Viewer now displays also tokens and POS tags. Enable POS Tagger in Preprocess Text. Now open Corpus Viewer and tick the checkbox Show Tokens &amp; Tags. This will display tagged token at the bottom of each document.</p>
 <a href="/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/CorpusViewer.png" data-gallery="true"><img alt="" loading="lazy" width="869" height="542" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/CorpusViewer.png"/></a>
 <p>Corpus Viewer can now display tokens and POS tags below each document.</p>
-<p>This is just a brief overview of what one can do with the new Orange text mining functionalities. Of course, these are just exemplary workflows. If you did textual analysis with great results using any of these widgets, feel free to share it with us! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-09-23 14:50:38+00:00","draft":false,"title":"Text Mining: version 0.2.0","blog":["addons","clustering","examples","orange3","preprocessing","release","text mining","widget"],"oldUrl":"/blog/2016/09/23/text-mining-version-0-2-0/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-text\",\n        children: \"Orange3-Text\"\n      }), \" has just recently been polished, updated and enhanced! Our GSoC student Alexey has \", _jsx(_components.a, {\n        href: \"/blog/2016/07/05/rehaul-of-text-mining-add-on/\",\n        children: \"helped us greatly\"\n      }), \" to achieve another milestone in Orange development and release the latest 0.2.0 version of our text mining add-on. The new release, which is already available on PyPi, includes Wikipedia and SimHash widgets and a rehaul of Bag of Words, Topic Modeling and Corpus Viewer.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Wikipedia widget allows retrieving sources from Wikipedia API and can handle multiple queries. It serves as an easy data gathering source and it's great for exploring text mining techniques. Here we've simply queried Wikipedia for articles on Slovenia and Germany and displayed them in Corpus Viewer.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/wiki1-1.png\",\n      alt: \"\",\n      width: \"1341\",\n      height: \"726\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Query Wikipedia by entering your query word list in the widget. Put each query on a separate line and run Search.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://matpalm.com/resemblance/simhash/\",\n        children: \"Similarity Hashing\"\n      }), \" widget computes similarity hashes for the given corpus, allowing the user to find duplicates, plagiarism or textual borrowing in the corpus. Here's an example from Wikipedia, which has a pre-defined structure of articles, making our corpus quite similar. We've used Wikipedia widget and retrieved 10 articles for the query 'Slovenia'. Then we've used Similarity Hashing to compute hashes for our text. What we got on the output is a table of 64 binary features (predefined in the SimHash widget), which denote a 64-bit hash size. Then we computed similarities in text by sending Similarity Hashing to Distances. Here we've selected cosine row distances and sent the output to Hierarchical Clustering. We can see that we have some similar documents, so we can select and inspect them in Corpus Viewer.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/SimHash1.png\",\n      alt: \"\",\n      width: \"956\",\n      height: \"406\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output of Similarity Hashing widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/SimHash.png\",\n      alt: \"\",\n      width: \"1616\",\n      height: \"912\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We've selected the two most similar documents in Hierarchical Clustering and displayed them in Corpus Viewer.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Topic Modeling now includes three modeling algorithms, namely \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Latent_semantic_analysis\",\n        children: \"Latent Semantic Indexing (LSP)\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation\",\n        children: \"Latent Dirichlet Allocation (LDA)\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Hierarchical_Dirichlet_process\",\n        children: \"Hierarchical Dirichlet Process (HDP)\"\n      }), \". Let's query Twitter for the latest tweets from Hillary Clinton and Donald Trump. First we preprocess the data and send the output to Topic Modeling. The widget suggests 10 topics, with the most significant words denoting each topic, and outputs topic probabilities for each document.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can inspect distances between the topics with Distances (cosine) and Hierarchical Clustering. Seems like topics are not extremely author specific, since Hierarchical Clustering often puts Trump and Clinton in the same cluster. We've used Average linkage, but you can play around with different linkages and see if you can get better results.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/Topic-Modelling.png\",\n      alt: \"\",\n      width: \"1609\",\n      height: \"906\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Example of comparing text by topics.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now we connect Corpus Viewer to Preprocess Text. This is nothing new, but Corpus Viewer now displays also tokens and POS tags. Enable POS Tagger in Preprocess Text. Now open Corpus Viewer and tick the checkbox Show Tokens \u0026 Tags. This will display tagged token at the bottom of each document.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-09-text-mining-version-0-2-0/__optimized-images__/CorpusViewer.png\",\n      alt: \"\",\n      width: \"869\",\n      height: \"542\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Corpus Viewer can now display tokens and POS tags below each document.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is just a brief overview of what one can do with the new Orange text mining functionalities. Of course, these are just exemplary workflows. 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+<p>This is just a brief overview of what one can do with the new Orange text mining functionalities. Of course, these are just exemplary workflows. If you did textual analysis with great results using any of these widgets, feel free to share it with us! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-09-23 14:50:38+00:00","draft":false,"title":"Text Mining: version 0.2.0","blog":["addons","clustering","examples","orange3","preprocessing","release","text mining","widget"],"oldUrl":"/blog/2016/09/23/text-mining-version-0-2-0/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-text\",\n        children: \"Orange3-Text\"\n      }), \" has just recently been polished, updated and enhanced! Our GSoC student Alexey has \", _jsx(_components.a, {\n        href: \"/blog/2016/07/05/rehaul-of-text-mining-add-on/\",\n        children: \"helped us greatly\"\n      }), \" to achieve another milestone in Orange development and release the latest 0.2.0 version of our text mining add-on. The new release, which is already available on PyPi, includes Wikipedia and SimHash widgets and a rehaul of Bag of Words, Topic Modeling and Corpus Viewer.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Wikipedia widget allows retrieving sources from Wikipedia API and can handle multiple queries. It serves as an easy data gathering source and it's great for exploring text mining techniques. 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We've used Wikipedia widget and retrieved 10 articles for the query 'Slovenia'. Then we've used Similarity Hashing to compute hashes for our text. What we got on the output is a table of 64 binary features (predefined in the SimHash widget), which denote a 64-bit hash size. Then we computed similarities in text by sending Similarity Hashing to Distances. Here we've selected cosine row distances and sent the output to Hierarchical Clustering. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Text Preprocessing</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Text Preprocessing"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2017-06-19 08:04:03+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="preprocessing"/><meta property="article:tag" content="text mining"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, preprocessing, text mining, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Text Preprocessing</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 19, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In data mining, preprocessing is key. And in text mining, it is the key and the door. In other words, it&#x27;s the most vital step in the analysis.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/07/05/rehaul-of-text-mining-add-on/">Text Mining add-on</a></p>
 <p>So what does preprocessing do? Let&#x27;s have a look at an example. Place Corpus widget from Text add-on on the canvas. Open it and load <em>Grimm-tales-selected</em>. As always, first have a quick glance of the data in Corpus Viewer. This data set contains 44 selected <a href="https://en.wikipedia.org/wiki/Grimms%27_Fairy_Tales">Grimms&#x27; tales</a>.</p>
@@ -167,4 +167,4 @@
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 <p>One final check in the Word Cloud should reveal we did a nice job preparing our data. We can now see the tales talk about kings, mothers, fathers, foxes and something that is little. Much more informative!</p>
 <a href="/blog/2017-06-text-preprocessing/__optimized-images__/Screen-Shot-2017-06-19-at-09.51.40.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="1164" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-06-text-preprocessing/__optimized-images__/Screen-Shot-2017-06-19-at-09.51.40.png"/></a>
-<p><strong>Related:</strong> <a href="/blog/2017/06/09/workshop-text-analysis-for-social-scientists/">Workshop: Text Analysis for Social Scientists</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-06-19 08:04:03+00:00","draft":false,"title":"Text Preprocessing","blog":["orange3","preprocessing","text mining","visualization"],"oldUrl":"/blog/2017/06/19/text-preprocessing/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In data mining, preprocessing is key. And in text mining, it is the key and the door. In other words, it's the most vital step in the analysis.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/07/05/rehaul-of-text-mining-add-on/\",\n        children: \"Text Mining add-on\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"So what does preprocessing do? Let's have a look at an example. Place Corpus widget from Text add-on on the canvas. Open it and load \", _jsx(_components.em, {\n        children: \"Grimm-tales-selected\"\n      }), \". As always, first have a quick glance of the data in Corpus Viewer. This data set contains 44 selected \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Grimms%27_Fairy_Tales\",\n        children: \"Grimms' tales\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-06-text-preprocessing/__optimized-images__/Screen-Shot-2017-06-19-at-09.45.48.png\",\n      alt: \"\",\n      width: \"1168\",\n      height: \"810\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, let us see the most frequent words of this corpus in a Word Cloud.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-06-text-preprocessing/__optimized-images__/Screen-Shot-2017-06-19-at-09.46.29.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1161\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Ugh, what a mess! The most frequent words in these texts are conjunctions ('and', 'or') and prepositions ('in', 'of'), but so they are in almost every English text in the world. 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+<p><strong>Related:</strong> <a href="/blog/2017/06/09/workshop-text-analysis-for-social-scientists/">Workshop: Text Analysis for Social Scientists</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-06-19 08:04:03+00:00","draft":false,"title":"Text Preprocessing","blog":["orange3","preprocessing","text mining","visualization"],"oldUrl":"/blog/2017/06/19/text-preprocessing/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"In data mining, preprocessing is key. And in text mining, it is the key and the door. In other words, it's the most vital step in the analysis.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/07/05/rehaul-of-text-mining-add-on/\",\n        children: \"Text Mining add-on\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"So what does preprocessing do? Let's have a look at an example. Place Corpus widget from Text add-on on the canvas. Open it and load \", _jsx(_components.em, {\n        children: \"Grimm-tales-selected\"\n      }), \". As always, first have a quick glance of the data in Corpus Viewer. This data set contains 44 selected \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Grimms%27_Fairy_Tales\",\n        children: \"Grimms' tales\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-06-text-preprocessing/__optimized-images__/Screen-Shot-2017-06-19-at-09.45.48.png\",\n      alt: \"\",\n      width: \"1168\",\n      height: \"810\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Now, let us see the most frequent words of this corpus in a Word Cloud.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-06-text-preprocessing/__optimized-images__/Screen-Shot-2017-06-19-at-09.46.29.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1161\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Ugh, what a mess! The most frequent words in these texts are conjunctions ('and', 'or') and prepositions ('in', 'of'), but so they are in almost every English text in the world. We need to remove these frequent and uninteresting words to get to the interesting part. We remove the punctuation by defining our tokens. \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Regular_expression\",\n        children: \"Regexp\"\n      }), \" \", _jsx(_components.em, {\n        children: \"\\\\w+\"\n      }), \" will keep full words and omit everything else. Next, we filter out the uninteresting words with a list of stopwords. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, text mining, update, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Text Workshops in Ljubljana</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 11, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In the past month, we had two workshops that focused on text mining. The first one, <a href="https://www.kc.uni-lj.si/novice/poletna-ola-faksi-v-praksi.html">Faksi v praksi</a>, was organized by the <a href="https://kc.uni-lj.si/domov.html">University of Ljubljana Career Centers</a>, where high school students learned about what we do at the <a href="https://fri.uni-lj.si/en">Faculty of Computer and Information Science</a>. We taught them what text mining is and how to group a collection of documents in Orange. The second one took on a more serious note, as the public sector employees joined us for the third set of workshops from the Ministry of Public Affairs. This time, we did not only cluster documents, but also built predictive models, explored predictions in nomogram, plotted documents on a map and discovered how to find the emotion in a tweet.</p>
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 <p>These workshops gave us a lot of incentive to improve the Text add-on. We really wanted to support more languages and add extra functionalities to widgets. In the upcoming week, we will release the 0.5.0 version, which introduces support for Slovenian in Sentiment Analysis widget, adds concordance output option to Concordances and, most importantly, implements <a href="http://ufal.mff.cuni.cz/udpipe/users-manual#universal_dependencies_20_models">UDPipe lemmatization</a>, which means Orange will now support about 50 languages! Well, at least for normalization. 😇</p>
@@ -166,4 +166,4 @@
 <a href="/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.19.59.png" data-gallery="true"><img alt="" loading="lazy" width="1444" height="1192" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.19.59.png"/></a>
 <p>Of course, there are some drawbacks of lexicon-based methods. Namely, they don&#x27;t work well with phrases, they often don&#x27;t consider modern language (see &#x27;Jupiiiiiii&#x27; or &#x27;Hooooooraaaaay!&#x27;, where the more the letters, the more expressive the word is) and they fail with sarcasm. Nevertheless, even such crude methods give us a nice glimpse into the corpus and enable us to extract interesting documents.</p>
 <a href="/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.17.45.png" data-gallery="true"><img alt="" loading="lazy" width="1158" height="380" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.17.45.png"/></a>
-<p>Stay tuned for the information on the release date and the upcoming post on UDPipe infrastructure!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-09-11 13:49:28+00:00","draft":false,"title":"Text Workshops in Ljubljana","blog":["addons","text mining","update","workshop"],"oldUrl":"/blog/2018/09/11/text-workshops-in-ljubljana/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the past month, we had two workshops that focused on text mining. 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+<p>Stay tuned for the information on the release date and the upcoming post on UDPipe infrastructure!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-09-11 13:49:28+00:00","draft":false,"title":"Text Workshops in Ljubljana","blog":["addons","text mining","update","workshop"],"oldUrl":"/blog/2018/09/11/text-workshops-in-ljubljana/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In the past month, we had two workshops that focused on text mining. 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We have added the KKS 1.001 \", _jsx(_components.a, {\n        href: \"https://www.clarin.si/repository/xmlui/handle/11356/1115#\",\n        children: \"opinion corpus of Slovene web commentaries\"\n      }), \", which is a part of the CLARIN infrastructure. You can access it in the Corpus widget. Go to Browse documentation corpora and look for \", _jsx(_components.em, {\n        children: \"slo-opinion-corpus.tab\"\n      }), \". Let's have a quick view in a Corpus Viewer.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.01.32.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1058\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The data comes from comment sections of Slovenian online media and contains a fairly expressive language. Let us observe, whether a post is negative or positive. 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Remove all attributes other than \", _jsx(_components.em, {\n        children: \"sentiment\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.01.43.png\",\n      alt: \"\",\n      width: \"994\",\n      height: \"952\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, we can observe the results in a Heat Map. The blue lines are the negative posts, while the yellow ones are positive. Let us select the most positive tweets and see, what they are about.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.18.48.png\",\n      alt: \"\",\n      width: \"1106\",\n      height: \"1330\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Looks like Slovenians are happy, when petrol gets cheaper and sports(wo)men are winning. We can relate.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-09-text-workshops-in-ljubljana/__optimized-images__/Screen-Shot-2018-09-11-at-15.19.59.png\",\n      alt: \"\",\n      width: \"1444\",\n      height: \"1192\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Of course, there are some drawbacks of lexicon-based methods. Namely, they don't work well with phrases, they often don't consider modern language (see 'Jupiiiiiii' or 'Hooooooraaaaay!', where the more the letters, the more expressive the word is) and they fail with sarcasm. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - The Beauty of Random Forest</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - The Beauty of Random Forest"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-12-22 08:55:36+00:00"/><meta property="article:author" content="BLAZ"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="interactive data visualization"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="regression"/><meta property="article:tag" content="tree"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>classification, interactive data visualization, orange3, regression, tree, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">The Beauty of Random Forest</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BLAZ</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 22, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It is the time of the year when we adore Christmas trees. But these are not the only trees we, at Orange team, think about. In fact, through almost life-long professional deformation of being a data scientist, when I think about trees I would often think about classification and regression trees. And they can be beautiful as well. Not only for their elegance in explaining the hidden patterns, but aesthetically, when rendered in Orange. And even more beautiful then a single tree is Orange&#x27;s rendering of a forest, that is, a random forest.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/07/29/pythagorean-trees-and-forests/">Pythagorean Trees and Forests</a></p>
 <p>Here are six trees in the random forest constructed on the <a href="https://archive.ics.uci.edu/ml/datasets/Housing">housing data set</a>:
@@ -158,4 +158,4 @@
 <a href="/blog/2016-12-the-beauty-of-random-forest/__optimized-images__/random-forest-anneal2.png" data-gallery="true"><img alt="" loading="lazy" width="882" height="331" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-12-the-beauty-of-random-forest/__optimized-images__/random-forest-anneal2.png"/></a></p>
 <p>A Christmas-lit random forest inferred from <a href="http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits">pen digits data set</a> looks somehow messy in trying to categorize to ten different classes:
 <a href="/blog/2016-12-the-beauty-of-random-forest/__optimized-images__/pythagorean-forest-pendigits.png" data-gallery="true"><img alt="" loading="lazy" width="887" height="887" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-12-the-beauty-of-random-forest/__optimized-images__/pythagorean-forest-pendigits.png"/></a></p>
-<p>The power of beauty! No wonder random forests are one of the best machine learning tools. Orange renders them according to the idea of Fabian Beck and colleagues who proposed <a href="http://publications.fbeck.com/ivapp14-pythagoras.pdf">Pythagoras trees for visualizations of hierarchies</a>. The actual implementation for classification and regression trees for Orange was created by <a href="https://github.com/pavlin-policar">Pavlin Policar.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-12-22 08:55:36+00:00","draft":false,"title":"The Beauty of Random Forest","blog":["classification","interactive data visualization","orange3","regression","tree","visualization"],"oldUrl":"/blog/2016/12/22/the-beauty-of-random-forest/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It is the time of the year when we adore Christmas trees. 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+<p>The power of beauty! No wonder random forests are one of the best machine learning tools. Orange renders them according to the idea of Fabian Beck and colleagues who proposed <a href="http://publications.fbeck.com/ivapp14-pythagoras.pdf">Pythagoras trees for visualizations of hierarchies</a>. The actual implementation for classification and regression trees for Orange was created by <a href="https://github.com/pavlin-policar">Pavlin Policar.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BLAZ","date":"2016-12-22 08:55:36+00:00","draft":false,"title":"The Beauty of Random Forest","blog":["classification","interactive data visualization","orange3","regression","tree","visualization"],"oldUrl":"/blog/2016/12/22/the-beauty-of-random-forest/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It is the time of the year when we adore Christmas trees. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>release, Status Bar</strong></p><h1 class="sc-c67a4900-0 loxVcf">The Changing Status Bar</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Blaz Zupan</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 08, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Every week on Friday, when the core team of Orange developers meets, we are designing new improvements of Orange&#x27;s graphical interface. This time, it was the status bar. Well, actually, it was the status bar quite a while ago and required the change of the core widget library, but it is materializing these days and you will see the changes in the next release.</p>
 <p>Consider the Neighbors widget. The widget considers the input data and reference data items, and outputs instance form input data that are most similar to the references. Like, if the dolphin is a reference, we would like to know which are the three most similar animals. But this is not what want I wanted to write about. I would only like to say that we are making a slight change in the user interface. Below is the Neighbors widget in the current release of Orange, and the upcoming one.</p>
 <p>\</p>
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 <p><br/>
 <!-- -->\</p>
-<p>See the difference? We are getting rid of the infobox on the top of the control tab, and moving it to the status bar. In the infobox widgets typically display what is in their input and what is on the output after the data has been processed. Moving this information to the status bar will make widgets more compact and less cluttered. We will similarly change the infoboxes in this way in all of the widgets.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaz Zupan","date":"2019-03-08","draft":false,"title":"The Changing Status Bar","thumbImage":"changing-blog.png","image":"","blog":["release","Status Bar"],"shortExcerpt":"We are constantly optimizing Orange's look-and-feel. 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+<p>See the difference? We are getting rid of the infobox on the top of the control tab, and moving it to the status bar. In the infobox widgets typically display what is in their input and what is on the output after the data has been processed. Moving this information to the status bar will make widgets more compact and less cluttered. We will similarly change the infoboxes in this way in all of the widgets.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaz Zupan","date":"2019-03-08","draft":false,"title":"The Changing Status Bar","thumbImage":"changing-blog.png","image":"","blog":["release","Status Bar"],"shortExcerpt":"We are constantly optimizing Orange's look-and-feel. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - The easy way to install add-ons</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - The easy way to install add-ons"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2012-11-30 08:00:00+00:00"/><meta property="article:author" content="BIOLAB"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="orange25"/><meta property="article:tag" content="pypi"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, orange25, pypi</strong></p><h1 class="sc-c67a4900-0 loxVcf">The easy way to install add-ons</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 30, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The possibility of extending functionality of Orange through <a href="http://orange.biolab.si/addons/">add-ons</a> has been present for a long time. In fact, we never provided the <a href="http://orange-bioinformatics.readthedocs.org/en/latest/">toolbox for crunching bioinformatical data</a> as an integral part of Orange; it has always been an add-on. The exact mechanism of distribution of add-ons has changed significantly in the last year to simplify the process for add-on authors and to make it more standards-compliant. Among other things, this enables system administrators to install add-ons system-wide directly from <a href="http://pypi.python.org/pypi?%3Aaction=search&amp;term=orange&amp;submit=search">PyPi</a> using easy_install or pip. Unfortunately there were also negative side effects of this process, notably the temporary breakage of the add-on management dialog within the Orange Canvas.</p>
 <p>We are happy to report that this is now being taken care of and you are encouraged to test the functionality.</p>
 <a href="/blog/2012-11-the-easy-way-to-install-add-ons/__optimized-images__/orange-addons-dialog.png__1000x1000_q95.png" data-gallery="true"><img alt="" loading="lazy" width="603" height="613" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2012-11-the-easy-way-to-install-add-ons/__optimized-images__/orange-addons-dialog.png__1000x1000_q95.png"/></a>
 <p>Select &quot;Add-ons...&quot; in the Options menu. A dialog will open that will list and describe existing add-ons. You can see the same list on the <a href="http://orange.biolab.si/addons/">appropriate part of Orange website</a>, but there is more. In the dialog, you can simply pick the add-ons you wish to use, confirm the selection and you should be good to go: widgets that come with the selected add-ons will become available immediately.</p>
 <p>In case you change your mind, on some systems you can also uninstall add-ons by removing the check marks in front of them. This only works if you have <a href="http://pypi.python.org/pypi/pip">pip</a> installed, which is uncommon on Windows systems.</p>
 <p>This might be a good time to warn you that the described functionality is new and not thoroughly tested on all the platforms on which Orange runs. If you stumble upon any strange or unwanted behavior, please let us now on the <a href="http://orange.biolab.si/forum/">Orange forum</a>, preferrably in the <em>Bugs</em> section.</p>
-<p>Note that the <strong>Orange-Text</strong> add-on requires a compiler and appropriate libraries on your computer, and it as of now still refuses to be installed using the dialog. 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+<p>Note that the <strong>Orange-Text</strong> add-on requires a compiler and appropriate libraries on your computer, and it as of now still refuses to be installed using the dialog. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>cross validation, leave one out, LOO, test and score</strong></p><h1 class="sc-c67a4900-0 loxVcf">The Mystery of Test &amp; Score</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 28, 2019</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Test &amp; Score is surely one the most used widgets in Orange. Fun fact: it is the fourth in popularity, right after Data Table, File and Scatter Plot. So let us dive into the nuts and bolts of the Test &amp; Score widget.</p>
 <p>The widget generally accepts two inputs – Data and Learner. Data is the data set that we will be using for modeling, say, iris.tab that is already pre-loaded in the File widget. Learner is any kind of learning algorithm, for example, Logistic Regression. You can only use those learners that support your type of task. If you wish to do classification, you cannot use Linear Regression and for regression you cannot use Logistic Regression. Most other learners support both tasks. You can connect more than one learner to Test &amp; Score.</p>
 <p>\</p>
@@ -165,4 +165,4 @@
 <p>\</p>
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We explain each of them in a few lines.","longExcerpt":"Test \u0026 Score widget is used for evaluating model performance, but what do the methods do? We explain cross validation, random sampling, leave one out and cross validation by feature in a few lines.","oldUrl":"/blog/2019/1/28/the-mystery-of-test-and-score/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    br: \"br\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Test \u0026 Score is surely one the most used widgets in Orange. 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+<!-- -->\</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2019-01-28","draft":false,"title":"The Mystery of Test \u0026 Score","thumbImage":"ts-blog.png","image":"","blog":["cross validation","leave one out","LOO","test and score"],"shortExcerpt":"Test \u0026 Score widget is used for evaluating model performance, but what do the methods do? 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diff --git a/blog/the-story-of-shadow-and-orange/index.html b/blog/the-story-of-shadow-and-orange/index.html
index f64b88aae..5443ce959 100644
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - The Story of Shadow and Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - The Story of Shadow and Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-10-10 10:09:56+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="canvas"/><meta property="article:tag" content="interface"/><meta property="article:tag" content="oasys"/><meta name="next-head-count" content="19"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>canvas, interface, oasys</strong></p><h1 class="sc-c67a4900-0 loxVcf">The Story of Shadow and Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Oct 10, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This is a long story. I remember when started my PhD in Italy. There I met a researcher and he said to me: »You should do some simulations on x-ray optics beamline.« »Yes, but how should I do that?« He gave me a big tape, it was 1986. I soon realized it was all code. But it was a code called <a href="http://scripts.iucr.org/cgi-bin/paper?S0909049511026306">Shadow</a>.</p>
 <p>I started to look at the code, to play with it, do some simulations… Soon my boss told me:</p>
 <p>»You should do a simulation with asymmetric crystals for monochromators.«</p>
@@ -164,4 +164,4 @@
 <p>And at that moment, she said many things were on this big old Mac. So I proposed to buy this Mac from her, but my home institution wasn&#x27;t happy, they saw no reason to buy a second-hand Mac. Even though it contained some important things Cerrina was working on!</p>
 <p>Luckily, I managed to get it and I was able to recover many things from it. Moreover, I kept maintaining the Shadow code, because it is a standard software in the community. At the very beginning, the source was not public. Then it was eventually published, but the code was very complicated and nobody managed to recompile that. Thus I decided to clean the code and finally we completed the new version of Shadow in 2011.</p>
 <p>Three years ago it was time to update Shadow again, especially the interface. One day I discovered Orange and I thought &#x27;it looked nice&#x27;. In that exact time I met Luca [Rebuffi] in Trieste. He got so excited about Orange that his PhD project became redesigning <a href="https://github.com/lucarebuffi/ShadowOui">Shadow&#x27;s</a> interface with Orange! And now we have <a href="https://www.elettra.trieste.it/oasys.html">OASYS</a>, which is an adaptation of Orange for optical physics. So I hope that in the future, we will have many more users and also many more developers helping us bring simple tools to the scientific community.</p>
-<p>-- Manuel Sanchez del Rio</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-10-10 10:09:56+00:00","draft":false,"title":"The Story of Shadow and Orange","blog":["canvas","interface","oasys"],"oldUrl":"/blog/2016/10/10/the-story-of-shadow-and-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This is a long story. 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+<p>-- Manuel Sanchez del Rio</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-10-10 10:09:56+00:00","draft":false,"title":"The Story of Shadow and Orange","blog":["canvas","interface","oasys"],"oldUrl":"/blog/2016/10/10/the-story-of-shadow-and-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This is a long story. I remember when started my PhD in Italy. There I met a researcher and he said to me: »You should do some simulations on x-ray optics beamline.« »Yes, but how should I do that?« He gave me a big tape, it was 1986. I soon realized it was all code. But it was a code called \", _jsx(_components.a, {\n        href: \"http://scripts.iucr.org/cgi-bin/paper?S0909049511026306\",\n        children: \"Shadow\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I started to look at the code, to play with it, do some simulations… Soon my boss told me:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"»You should do a simulation with asymmetric crystals for monochromators.«\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"»But asymmetric crystals are not foreseen in this code.«\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"»Yes, think about how to do it. You should contact Franco Cerrina, he's the author of Shadow.«\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"I indeed contacted prof. Cerrina and at that time this was not easy, because there was no direct e-mail. What we had was called a digital deck net, Digital Computers Network. I had to go to another laboratory just to send him an e-mail. Soon, he replied: »Come to see me.« I managed to get some funding to go to the US and for the next two years I spent a good amount of time in Madison, Wisconsin.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"I started to work with prof. Cerrina and it was thanks to my work on Shadow that I was called by the \", _jsx(_components.a, {\n        href: \"http://www.esrf.eu/\",\n        children: \"European Synchrotron Facility\"\n      }), \" and they offered me a position. But soon I stopped working on Shadow, because I was getting busy with other things.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"It was only in 2009 that I contacted prof. Cerrina again. We needed to upgrade our software, so I went back to the US two or three times and started working on what is now \", _jsx(_components.a, {\n        href: \"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267628/\",\n        children: \"Shadow3\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In 2010 I organized a trip to go visit again with my family for the summer. We booked the house, we booked the trip… And it was ten days before the departure that I learned that Cerrina died. And since everything was already organized, we decided to visit the US anyway.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There, I went to Cerrina's laboratory and met his PhD student, who was keeping his possessions. I said to her:\"\n    }), \"\\n\", _jsx(\"blockquote\", {\n      children: \"»Tell me everything you were doing recently and I will try to recover what I can.«\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"And at that moment, she said many things were on this big old Mac. So I proposed to buy this Mac from her, but my home institution wasn't happy, they saw no reason to buy a second-hand Mac. Even though it contained some important things Cerrina was working on!\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Luckily, I managed to get it and I was able to recover many things from it. Moreover, I kept maintaining the Shadow code, because it is a standard software in the community. At the very beginning, the source was not public. Then it was eventually published, but the code was very complicated and nobody managed to recompile that. Thus I decided to clean the code and finally we completed the new version of Shadow in 2011.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Three years ago it was time to update Shadow again, especially the interface. One day I discovered Orange and I thought 'it looked nice'. In that exact time I met Luca [Rebuffi] in Trieste. He got so excited about Orange that his PhD project became redesigning \", _jsx(_components.a, {\n        href: \"https://github.com/lucarebuffi/ShadowOui\",\n        children: \"Shadow's\"\n      }), \" interface with Orange! And now we have \", _jsx(_components.a, {\n        href: \"https://www.elettra.trieste.it/oasys.html\",\n        children: \"OASYS\"\n      }), \", which is an adaptation of Orange for optical physics. So I hope that in the future, we will have many more users and also many more developers helping us bring simple tools to the scientific community.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"-- Manuel Sanchez del Rio\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"the-story-of-shadow-and-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>gsoc</strong></p><h1 class="sc-c67a4900-0 loxVcf">This year five students participate in Google Summer of Code</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 24, 2012</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>This year five students have been accepted to participate in <a href="https://www.google-melange.com/gsoc/homepage/google/gsoc2012">Google Summer of Code</a> and contribute to Orange in their summer time. Congratulations!</p>
 <ul>
 <li><a href="https://www.google-melange.com/gsoc/project/google/gsoc2012/amela/26002">Amela</a> – Widgets for statistics</li>
@@ -158,4 +158,4 @@
 <li><a href="https://www.google-melange.com/gsoc/project/google/gsoc2012/makarov/27005">Makarov Dmitry</a> – Text mining add-on for Orange</li>
 <li><a href="https://www.google-melange.com/gsoc/project/google/gsoc2012/mlevar/5001">Miran Levar</a> – Multi-Target Learning for Orange</li>
 </ul>
-<p>Overall, 1,212 <a href="http://google-opensource.blogspot.com/2012/04/students-announced-for-google-summer-of.html">students have been accepted</a> this year to <a href="https://www.google-melange.com/gsoc/accepted_orgs/google/gsoc2012">various open source organizations</a> from all around the world.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-04-24 10:50:00+00:00","draft":false,"title":"This year five students participate in Google Summer of Code","blog":["gsoc"],"oldUrl":"/blog/2012/04/24/this-year-five-students-participate-in-google-summer-of-code/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This year five students have been accepted to participate in \", _jsx(_components.a, {\n        href: \"https://www.google-melange.com/gsoc/homepage/google/gsoc2012\",\n        children: \"Google Summer of Code\"\n      }), \" and contribute to Orange in their summer time. 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+<p>Overall, 1,212 <a href="http://google-opensource.blogspot.com/2012/04/students-announced-for-google-summer-of.html">students have been accepted</a> this year to <a href="https://www.google-melange.com/gsoc/accepted_orgs/google/gsoc2012">various open source organizations</a> from all around the world.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2012-04-24 10:50:00+00:00","draft":false,"title":"This year five students participate in Google Summer of Code","blog":["gsoc"],"oldUrl":"/blog/2012/04/24/this-year-five-students-participate-in-google-summer-of-code/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"This year five students have been accepted to participate in \", _jsx(_components.a, {\n        href: \"https://www.google-melange.com/gsoc/homepage/google/gsoc2012\",\n        children: \"Google Summer of Code\"\n      }), \" and contribute to Orange in their summer time. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>timeseries, line chart, spiralogram, prediction, VAR model</strong></p><h1 class="sc-c67a4900-0 loxVcf">Timeseries add-on lost a lot of weight</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar Žagar</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 04, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>The Timeseries add-on has been on a backlog for quite some time now. Mostly because a bulk of its visualizations have been written in <a href="https://www.highcharts.com/">Highcharts</a>, a visualization library built on JavaScript. We&#x27;re Python developers, so none of us felt comfortable touching anything JS. Moreover, Highcharts is not fully open source, so licensing is a potential issue for certain Orange users.</p>
 <p>There was only one person who could (and would) tackle this - our senior developer Vesna. She is a whiz with creating beautiful visualization widgets (see <a href="https://orangedatamining.com/widget-catalog/visualize/barplot/">Bar Plot</a> and <a href="https://orangedatamining.com/widget-catalog/visualize/lineplot/">Line Plot</a>!) and in a few weeks she managed to rewrite Line Chart to PyQt. The path towards this major milestone was paved by Janez, who rewrote Correlogram, Spiralogram, and Periodogram. The changes resolved a large proportion of issues in Timeseries, making the add-on now much more reliable. The changes are available in the Timeseries version 0.5.0.</p>
 <p>Here&#x27;s a simple example of timeseries analysis in Orange. I&#x27;ve used Kaggle&#x27;s House Property Sales Time Series <a href="https://www.kaggle.com/datasets/htagholdings/property-sales">data</a>, specifically the <em>ma_lga_12345.csv</em> file containing 347 records of real estate sales, each presented with the date of the sale (in quarter of the year), the median price (smoothed with moving average), property type (unit or house) and the number of bedrooms (1-5).</p>
@@ -160,4 +160,4 @@
 <p>In spiralogram, we can inspect the same variable, this time split into years, separated into units and houses, and displaying a mean value of MA for each slice. Evidently, houses are much more expensive than units. Now change <em>type</em> to <em>bedrooms</em>. When does large real estate (5+ bedrooms) become noticeably more expensive?</p>
 <a href="/blog/2022-08-timeseries/__optimized-images__/2022-08-04_spiralogram.png" data-gallery="true"><img alt="" loading="lazy" width="1037" height="560" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-08-timeseries/__optimized-images__/2022-08-04_spiralogram.png"/></a>
 <p>Let us conclude this with a short example in predictive modelling. Say we wish to predict the median price based on the number of bedrooms and the type of real estate. We have to first set MA as the target variable with Select Columns. Then, we pass the data to VAR model, an autoregressive model for predicting timeseries. Finally, we use another Line Chart, to which we pass both the original timeseries and the forecast from the VAR model. In the right-most section of the plot, we see the prediction (the dashed line) and the confidence intervals (the blue area). While the model predicts the price to stay at around 600k, the confidence intervals are very wide, making this an uncertain prediction.</p>
-<a href="/blog/2022-08-timeseries/__optimized-images__/2022-08-04_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="3010" height="1670" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-08-timeseries/__optimized-images__/2022-08-04_workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-08-04","draft":false,"title":"Timeseries add-on lost a lot of weight","thumbImage":"2022-08-04_spiralo.png","frontPageImage":"2022-08-04_spiralo.png","blog":["timeseries","line chart","spiralogram","prediction","VAR model"],"shortExcerpt":"We've updated most visualizations in the Timeseries add-on","longExcerpt":"Timeseries' visualizations are becoming fully PyQt, making them easier to maintain. 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+<a href="/blog/2022-08-timeseries/__optimized-images__/2022-08-04_workflow.png" data-gallery="true"><img alt="" loading="lazy" width="3010" height="1670" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-08-timeseries/__optimized-images__/2022-08-04_workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar Žagar","date":"2022-08-04","draft":false,"title":"Timeseries add-on lost a lot of weight","thumbImage":"2022-08-04_spiralo.png","frontPageImage":"2022-08-04_spiralo.png","blog":["timeseries","line chart","spiralogram","prediction","VAR model"],"shortExcerpt":"We've updated most visualizations in the Timeseries add-on","longExcerpt":"Timeseries' visualizations are becoming fully PyQt, making them easier to maintain. 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diff --git a/blog/tips-and-tricks-for-data-preparation/index.html b/blog/tips-and-tricks-for-data-preparation/index.html
index 0a8dfbce3..d8a01b884 100644
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+++ b/blog/tips-and-tricks-for-data-preparation/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Tips and Tricks for Data Preparation</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Tips and Tricks for Data Preparation"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-01-29 14:59:18+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="data"/><meta property="article:tag" content="dataloading"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>data, dataloading</strong></p><h1 class="sc-c67a4900-0 loxVcf">Tips and Tricks for Data Preparation</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 29, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Probably the most crucial step in your data analysis is purging and cleaning your data. Here are a couple of cool tricks that will make your data preparation a bit easier.</p>
 <ul>
 <li>
@@ -172,4 +172,4 @@
 Set domain with Edit domain widget.</p>
 </li>
 </ul>
-<p>What&#x27;s your preferred trick?</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-01-29 14:59:18+00:00","draft":false,"title":"Tips and Tricks for Data Preparation","blog":["data","dataloading"],"oldUrl":"/blog/2016/01/29/tips-and-tricks-for-data-preparation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Probably the most crucial step in your data analysis is purging and cleaning your data. 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You can also use regex to easily find and replace parts of text.\"]\n        }), \"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.img, {\n            src: \"/blog/2016-01-tips-and-tricks-for-data-preparation/__optimized-images__/blog-editing-data2.png\",\n            alt: \"\",\n            width: \"656\",\n            height: \"539\"\n          }), \"\\nWe can replace all instances of 'male' with 'man' in one click.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"Apply simple hacks.\"\n          }), \" Sometimes when converting files to different formats data can get some background information appended that you cannot properly see. A cheap and dirty trick is to manually select the cells and rows and copy-paste them to a new sheet. 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In a Text Import Wizard you can set whether your data is delimited or not (in our case it is), how it is delimited (comma, tab, etc.), whether you have a header or not, what qualifies as text (\\\" is a recommended option), what is your encoding and so on.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"Manually annotate the data.\"\n          }), \" Orange loves headers and the easiest way to assure your data gets read properly is to set the header yourself. Add two extra rows under your feature names. 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+<p>What&#x27;s your preferred trick?</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-01-29 14:59:18+00:00","draft":false,"title":"Tips and Tricks for Data Preparation","blog":["data","dataloading"],"oldUrl":"/blog/2016/01/29/tips-and-tricks-for-data-preparation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Probably the most crucial step in your data analysis is purging and cleaning your data. 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You can also use regex to easily find and replace parts of text.\"]\n        }), \"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.img, {\n            src: \"/blog/2016-01-tips-and-tricks-for-data-preparation/__optimized-images__/blog-editing-data2.png\",\n            alt: \"\",\n            width: \"656\",\n            height: \"539\"\n          }), \"\\nWe can replace all instances of 'male' with 'man' in one click.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"Apply simple hacks.\"\n          }), \" Sometimes when converting files to different formats data can get some background information appended that you cannot properly see. A cheap and dirty trick is to manually select the cells and rows and copy-paste them to a new sheet. This will start with a clean slate and you data will be read properly.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.strong, {\n            children: \"Check your settings.\"\n          }), \" When reading .csv files in Excel, you might see all your data squished in one column and literally separated with commas. This can be easily solved with Data --\u003e From Text (Get external data) and a new window will appear. 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@@ -153,4 +153,4 @@
 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>article, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Top 100 Changemakers in Central and Eastern Europe</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Nov 18, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Recently Orange and one of its inventors, <a href="http://www.ne100.org/challenger/blaz-zupan,5828e5617525ba2e7e52446d">Blaž Zupan</a>, have been recognized as one of the top 100 changemakers in the region. A <a href="http://www.ne100.org/">2016 New Europe 100</a> is an annual list of innovators and entrepreneurs in Central and Eastern Europe highlighting novel approaches to pressing problems.</p>
 <p><a href="http://orange.biolab.si">Orange</a> has been recognized for <em>making data more approachable</em>, which has been our goal from the get-go. The tool is continually being developed with the end user in mind - someone who wants to analyze his/her data quickly, visually, interactively, and efficiently. We&#x27;re always thinking hard how to expose valuable information in the data, how to improve the user experience, which defaults are the most appropriate for the method, and, finally, how to intuitively teach people about data mining.</p>
 <p>This nomination is a great validation of our efforts and it only makes us work harder. Because every research should be fruitful and fun!</p>
-<a href="/blog/2016-11-top-100-changemakers-in-central-and-eastern-europe/__optimized-images__/adv_data_mining-02-1.png" data-gallery="true"><img alt="" loading="lazy" width="1971" height="1733" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-11-top-100-changemakers-in-central-and-eastern-europe/__optimized-images__/adv_data_mining-02-1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-11-18 13:28:09+00:00","draft":false,"title":"Top 100 Changemakers in Central and Eastern Europe","blog":["article","orange3"],"oldUrl":"/blog/2016/11/18/top-100-changemakers-in-central-and-eastern-europe/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Recently Orange and one of its inventors, \", _jsx(_components.a, {\n        href: \"http://www.ne100.org/challenger/blaz-zupan,5828e5617525ba2e7e52446d\",\n        children: \"Blaž Zupan\"\n      }), \", have been recognized as one of the top 100 changemakers in the region. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Training with Orange: A Tutorial at AIME 2022 in Halifax</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Training with Orange: A Tutorial at AIME 2022 in Halifax"/><meta property="og:description" content="A half-a day tutorial on data science training with Orange on June 14 in Halifax, Canada."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2022-04-26"/><meta property="article:author" content="Blaž Zupan"/><meta property="article:tag" content="orange"/><meta property="article:tag" content="workshop"/><meta property="og:image" content="/blog/2022-04-amia-tutorial/__optimized-images__/2022-04-26-wordcloud.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="18"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -206,4 +206,4 @@ <h1>Previous Similar Tutorials</h1>
 <p>Related to the type of training we propose to showcase in the tutorial are also short educational videos on data science in Orange available on <a href="http://youtube.com/orangedatamining">YouTube</a>.</p>
 <h2>Presenter</h2>
 <p><a href="https://en.wikipedia.org/wiki/Bla%C5%BE_Zupan">Blaž Zupan</a>, University of Ljubljana, Slovenia, and Baylor College of Medicine, Houston, USA</p>
-<p>Prof. Dr. Blaž Zupan teaches artificial intelligence and machine learning at the University of Ljubljana and Baylor College of Medicine in Houston. His research has focused on explainable AI and combinations of machine learning and data visualization techniques. He runs a twenty-member bioinformatics laboratory, developing <a href="http://orangedatamining.com">Orange</a>, a comprehensive open-source toolbox for machine learning. Orange is a great data science training tool, and Zupan’s group has designed and carried out over a hundred hands-on workshops and courses in the past five years. Zupan <a href="https://scholar.google.com/scholar?hl=en&amp;as_sdt=0%2C5&amp;q=blaz+zupan&amp;btnG=">has published</a> his work in more than a hundred articles that have received over ten thousand citations.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-04-26","draft":false,"title":"Training with Orange: A Tutorial at AIME 2022 in Halifax","thumbImage":"2022-04-26-wordcloud.png","frontPageImage":"2022-04-26-wordcloud.png","blog":["orange","workshop"],"shortExcerpt":"A half-a day tutorial on data science training with Orange on June 14 in Halifax, Canada.","longExcerpt":"A conference on Artificial Intelligence in Medicine is hosting our tutorial on training of crucial mechanics behind data science and machine learning with Orange.","x2images":false,"oldUrl":"/blog/2022/2022-04-26-amia-tutorial/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    h1: \"h1\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"When\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"June 14, 2022\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Where\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Halifax, Canada. This is a half-day, on site tutorial organized during the \", _jsx(_components.a, {\n        href: \"https://aime22.aimedicine.info/\",\n        children: \"AIME 2022\"\n      }), \" conference.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"On Tutorial\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the tutorial, we will claim and demonstrate that, given the right tool, it may take only a few hours to familiarize outsiders in data science.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The tutorial is intended for teachers and trainees: we will present both the training steps and, as a demonstration, go through the actual training. After the tutorial, it should be evident that after a short training of this kind, the professionals can gain enough intuition about data science to recognize opportunities that this field can offer and actively engage in data science projects. Besides good mentors and an encouraging working environment, the right tool is critical for such training.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will advocate the workflow-based construction of analytical pipelines with interactive visualizations and show that they can be the key to the simplicity of the interface and flexibility to adopt analytics to any data type and problem domain. We will illustrate the use of such a tool on practical cases and explain clustering, model development, data visualization, and analysis of data with gene expressions.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The tutorial will be hands-on, and the attendees will be encouraged to bring their laptops and follow me through practical cases.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-04-amia-tutorial/2022-04-26-sc-markers-tsne.png\",\n      width: \"1755\",\n      height: \"1508\",\n      src: \"/blog/2022-04-amia-tutorial/__optimized-images__/2022-04-26-sc-markers-tsne.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The tutorial will use Orange, our software tool that features workflows, interactive visualizations, and blends visualizations and machine learning. The figure shows the workflow to analyze the landscape of single cells using their expression data and support highlighting of selected gene markers and gene marker discovery. We plan to run this use case and construct a similar workflow during the proposed tutorial.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Covered Topics\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For the prospective or actual teachers and professors of machine learning and data science, we will show how to:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"organize a short introductory or advanced course in data science that can take only a few hours but cover a substantial number of topics from machine learning and data visualization,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"train intuitive concepts of data science, rather than dive into details of mathematics and statistics,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"use the workflows-based environment to soften the learning curve and train data science without invoking any computer programming,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"use interactive visualizations and visual analytics to engage in data exploration and additionally motivate the trainees and make training lessons exciting and engaging,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"design a practical training course that immediately dives into the data and helps trainees understand data science on practical use cases,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"design a course that uses case-based studies and avoids using any powerpoints.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Everyone interested in learning about data science or those who would like to enjoy a fast-paced walk through the machine learning landscape can join the tutorial. The tutorial will cover the topics of\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"clustering, including hierarchical clustering, k-means, visualization of clustering results and explanation of clusters,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"dimensionality reduction approaches and design of data maps with methods like MDS and t-SNE,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"predictive modeling using essential supervised machine learning techniques (logistic regression, random forests, neural networks) and several tools that can explain the structure of the models, like nomograms,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"overfitting, how to cheat with it and how to avoid it,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"evaluation of prediction models, including cross-validation and assessment of accuracy.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The listed topics conceptually cover a substantial part of the machine learning landscape. The workshop aims to demonstrate that using the right tools and pedagogical approaches, these can be covered, in an intuitive way, in only a few hours.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"During the tutorial, we will showcase the data science and machine learning methods on several datasets that will include, among others, data with socioeconomic and health indices of countries, gene expression data from single-cell experiments, and images from molecular biology.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Motivation behind this Tutorial\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Machine learning methods are drivers of change in sciences and engineering. The computational approaches that can sip through vast collections of data, extract exciting patterns, and devise predictive models are becoming omnipresent. But only a few professionals understand the basics of data science, and even fewer engage in building models using their data. We need to devise means to train them and familiarize them with core data science concepts without burdening them with mathematical and computer science details. The tools used in such training could also come handing when presenting and communicating the results of the data analysis, where methods from visual analytics can help us actively engage the data owners. Especially in medicine, it may be essential to train medical professionals and students so that they can gain enough intuition about data science to recognize opportunities that this field can offer and actively engage in data science projects.\"\n    }), \"\\n\", _jsx(_components.h1, {\n      children: \"Intended Audience\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Current or prospective teachers or trainers of data science,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"AIME attendees interested in tools for explorative data analysis, integration of machine learning, and data visualization,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data scientists that would like to familiarize themselves with a workflow-based, visual analytics tool that can support active communication of results of data science to the data owners,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Anyone curious about how we can pack the essentials of data science within a half a day workshop.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h1, {\n      children: \"Get Engaged\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the tutorial, we will showcase the use of the tool, \", _jsx(_components.a, {\n        href: \"http://orangedatamining.com\",\n        children: \"Orange\"\n      }), \", for data science training through a set of case studies, and with these introduce the data science topics and methods. We encourage the audience to follow the tutorial and work on their laptops. We will also encourage discussion and active participation through lecturer interactions with the audience through questions on the topic and on the chosen case studies.\"]\n    }), \"\\n\", _jsx(_components.h1, {\n      children: \"Previous Similar Tutorials\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the past five years, our group has perfected the hands-in training approach by carrying out close to a hundred hands-on machine learning and data science workshops around the world, including those in Houston, Ås, Pavia, Luxemburg, Kolkata, Liverpool, Bari, Oslo, Ashburn, Konstanz, Belgrade, Helsinki, Melbourne, Sao Paolo, Verona, and Ljubljana. The workshops and tutorials ranged anyway from an hour to a few days. They were carried out for Ph.D. students, researchers, company professionals, employees of the public sector, and teachers and students in high school. Please refer to our \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/workshop/\",\n        children: \"blogs\"\n      }), \" for short reports on some of our workshops.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have also recently published several manuscripts about the proposed training, including:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Demšar J, Zupan B (2021) \", _jsx(_components.a, {\n          href: \"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008671\",\n          children: \"Hands-on training about overfitting\"\n        }), \", PLOS Computational Biology 17(3): e1008671.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Godec P, Pančur M, Ilenič N, Čopar A, Stražar M, Erjavec A, Pretnar A, Demšar J, Starič A, Toplak M, Žagar L, Hartman J, Wang H, Bellazzi R, Petrovič U, Garagna S, Zuccotti M, Park D, Shaulsky G, Zupan B (2019)\\n\", _jsx(_components.a, {\n          href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n          children: \"Democratized image analytics by visual programming through integration of deep models and small-scale machine learning\"\n        }), \", Nature Communications 10(1):4551.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Stražar M, Žagar L, Kokošar J, Tanko V, Erjavec A, Poličar P, Starič A, Demšar J, Shaulsky G, Menon V, Lamire A, Parikh A, and Zupan B (2019) \", _jsx(_components.a, {\n          href: \"https://academic.oup.com/bioinformatics/article/35/14/i4/5529249\",\n          children: \"scOrange – A Tool for Hands-On Training of Concepts from Single Cell Data Analytics\"\n        }), \", Bioinformatics 35(14):i4-i12.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related to the type of training we propose to showcase in the tutorial are also short educational videos on data science in Orange available on \", _jsx(_components.a, {\n        href: \"http://youtube.com/orangedatamining\",\n        children: \"YouTube\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Presenter\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Bla%C5%BE_Zupan\",\n        children: \"Blaž Zupan\"\n      }), \", University of Ljubljana, Slovenia, and Baylor College of Medicine, Houston, USA\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Prof. Dr. Blaž Zupan teaches artificial intelligence and machine learning at the University of Ljubljana and Baylor College of Medicine in Houston. 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Zupan \", _jsx(_components.a, {\n        href: \"https://scholar.google.com/scholar?hl=en\u0026as_sdt=0%2C5\u0026q=blaz+zupan\u0026btnG=\",\n        children: \"has published\"\n      }), \" his work in more than a hundred articles that have received over ten thousand citations.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-04-amia-tutorial/__optimized-images__/2022-04-26-wordcloud.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"training-with-orange-a-tutorial-at-aime-2022-in-halifax"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>Prof. Dr. Blaž Zupan teaches artificial intelligence and machine learning at the University of Ljubljana and Baylor College of Medicine in Houston. His research has focused on explainable AI and combinations of machine learning and data visualization techniques. He runs a twenty-member bioinformatics laboratory, developing <a href="http://orangedatamining.com">Orange</a>, a comprehensive open-source toolbox for machine learning. Orange is a great data science training tool, and Zupan’s group has designed and carried out over a hundred hands-on workshops and courses in the past five years. Zupan <a href="https://scholar.google.com/scholar?hl=en&amp;as_sdt=0%2C5&amp;q=blaz+zupan&amp;btnG=">has published</a> his work in more than a hundred articles that have received over ten thousand citations.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-04-26","draft":false,"title":"Training with Orange: A Tutorial at AIME 2022 in Halifax","thumbImage":"2022-04-26-wordcloud.png","frontPageImage":"2022-04-26-wordcloud.png","blog":["orange","workshop"],"shortExcerpt":"A half-a day tutorial on data science training with Orange on June 14 in Halifax, Canada.","longExcerpt":"A conference on Artificial Intelligence in Medicine is hosting our tutorial on training of crucial mechanics behind data science and machine learning with Orange.","x2images":false,"oldUrl":"/blog/2022/2022-04-26-amia-tutorial/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    h1: \"h1\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"When\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"June 14, 2022\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Where\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Halifax, Canada. This is a half-day, on site tutorial organized during the \", _jsx(_components.a, {\n        href: \"https://aime22.aimedicine.info/\",\n        children: \"AIME 2022\"\n      }), \" conference.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"On Tutorial\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the tutorial, we will claim and demonstrate that, given the right tool, it may take only a few hours to familiarize outsiders in data science.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The tutorial is intended for teachers and trainees: we will present both the training steps and, as a demonstration, go through the actual training. After the tutorial, it should be evident that after a short training of this kind, the professionals can gain enough intuition about data science to recognize opportunities that this field can offer and actively engage in data science projects. Besides good mentors and an encouraging working environment, the right tool is critical for such training.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We will advocate the workflow-based construction of analytical pipelines with interactive visualizations and show that they can be the key to the simplicity of the interface and flexibility to adopt analytics to any data type and problem domain. We will illustrate the use of such a tool on practical cases and explain clustering, model development, data visualization, and analysis of data with gene expressions.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The tutorial will be hands-on, and the attendees will be encouraged to bring their laptops and follow me through practical cases.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2022-04-amia-tutorial/2022-04-26-sc-markers-tsne.png\",\n      width: \"1755\",\n      height: \"1508\",\n      src: \"/blog/2022-04-amia-tutorial/__optimized-images__/2022-04-26-sc-markers-tsne.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The tutorial will use Orange, our software tool that features workflows, interactive visualizations, and blends visualizations and machine learning. The figure shows the workflow to analyze the landscape of single cells using their expression data and support highlighting of selected gene markers and gene marker discovery. We plan to run this use case and construct a similar workflow during the proposed tutorial.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Covered Topics\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For the prospective or actual teachers and professors of machine learning and data science, we will show how to:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"organize a short introductory or advanced course in data science that can take only a few hours but cover a substantial number of topics from machine learning and data visualization,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"train intuitive concepts of data science, rather than dive into details of mathematics and statistics,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"use the workflows-based environment to soften the learning curve and train data science without invoking any computer programming,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"use interactive visualizations and visual analytics to engage in data exploration and additionally motivate the trainees and make training lessons exciting and engaging,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"design a practical training course that immediately dives into the data and helps trainees understand data science on practical use cases,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"design a course that uses case-based studies and avoids using any powerpoints.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Everyone interested in learning about data science or those who would like to enjoy a fast-paced walk through the machine learning landscape can join the tutorial. The tutorial will cover the topics of\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"clustering, including hierarchical clustering, k-means, visualization of clustering results and explanation of clusters,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"dimensionality reduction approaches and design of data maps with methods like MDS and t-SNE,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"predictive modeling using essential supervised machine learning techniques (logistic regression, random forests, neural networks) and several tools that can explain the structure of the models, like nomograms,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"overfitting, how to cheat with it and how to avoid it,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"evaluation of prediction models, including cross-validation and assessment of accuracy.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The listed topics conceptually cover a substantial part of the machine learning landscape. The workshop aims to demonstrate that using the right tools and pedagogical approaches, these can be covered, in an intuitive way, in only a few hours.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"During the tutorial, we will showcase the data science and machine learning methods on several datasets that will include, among others, data with socioeconomic and health indices of countries, gene expression data from single-cell experiments, and images from molecular biology.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Motivation behind this Tutorial\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Machine learning methods are drivers of change in sciences and engineering. The computational approaches that can sip through vast collections of data, extract exciting patterns, and devise predictive models are becoming omnipresent. But only a few professionals understand the basics of data science, and even fewer engage in building models using their data. We need to devise means to train them and familiarize them with core data science concepts without burdening them with mathematical and computer science details. The tools used in such training could also come handing when presenting and communicating the results of the data analysis, where methods from visual analytics can help us actively engage the data owners. Especially in medicine, it may be essential to train medical professionals and students so that they can gain enough intuition about data science to recognize opportunities that this field can offer and actively engage in data science projects.\"\n    }), \"\\n\", _jsx(_components.h1, {\n      children: \"Intended Audience\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Current or prospective teachers or trainers of data science,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"AIME attendees interested in tools for explorative data analysis, integration of machine learning, and data visualization,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data scientists that would like to familiarize themselves with a workflow-based, visual analytics tool that can support active communication of results of data science to the data owners,\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Anyone curious about how we can pack the essentials of data science within a half a day workshop.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h1, {\n      children: \"Get Engaged\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the tutorial, we will showcase the use of the tool, \", _jsx(_components.a, {\n        href: \"http://orangedatamining.com\",\n        children: \"Orange\"\n      }), \", for data science training through a set of case studies, and with these introduce the data science topics and methods. We encourage the audience to follow the tutorial and work on their laptops. We will also encourage discussion and active participation through lecturer interactions with the audience through questions on the topic and on the chosen case studies.\"]\n    }), \"\\n\", _jsx(_components.h1, {\n      children: \"Previous Similar Tutorials\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the past five years, our group has perfected the hands-in training approach by carrying out close to a hundred hands-on machine learning and data science workshops around the world, including those in Houston, Ås, Pavia, Luxemburg, Kolkata, Liverpool, Bari, Oslo, Ashburn, Konstanz, Belgrade, Helsinki, Melbourne, Sao Paolo, Verona, and Ljubljana. The workshops and tutorials ranged anyway from an hour to a few days. They were carried out for Ph.D. students, researchers, company professionals, employees of the public sector, and teachers and students in high school. Please refer to our \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/blog/workshop/\",\n        children: \"blogs\"\n      }), \" for short reports on some of our workshops.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have also recently published several manuscripts about the proposed training, including:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Demšar J, Zupan B (2021) \", _jsx(_components.a, {\n          href: \"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008671\",\n          children: \"Hands-on training about overfitting\"\n        }), \", PLOS Computational Biology 17(3): e1008671.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Godec P, Pančur M, Ilenič N, Čopar A, Stražar M, Erjavec A, Pretnar A, Demšar J, Starič A, Toplak M, Žagar L, Hartman J, Wang H, Bellazzi R, Petrovič U, Garagna S, Zuccotti M, Park D, Shaulsky G, Zupan B (2019)\\n\", _jsx(_components.a, {\n          href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n          children: \"Democratized image analytics by visual programming through integration of deep models and small-scale machine learning\"\n        }), \", Nature Communications 10(1):4551.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Stražar M, Žagar L, Kokošar J, Tanko V, Erjavec A, Poličar P, Starič A, Demšar J, Shaulsky G, Menon V, Lamire A, Parikh A, and Zupan B (2019) \", _jsx(_components.a, {\n          href: \"https://academic.oup.com/bioinformatics/article/35/14/i4/5529249\",\n          children: \"scOrange – A Tool for Hands-On Training of Concepts from Single Cell Data Analytics\"\n        }), \", Bioinformatics 35(14):i4-i12.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related to the type of training we propose to showcase in the tutorial are also short educational videos on data science in Orange available on \", _jsx(_components.a, {\n        href: \"http://youtube.com/orangedatamining\",\n        children: \"YouTube\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Presenter\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Bla%C5%BE_Zupan\",\n        children: \"Blaž Zupan\"\n      }), \", University of Ljubljana, Slovenia, and Baylor College of Medicine, Houston, USA\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Prof. Dr. Blaž Zupan teaches artificial intelligence and machine learning at the University of Ljubljana and Baylor College of Medicine in Houston. His research has focused on explainable AI and combinations of machine learning and data visualization techniques. He runs a twenty-member bioinformatics laboratory, developing \", _jsx(_components.a, {\n        href: \"http://orangedatamining.com\",\n        children: \"Orange\"\n      }), \", a comprehensive open-source toolbox for machine learning. Orange is a great data science training tool, and Zupan’s group has designed and carried out over a hundred hands-on workshops and courses in the past five years. Zupan \", _jsx(_components.a, {\n        href: \"https://scholar.google.com/scholar?hl=en\u0026as_sdt=0%2C5\u0026q=blaz+zupan\u0026btnG=\",\n        children: \"has published\"\n      }), \" his work in more than a hundred articles that have received over ten thousand citations.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":600,"height":400,"src":"/blog/2022-04-amia-tutorial/__optimized-images__/2022-04-26-wordcloud.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"training-with-orange-a-tutorial-at-aime-2022-in-halifax"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, interactive data visualization, unsupervised, visualization, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Understanding Voting Patterns at AKOS Workshop</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 22, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Two days ago we held another Introduction to Data Mining workshop at our faculty. This time the target audience was a group of public sector professionals and our challenge was finding the right data set to explain key data mining concepts. Iris is fun, but not everyone is a biologist, right? Fortunately, we found this really nice data set with ballot counts from the Slovenian National Assembly (thanks to <a href="https://parlameter.si/">Parlameter</a>).</p>
 <p><strong>Related:</strong> <a href="/blog/2016/10/02/intro-to-data-mining-for-life-scientists/">Intro to Data Mining for Life Scientists</a></p>
 <a href="/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/IMG_20170920_105828-1.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="613" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/IMG_20170920_105828-1.jpg"/></a>
@@ -173,4 +173,4 @@
 <p>It is always great to inspect discovered groups and outliers. This way an expert can interpret the clusters and also explain, what outliers mean. Sometimes it is simply a matter of data (missing values), but sometimes we could find shifting alliances. Perhaps an outlier could be an MP about to switch to another party.</p>
 <a href="/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.56.35.png" data-gallery="true"><img alt="" loading="lazy" width="744" height="610" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.56.35.png"/></a>
 <p>The final workflow.</p>
-<p>You can have fun with <a href="https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/slovenian-national-assembly-eng.tab">these data</a>, too. Let us know if you discover something interesting!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-09-22 11:34:26+00:00","draft":false,"title":"Understanding Voting Patterns at AKOS Workshop","blog":["clustering","interactive data visualization","unsupervised","visualization","workshop"],"oldUrl":"/blog/2017/09/22/understanding-voting-patterns-at-akos-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Two days ago we held another Introduction to Data Mining workshop at our faculty. This time the target audience was a group of public sector professionals and our challenge was finding the right data set to explain key data mining concepts. Iris is fun, but not everyone is a biologist, right? Fortunately, we found this really nice data set with ballot counts from the Slovenian National Assembly (thanks to \", _jsx(_components.a, {\n        href: \"https://parlameter.si/\",\n        children: \"Parlameter\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/10/02/intro-to-data-mining-for-life-scientists/\",\n        children: \"Intro to Data Mining for Life Scientists\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/IMG_20170920_105828-1.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"613\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Workshop for the Agency for Communication Networks and Services (AKOS).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The data contains ballot counts, statistics, and description for 84 members of the parliament (MPs). First, we inspected the data in a Data Table. Each MP is described with 14 meta features and has 18 ballot counts recorded.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-10.31.51.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"591\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Out data has 84 instances, 18 features (ballot counts) and 14 meta features (MP description).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have some numerical features, which means we can also inspect the data in Scatter Plot. We will plot MPs' attendance vs. the number of their initiatives. Quite interesting! There is a big group of MPs who regularly attend the sessions, but rarely propose changes. Could this be the coalition?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-13.48.24.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1196\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot of MPs' session attendance (in percentage) and the number of initiatives. Already an interesting pattern emerges.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The next question that springs to our mind is - can we discover interesting voting patterns from our data? Let us see. We first explored the data in Hierarchical Clustering. Looks like there are some nice clusters in our data! The blue cluster is the coalition, red the SDS party and green the rest (both from the opposition).\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2015-12-02-hierarchical-clustering-a-simple-explanation/\",\n        children: \"Hierarchical Clustering: A Simple Explanation\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-10.58.08.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1557\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hierarchical Clustering visualizes a hierarchy of clusters. But it is hard to observe similarity of pairs of data instances. How similar are Luka Mesec and Branko Grims? It is hard to tell...\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But it is hard to inspect so many data instances in a dendrogram. For example, we have no idea how similar are the voting records of Eva Irgl and Alenka Bratušek. Surely, there must be a better way to explore similarities and perhaps verify that voting patterns exist at even a party-level... Let us try MDS. MDS transforms multidimensional data into a 2D projection so that similar data instances lie close to each other.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.08.28.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1218\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"MDS can plot a multidimensional data in 2D so that similar data points lie close to each other. But sometimes this optimization is hard. This is why we have grey lines connecting the dots - the dots connected are similar at the selected cut-off level (Show similar pairs slider).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ah, this is nice! We even colored data points by the party. MDS beautifully shows the coalition (blue dots) and the opposition (all other colors). Even parties are clustered together. But there are some outliers. Let us inspect Matej Tonin, who is quite far away from his orange group. Seems like he was missing at the last two sessions and did not vote. Hence his voting is treated differently.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.16.02.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"857\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data Table is a handy tool for instant data inspection. It is always great to check, what is on the output of each widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It is always great to inspect discovered groups and outliers. This way an expert can interpret the clusters and also explain, what outliers mean. Sometimes it is simply a matter of data (missing values), but sometimes we could find shifting alliances. Perhaps an outlier could be an MP about to switch to another party.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.56.35.png\",\n      alt: \"\",\n      width: \"744\",\n      height: \"610\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The final workflow.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can have fun with \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/slovenian-national-assembly-eng.tab\",\n        children: \"these data\"\n      }), \", too. 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\ No newline at end of file
+<p>You can have fun with <a href="https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/slovenian-national-assembly-eng.tab">these data</a>, too. Let us know if you discover something interesting!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-09-22 11:34:26+00:00","draft":false,"title":"Understanding Voting Patterns at AKOS Workshop","blog":["clustering","interactive data visualization","unsupervised","visualization","workshop"],"oldUrl":"/blog/2017/09/22/understanding-voting-patterns-at-akos-workshop/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Two days ago we held another Introduction to Data Mining workshop at our faculty. This time the target audience was a group of public sector professionals and our challenge was finding the right data set to explain key data mining concepts. Iris is fun, but not everyone is a biologist, right? Fortunately, we found this really nice data set with ballot counts from the Slovenian National Assembly (thanks to \", _jsx(_components.a, {\n        href: \"https://parlameter.si/\",\n        children: \"Parlameter\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2016/10/02/intro-to-data-mining-for-life-scientists/\",\n        children: \"Intro to Data Mining for Life Scientists\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/IMG_20170920_105828-1.jpg\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"613\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Workshop for the Agency for Communication Networks and Services (AKOS).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The data contains ballot counts, statistics, and description for 84 members of the parliament (MPs). First, we inspected the data in a Data Table. Each MP is described with 14 meta features and has 18 ballot counts recorded.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-10.31.51.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"591\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Out data has 84 instances, 18 features (ballot counts) and 14 meta features (MP description).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have some numerical features, which means we can also inspect the data in Scatter Plot. We will plot MPs' attendance vs. the number of their initiatives. Quite interesting! There is a big group of MPs who regularly attend the sessions, but rarely propose changes. Could this be the coalition?\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-13.48.24.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1196\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot of MPs' session attendance (in percentage) and the number of initiatives. Already an interesting pattern emerges.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The next question that springs to our mind is - can we discover interesting voting patterns from our data? Let us see. We first explored the data in Hierarchical Clustering. Looks like there are some nice clusters in our data! The blue cluster is the coalition, red the SDS party and green the rest (both from the opposition).\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Related:\"\n      }), \" \", _jsx(_components.a, {\n        href: \"/blog/2015-12-02-hierarchical-clustering-a-simple-explanation/\",\n        children: \"Hierarchical Clustering: A Simple Explanation\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-10.58.08.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1557\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hierarchical Clustering visualizes a hierarchy of clusters. But it is hard to observe similarity of pairs of data instances. How similar are Luka Mesec and Branko Grims? It is hard to tell...\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But it is hard to inspect so many data instances in a dendrogram. For example, we have no idea how similar are the voting records of Eva Irgl and Alenka Bratušek. Surely, there must be a better way to explore similarities and perhaps verify that voting patterns exist at even a party-level... Let us try MDS. MDS transforms multidimensional data into a 2D projection so that similar data instances lie close to each other.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.08.28.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"1218\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"MDS can plot a multidimensional data in 2D so that similar data points lie close to each other. But sometimes this optimization is hard. This is why we have grey lines connecting the dots - the dots connected are similar at the selected cut-off level (Show similar pairs slider).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ah, this is nice! We even colored data points by the party. MDS beautifully shows the coalition (blue dots) and the opposition (all other colors). Even parties are clustered together. But there are some outliers. Let us inspect Matej Tonin, who is quite far away from his orange group. Seems like he was missing at the last two sessions and did not vote. Hence his voting is treated differently.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.16.02.png\",\n      alt: \"\",\n      width: \"1600\",\n      height: \"857\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data Table is a handy tool for instant data inspection. It is always great to check, what is on the output of each widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It is always great to inspect discovered groups and outliers. This way an expert can interpret the clusters and also explain, what outliers mean. Sometimes it is simply a matter of data (missing values), but sometimes we could find shifting alliances. Perhaps an outlier could be an MP about to switch to another party.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2017-09-understanding-voting-patterns-at-akos-workshop/__optimized-images__/Screen-Shot-2017-09-22-at-11.56.35.png\",\n      alt: \"\",\n      width: \"744\",\n      height: \"610\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The final workflow.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can have fun with \", _jsx(_components.a, {\n        href: \"https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/slovenian-national-assembly-eng.tab\",\n        children: \"these data\"\n      }), \", too. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Unfreezing Orange</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Unfreezing Orange"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-04-05 08:22:20+00:00"/><meta property="article:author" content="MARKO"/><meta property="article:tag" content="neuralnetwork"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="parallelization"/><meta property="article:tag" content="performance"/><meta property="article:tag" content="programming"/><meta property="article:tag" content="python"/><meta property="article:tag" content="qt"/><meta name="next-head-count" content="23"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>neuralnetwork, orange3, parallelization, performance, programming, python, qt</strong></p><h1 class="sc-c67a4900-0 loxVcf">Unfreezing Orange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>MARKO</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 05, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Have you ever tried Orange with data big enough that some widgets ran for more than a second? Then you have seen it: Orange froze. While the widget was processing, the interface would not respond to any inputs, and there was no way to stop that widget.</p>
 <p>Not all the widgets freeze, though! Some widgets, like Test &amp; Score, k-Means, or Image Embedding, do not block. While they are working, we are free to build other parts of the workflow, and these widgets also show their progress. Some, like Image Embedding, which work with lots of images, even allow interruptions.</p>
 <a href="/blog/2018-04-unfreezing-orange/__optimized-images__/progressbar.png" data-gallery="true"><img alt="" loading="lazy" width="359" height="294" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-04-unfreezing-orange/__optimized-images__/progressbar.png"/></a>
@@ -163,4 +163,4 @@
 <a href="/blog/2018-04-unfreezing-orange/__optimized-images__/figure.png" data-gallery="true"><img alt="" loading="lazy" width="1510" height="610" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-04-unfreezing-orange/__optimized-images__/figure.png"/></a>
 <p>Starting the task and showing the results are straightforward and <a href="https://orange-widget-base.readthedocs.io/en/latest/tutorial-responsive-gui.html">well documented in a tutorial for writing widgets</a>. Periodic communication with stopping is harder: it is completely task-dependent and can be either trivial, hard, or even impossible. Periodic communication is, in principle, unessential for responsiveness, but if we do not implement it, we will be unable to stop the running task and progress bars would not work either.</p>
 <p>Taking care of periodic communication was the hardest part of making the Neural Network widget responsive. It would have been easy, had we implemented neural networks ourselves. But we use the <a href="http://scikit-learn.org/stable/modules/neural_networks_supervised.html">scikit-learn implementation</a>, which does not expose an option to make additional function calls while fitting the network (we need to run code that communicates with the interface). We had to resort to a trick: we modified fitting so that a change to an attribute called <code>n_iters_</code> called a function (<a href="https://github.com/biolab/orange3/pull/2958">see pull request</a>). Not the cleanest solution, but it seems to work.</p>
-<p>For now, only a few widgets work so that the interface remains responsive. We are still searching for the best way to make existing widgets behave nicely, but responsiveness is now one of our priorities.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2018-04-05 08:22:20+00:00","draft":false,"title":"Unfreezing Orange","blog":["neuralnetwork","orange3","parallelization","performance","programming","python","qt"],"oldUrl":"/blog/2018/04/05/unfreezing-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\",\n    a: \"a\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Have you ever tried Orange with data big enough that some widgets ran for more than a second? Then you have seen it: Orange froze. While the widget was processing, the interface would not respond to any inputs, and there was no way to stop that widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Not all the widgets freeze, though! Some widgets, like Test \u0026 Score, k-Means, or Image Embedding, do not block. While they are working, we are free to build other parts of the workflow, and these widgets also show their progress. Some, like Image Embedding, which work with lots of images, even allow interruptions.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2018-04-unfreezing-orange/__optimized-images__/progressbar.png\",\n      alt: \"\",\n      width: \"359\",\n      height: \"294\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Why does Orange freeze? 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Periodic communication with stopping is harder: it is completely task-dependent and can be either trivial, hard, or even impossible. Periodic communication is, in principle, unessential for responsiveness, but if we do not implement it, we will be unable to stop the running task and progress bars would not work either.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Taking care of periodic communication was the hardest part of making the Neural Network widget responsive. It would have been easy, had we implemented neural networks ourselves. But we use the \", _jsx(_components.a, {\n        href: \"http://scikit-learn.org/stable/modules/neural_networks_supervised.html\",\n        children: \"scikit-learn implementation\"\n      }), \", which does not expose an option to make additional function calls while fitting the network (we need to run code that communicates with the interface). 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+<p>For now, only a few widgets work so that the interface remains responsive. We are still searching for the best way to make existing widgets behave nicely, but responsiveness is now one of our priorities.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"MARKO","date":"2018-04-05 08:22:20+00:00","draft":false,"title":"Unfreezing Orange","blog":["neuralnetwork","orange3","parallelization","performance","programming","python","qt"],"oldUrl":"/blog/2018/04/05/unfreezing-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\",\n    a: \"a\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Have you ever tried Orange with data big enough that some widgets ran for more than a second? 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Univariate GSoC Success</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Univariate GSoC Success"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-04-14 08:55:14+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="data"/><meta property="article:tag" content="distribution"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="gsoc2016"/><meta property="article:tag" content="plot"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="23"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, data, distribution, gsoc, gsoc2016, plot, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Univariate GSoC Success</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 14, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="https://developers.google.com/open-source/gsoc/">Google Summer of Code</a> application period has come to an end. We&#x27;ve received 34 applications, some of which were of truly high quality. Now it&#x27;s upon us to select the top performing candidates, but before that we wanted to have an overlook of the candidate pool. We&#x27;ve gathered data from our Google Form application and gave it a quick view in Orange.</p>
 <p>First, we needed to preprocess the data a bit, since it came in a messy form of strings. Feature Constructor to the rescue! We wanted to extract the OS usage across users. So we first made three new variables named &#x27;uses linux&#x27;, &#x27;uses windows&#x27; and &#x27;uses osx&#x27; to represent our three new columns. For each column we searched through &#x27;OS_of_choice_and_why&#x27;, looked up the value of the column, converted it to string, put the string in lowercase, found mentions of either &#x27;linux&#x27;, &#x27;windows&#x27; or &#x27;osx&#x27;, and voila.... if a mention occurred in the string, we marked the column with 1, else with 0.</p>
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@@ -171,4 +171,4 @@
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 OSS all the way!</p>
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-Some people love dogs and some love cats. Others prefer elephants and butterflies.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-04-14 08:55:14+00:00","draft":false,"title":"Univariate GSoC Success","blog":["analysis","data","distribution","gsoc","gsoc2016","plot","visualization"],"oldUrl":"/blog/2016/04/14/univariate-gsoc-success/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://developers.google.com/open-source/gsoc/\",\n        children: \"Google Summer of Code\"\n      }), \" application period has come to an end. 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+Some people love dogs and some love cats. Others prefer elephants and butterflies.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-04-14 08:55:14+00:00","draft":false,"title":"Univariate GSoC Success","blog":["analysis","data","distribution","gsoc","gsoc2016","plot","visualization"],"oldUrl":"/blog/2016/04/14/univariate-gsoc-success/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://developers.google.com/open-source/gsoc/\",\n        children: \"Google Summer of Code\"\n      }), \" application period has come to an end. 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We want Orange to display 'dogs' for someone who replied 'dogs', 'cats' for someone who replied 'cats' and '?' if the questions was a blank or very creative (we had people who wanted to be elephants and butterflies :) ).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To create three discrete values you would write:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"    0 if 'dogs' in str(Dogs_or_cats_.value).lower() else 1 if  'cats' in str(Dogs_or_cats_.value).lower() else 2\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Since we have three values, we need to assign them the corresponding indexes. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>documentation, orange3, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Updated Widget Documentation</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 04, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Happy news for all passionate Orange users! We’ve uploaded documentation for our Orange 3 widget selection.</p>
 <a href="/blog/2015-09-updated-widget-documentation/__optimized-images__/blog2.png" data-gallery="true"><img alt="" loading="lazy" width="616" height="539" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-09-updated-widget-documentation/__optimized-images__/blog2.png"/></a>
 <p>Right click and select &quot;Help&quot; or press F1.</p>
@@ -158,4 +158,4 @@
 <a href="/blog/2015-09-updated-widget-documentation/__optimized-images__/blog1.png" data-gallery="true"><img alt="" loading="lazy" width="842" height="695" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-09-updated-widget-documentation/__optimized-images__/blog1.png"/></a>
 <p>Widget documentation.</p>
 <p>** **</p>
-<p>We are going to be updating documentation as the widgets continue to develop. Documentation for bioinformatics and data fusion add-ons is expected to be up and running in the following week.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-09-04 13:00:18+00:00","draft":false,"title":"Updated Widget Documentation","blog":["documentation","orange3","widget"],"oldUrl":"/blog/2015/09/04/updated-widget-documentation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Happy news for all passionate Orange users! We’ve uploaded documentation for our Orange 3 widget selection.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-updated-widget-documentation/__optimized-images__/blog2.png\",\n      alt: \"\",\n      width: \"616\",\n      height: \"539\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Right click and select \\\"Help\\\" or press F1.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"** **\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It’s easy to use. To learn more about a particular wigdet, click on the widget. Either use right click and select “Help” or press F1. A new window will open with a widget description and an example for its use. There are also screenshots included as visual help.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-updated-widget-documentation/__optimized-images__/blog1.png\",\n      alt: \"\",\n      width: \"842\",\n      height: \"695\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Widget documentation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"** **\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are going to be updating documentation as the widgets continue to develop. Documentation for bioinformatics and data fusion add-ons is expected to be up and running in the following week.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"updated-widget-documentation"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<p>We are going to be updating documentation as the widgets continue to develop. Documentation for bioinformatics and data fusion add-ons is expected to be up and running in the following week.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-09-04 13:00:18+00:00","draft":false,"title":"Updated Widget Documentation","blog":["documentation","orange3","widget"],"oldUrl":"/blog/2015/09/04/updated-widget-documentation/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Happy news for all passionate Orange users! We’ve uploaded documentation for our Orange 3 widget selection.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-updated-widget-documentation/__optimized-images__/blog2.png\",\n      alt: \"\",\n      width: \"616\",\n      height: \"539\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Right click and select \\\"Help\\\" or press F1.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"** **\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It’s easy to use. To learn more about a particular wigdet, click on the widget. Either use right click and select “Help” or press F1. A new window will open with a widget description and an example for its use. There are also screenshots included as visual help.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2015-09-updated-widget-documentation/__optimized-images__/blog1.png\",\n      alt: \"\",\n      width: \"842\",\n      height: \"695\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Widget documentation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"** **\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We are going to be updating documentation as the widgets continue to develop. Documentation for bioinformatics and data fusion add-ons is expected to be up and running in the following week.\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"updated-widget-documentation"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>distribution, orange3, release, version</strong></p><h1 class="sc-c67a4900-0 loxVcf">Version 3.3.1 - Updates and Features</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 01, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>About a week ago we issued an updated stable release of Orange, version 3.3.1. We&#x27;ve introduced some new functionalities and improved a few old ones.</p>
 <p>Here&#x27;s what&#x27;s new in this release:</p>
 <ul>
@@ -176,4 +176,4 @@
 <p>Graphs in Classification Tree Viewer can be saved in <strong>.dot</strong> format.</p>
 </li>
 </ul>
-<p>You can view the entire <a href="https://github.com/biolab/orange3/blob/master/CHANGELOG.md">changelog</a> here. :) Enjoy the improvements!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-04-01 12:43:53+00:00","draft":false,"title":"Version 3.3.1 - Updates and Features","blog":["distribution","orange3","release","version"],"oldUrl":"/blog/2016/04/01/version-3-3-1-updates-and-features/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"About a week ago we issued an updated stable release of Orange, version 3.3.1. 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+<p>You can view the entire <a href="https://github.com/biolab/orange3/blob/master/CHANGELOG.md">changelog</a> here. :) Enjoy the improvements!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2016-04-01 12:43:53+00:00","draft":false,"title":"Version 3.3.1 - Updates and Features","blog":["distribution","orange3","release","version"],"oldUrl":"/blog/2016/04/01/version-3-3-1-updates-and-features/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"About a week ago we issued an updated stable release of Orange, version 3.3.1. 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@@ -165,4 +165,4 @@
 <p>We use two data sets in these videos. One includes imaginary data on student grades. Our task is to find student clusters and understand the characteristics of the students in each group.</p>
 <a href="/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-grades-clustering.png" data-gallery="true"><img alt="" loading="lazy" width="940" height="588" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-grades-clustering.png"/></a>
 <p>The second data set contains socioeconomic information on countries of the world. Here, we aimed to find clusters of countries with similar socioeconomic characteristics. It is interesting how easy it is to use Orange to find regions of comparable countries and how the world, despite its connectedness, is split and different.</p>
-<a href="/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-countries.png" data-gallery="true"><img alt="" loading="lazy" width="892" height="700" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-countries.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-11-25","draft":false,"title":"Videos on hierarchical clustering","thumbImage":"2022-11-25-video-series-clustering.png","frontPageImage":"2022-11-25-video-series-clustering.png","blog":["education"],"shortExcerpt":"Six new videos explaining hierarchical clustering in Orange are now online on YouTube.","longExcerpt":"Six new videos explaining hierarchical clustering in Orange are now online on YouTube.","oldUrl":"/blog/2022/2022-25-11-clustering-videos/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"The six videos explaining hierarchical clustering in Orange are a part of \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-b_SuBdoRsuNhTmaHJ0eKab\",\n        children: \"Introduction to Data Science series\"\n      }), \". 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+<a href="/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-countries.png" data-gallery="true"><img alt="" loading="lazy" width="892" height="700" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-12-clustering-videos/__optimized-images__/2022-11-25-countries.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Blaž Zupan","date":"2022-11-25","draft":false,"title":"Videos on hierarchical clustering","thumbImage":"2022-11-25-video-series-clustering.png","frontPageImage":"2022-11-25-video-series-clustering.png","blog":["education"],"shortExcerpt":"Six new videos explaining hierarchical clustering in Orange are now online on YouTube.","longExcerpt":"Six new videos explaining hierarchical clustering in Orange are now online on YouTube.","oldUrl":"/blog/2022/2022-25-11-clustering-videos/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"The six videos explaining hierarchical clustering in Orange are a part of \", _jsx(_components.a, {\n        href: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-b_SuBdoRsuNhTmaHJ0eKab\",\n        children: \"Introduction to Data Science series\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>clustering, images, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Viewing Images</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">Apr 29, 2014</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>I am lately having fun with Image Viewer. The widget has been recently updated and can display images stored locally or on the internet. But wait, what images? How on earth can Orange now display images if it can handle mere tabular or basket-based data?</p>
 <p>Here&#x27;s an example. I have considered a subset of animals from the [download id=&quot;864&quot;] data set (comes with Orange installation), and for demonstration purposes selected only a handful of attributes. I have added a new string attribute (&quot;images&quot;) and declared that this is a meta attribute of the type &quot;image&quot;. The values of this attribute are links to images on the web:</p>
 <a href="/blog/2014-04-viewing-images/__optimized-images__/animals-dataset_1.png__610x213_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="610" height="213" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2014-04-viewing-images/__optimized-images__/animals-dataset_1.png__610x213_q95_crop_upscale.png"/></a>
@@ -157,4 +157,4 @@
 <a href="/blog/2014-04-viewing-images/__optimized-images__/animals-schema.png__610x428_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="610" height="428" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2014-04-viewing-images/__optimized-images__/animals-schema.png__610x428_q95_crop_upscale.png"/></a>
 <p>Typically and just like above, you would use a string meta attribute to store the link to images. Images can be referred to using a HTTP address, or, if stored locally, using a relative path from the data file location to the image files.</p>
 <p>Here is another example, where all the images were local and we have associated them with a famous <a href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">digits data set</a> ( <a href="/blog/2014-04-viewing-images/">download id=&quot;868&quot;</a> is a data set in the Orange format with the image files). The task for this data set is to classify handwritten digits based on their bitmap representation. In the schema below we wanted to find out which are the most frequent errors some classification algorithm would make, and how do the images of the misclassified digits look like. Turns out that SVM with RBF kernel most often misclassify the digit 9 and confuses it with a digit 3:</p>
-<a href="/blog/2014-04-viewing-images/__optimized-images__/digits-schema.png__610x495_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="610" height="495" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2014-04-viewing-images/__optimized-images__/digits-schema.png__610x495_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2014-04-29 21:28:00+00:00","draft":false,"title":"Viewing Images","blog":["clustering","images","visualization"],"oldUrl":"/blog/2014/04/29/viewing-images/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"I am lately having fun with Image Viewer. 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+<a href="/blog/2014-04-viewing-images/__optimized-images__/digits-schema.png__610x495_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="610" height="495" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2014-04-viewing-images/__optimized-images__/digits-schema.png__610x495_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2014-04-29 21:28:00+00:00","draft":false,"title":"Viewing Images","blog":["clustering","images","visualization"],"oldUrl":"/blog/2014/04/29/viewing-images/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    img: \"img\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"I am lately having fun with Image Viewer. 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diff --git a/blog/visualization-of-classification-probabilities/index.html b/blog/visualization-of-classification-probabilities/index.html
index 0ae6b1127..20662137a 100644
--- a/blog/visualization-of-classification-probabilities/index.html
+++ b/blog/visualization-of-classification-probabilities/index.html
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Visualization of Classification Probabilities</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Visualization of Classification Probabilities"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2016-08-16 17:32:20+00:00"/><meta property="article:author" content="PRIMOZGODEC"/><meta property="article:tag" content="addons"/><meta property="article:tag" content="classification"/><meta property="article:tag" content="gsoc"/><meta property="article:tag" content="gsoc2016"/><meta property="article:tag" content="orange3"/><meta property="article:tag" content="widget"/><meta name="next-head-count" content="22"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, classification, gsoc, gsoc2016, orange3, widget</strong></p><h1 class="sc-c67a4900-0 loxVcf">Visualization of Classification Probabilities</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>PRIMOZGODEC</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 16, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><em>This is a guest blog from the Google Summer of Code project.</em></p>
 <p><a href="http://orange3-educational.readthedocs.io/en/latest/widgets/polynomialclassification.html">Polynomial Classification</a> widget is implemented as a part of my <a href="https://developers.google.com/open-source/gsoc/">Google Summer of Code</a> project along with other widgets in educational add-on (see my <a href="/blog/2016/08/12/interactive-k-means/">previous blog</a>). It visualizes probabilities for two-class classification (target vs. rest) using color gradient and contour lines, and it can do so for any Orange learner.</p>
 <p>Here is an example workflow. The data comes from the <a href="http://orange3.readthedocs.io/en/latest/widgets/data/file.html">File</a> widget. With no learner on input, the default is <a href="http://orange3.readthedocs.io/en/latest/widgets/classify/logisticregression.html">Logistic Regression</a>. Widget outputs learners <em>Coefficients,</em> <em>Classifier (model)</em> and <em>Learner.</em></p>
@@ -169,4 +169,4 @@
 <a href="/blog/2016-08-polynomial-classification/__optimized-images__/poly-classification-4e.png" data-gallery="true"><img alt="" loading="lazy" width="1386" height="1040" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-08-polynomial-classification/__optimized-images__/poly-classification-4e.png"/></a>
 <p>Polynomial expansion if high degrees may be dangerous. Following example shows overfitting when degree is five. See the two outliers, a blue one on the top and the red one at the lower right of the plot? The classifier was unnecessary able to separate the outliers from the pack, something that will become problematic when classifier will be used on the new data.</p>
 <a href="/blog/2016-08-polynomial-classification/__optimized-images__/poly-classification-owerfit.png" data-gallery="true"><img alt="" loading="lazy" width="1087" height="640" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-08-polynomial-classification/__optimized-images__/poly-classification-owerfit.png"/></a>
-<p>Overfitting is one of the central problems in machine learning. You are welcome to read our <a href="/blog/2016/03/12/overfitting-and-regularization/">previous blog on this problem and possible solutions</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"PRIMOZGODEC","date":"2016-08-16 17:32:20+00:00","draft":false,"title":"Visualization of Classification Probabilities","blog":["addons","classification","gsoc","gsoc2016","orange3","widget"],"oldUrl":"/blog/2016/08/16/polynomial-classification/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://orange3-educational.readthedocs.io/en/latest/widgets/polynomialclassification.html\",\n        children: \"Polynomial Classification\"\n      }), \" widget is implemented as a part of my \", _jsx(_components.a, {\n        href: \"https://developers.google.com/open-source/gsoc/\",\n        children: \"Google Summer of Code\"\n      }), \" project along with other widgets in educational add-on (see my \", _jsx(_components.a, {\n        href: \"/blog/2016/08/12/interactive-k-means/\",\n        children: \"previous blog\"\n      }), \"). 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+<p>Overfitting is one of the central problems in machine learning. You are welcome to read our <a href="/blog/2016/03/12/overfitting-and-regularization/">previous blog on this problem and possible solutions</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"PRIMOZGODEC","date":"2016-08-16 17:32:20+00:00","draft":false,"title":"Visualization of Classification Probabilities","blog":["addons","classification","gsoc","gsoc2016","orange3","widget"],"oldUrl":"/blog/2016/08/16/polynomial-classification/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://orange3-educational.readthedocs.io/en/latest/widgets/polynomialclassification.html\",\n        children: \"Polynomial Classification\"\n      }), \" widget is implemented as a part of my \", _jsx(_components.a, {\n        href: \"https://developers.google.com/open-source/gsoc/\",\n        children: \"Google Summer of Code\"\n      }), \" project along with other widgets in educational add-on (see my \", _jsx(_components.a, {\n        href: \"/blog/2016/08/12/interactive-k-means/\",\n        children: \"previous blog\"\n      }), \"). It visualizes probabilities for two-class classification (target vs. rest) using color gradient and contour lines, and it can do so for any Orange learner.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is an example workflow. The data comes from the \", _jsx(_components.a, {\n        href: \"http://orange3.readthedocs.io/en/latest/widgets/data/file.html\",\n        children: \"File\"\n      }), \" widget. With no learner on input, the default is \", _jsx(_components.a, {\n        href: \"http://orange3.readthedocs.io/en/latest/widgets/classify/logisticregression.html\",\n        children: \"Logistic Regression\"\n      }), \". Widget outputs learners \", _jsx(_components.em, {\n        children: \"Coefficients,\"\n      }), \" \", _jsx(_components.em, {\n        children: \"Classifier (model)\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Learner.\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-polynomial-classification/__optimized-images__/poly-classification-flow.png\",\n      alt: \"\",\n      width: \"504\",\n      height: \"296\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Polynomial Classification widget works on two continuous features only, all other features are ignored. The screenshot shows plot of classification for an \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Iris_flower_data_set\",\n        children: \"Iris data set\"\n      }), \" .\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-polynomial-classification/__optimized-images__/polynomial-classification-1-stamped.png\",\n      alt: \"\",\n      width: \"1087\",\n      height: \"640\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Set name of the learner. This is the name of learner on output.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set features that logistic regression is performed on.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set class that is classified separately from other classes.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Set the degree of a polynom that is used to transform an input data (1 means attributes are not transformed).\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select whether see or not contour lines in chart. The density of contours is regulated by \", _jsx(_components.em, {\n          children: \"Contour step.\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The classification for our case fails in separating Iris-versicolor from the other two classes. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Visualizations 101</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Visualizations 101"/><meta property="og:description" content="How to read different visualizations?"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2021-12-17"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="visualization"/><meta property="article:tag" content="data mining"/><meta property="article:tag" content="box plot"/><meta property="article:tag" content="scatter plot"/><meta property="article:tag" content="distributions"/><meta property="og:image" content="/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-viz.png"/><meta property="og:image:width" content="599"/><meta property="og:image:height" content="400"/><meta name="next-head-count" content="21"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>visualization, data mining, box plot, scatter plot, distributions</strong></p><h1 class="sc-c67a4900-0 loxVcf">Visualizations 101</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Dec 17, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange has a wide array of visualizations, which enable exploring the data from different perspectives. But each visualization is unique - it is used for a specific purpose, which is closely related to how one interprets the plot. Let&#x27;s have a look at the most common visualizations in Orange, when to use them and how to read them. We will use the <em>heart_disease.tab</em> data set from the File widget in the next examples.</p>
 <h3>Scatter Plot</h3>
 <p>Scatter plot is suitable for displaying a relationship between two numeric variables. It shows a 2-dimensional plot where points represent data instances (rows). The position of each point is defined by its value for x axis and y axis. The plot can also show relations to the third variable, either numeric or categorical, by setting the color, size, or shape of data points.</p>
@@ -183,4 +183,4 @@ <h3>Sieve Diagram</h3>
 <p>In the left corner of the plot we once again meet the Chi-squared, which tells us how significant the combination actually is.</p>
 <p><strong>How to read the plot</strong>: Based on the data set, it is more likely the patients between 55 and 60 years of age will have diameter narrowing. The p-value is below 0.05.</p>
 <h3>Word of caution</h3>
-<p>Data mining is about finding patterns in the data. But sometimes, patterns are random and correlation does not necessarily mean causation. So be careful when interpreting the results. Always disclose the size of your data set and how it was gathered. Report the results with the awareness, that you are only observing a sample of the population. Remember that p-values are the chance that the null hypothesis is true. Even if the p-value is 0.05, the null hypothesis could still be true - you would actually expect this to happen 5 times out of a 100, so keep this in mind, especially when you test 100+ variables!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-12-17","draft":false,"title":"Visualizations 101","thumbImage":"2021-12-17-viz.png","frontPageImage":"2021-12-17-viz.png","blog":["visualization","data mining","box plot","scatter plot","distributions"],"shortExcerpt":"How to read different visualizations?","longExcerpt":"When to use certain visualization and how to read it?","x2images":false,"oldUrl":"/blog/2021/2021-12-17-visualizations-101/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    h3: \"h3\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange has a wide array of visualizations, which enable exploring the data from different perspectives. But each visualization is unique - it is used for a specific purpose, which is closely related to how one interprets the plot. Let's have a look at the most common visualizations in Orange, when to use them and how to read them. We will use the \", _jsx(_components.em, {\n        children: \"heart_disease.tab\"\n      }), \" data set from the File widget in the next examples.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Scatter Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot is suitable for displaying a relationship between two numeric variables. It shows a 2-dimensional plot where points represent data instances (rows). The position of each point is defined by its value for x axis and y axis. The plot can also show relations to the third variable, either numeric or categorical, by setting the color, size, or shape of data points.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-scatter-plot.png\",\n      width: \"1928\",\n      height: \"1196\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-scatter-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the above image we see a relationship between \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"max HR\"\n      }), \" for the 303 patients in the data set. The color of the point corresponds to the presence or absence of diameter narrowing. We see that the presence of heart disease is slightly more frequent at the age of 60, but the difference is not entirely clear and we should't draw too many conclusions from this. What we can conclude is that the maximum heart rate falls with age - regression lines are sloping downwards. Interestingly, the values of max HR at younger ages are already noticeably lower for patients with diameter narrowing (red points and line). That said, the average maximum heart rate for healthy patients (no diameter narrowing) approaches that for sick patients (with diameter narrowing).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"max HR\"\n      }), \" are negatively correlated. The higher the age, the lower the maximum heart rate.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Box Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Box plot is perfect for initial exploration of the data set. It shows basic statistics for each variable and it can handle both numeric and categorical variables.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-box-plot-num2.png\",\n      width: \"1616\",\n      height: \"940\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-box-plot-num2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Box plot for numeric variables shows a whisker plot, which displays essential statistics. Above is an example with the \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" variable, which is by default split by the target variable \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \" in Orange. The visualization shows two box plots, one for each target value (patients with and without diameter narrowing).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The yellow vertical line shows the median value for each box. The blue vertical line shows the mean with the standard deviation represented by the horizontal thin blue line, which shows how \\\"spread out\\\" the values in the sample are. The big blue box is the data between the 25th and 75th percentile, that is the central 50% of the data. The line on the left extends to the minimum value, while the line of the right extends to the maximum value.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-box-plot-cat.png\",\n      width: \"1646\",\n      height: \"1056\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-box-plot-cat.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Categorical variables show the number (or proportion) of data instances for each value. These are no longer box plot, but stacked bar charts.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A great option in Orange is setting the \", _jsx(_components.em, {\n        children: \"Order by relevance to subgroup\"\n      }), \" checkbox, which orders the variables in the top left box by how much their values differ between groups defined by the selected Subgroup variable. It uses Chi-square test for categorical variables and compares the means of numeric variables using Student's T for two and ANOVA for multiple groups. Values of the test statistic are displayed below the plot. Normally, higher values are preferred, but the most important is the p-value - if it is below 0.05, the difference in distributions is generally considered significant.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": \", _jsx(_components.em, {\n        children: \"thal\"\n      }), \" is the variable that best separates between patients with and without diameter narrowing. There is a higher incidence of reversable defect in patients with diameter narrowing (64% have reversable defect) than in those without diameter narrowing (17% have reversable defect).\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Distributions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Distributions show histograms for numeric variables, where values within a given range will be assigned a bin. For categorical data, they show bar plots, that is the frequency of each value. In the below example, there are two bins, one with values below 50.87 and one with values above 50.78.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-distributions.png\",\n      width: \"1894\",\n      height: \"1166\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-distributions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget can be used to display differences in distributions.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": In the group of patients that are younger than 50 years, there is a 50% lower presence of diameter narrowing (70 % of patients don't have the condition).\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Sieve Diagram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sieve diagram is a complex, yet extremely useful visualization. It shows the difference between the expected and observed variable frequency. In other words, if a combination of two values, i.e. being 60 years old and having a heart disease, is more frequent than expected, Sieve diagram will show it. Hence it is primarily intended for categorical variables - numeric variables will be discretized.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-sieve.png\",\n      width: \"1096\",\n      height: \"1340\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-sieve.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the scatter plot above, we observed a slightly higher incidence of diameter narrowing in patient around 60 years of age. Let us check this is Sieve diagram.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"On y axis we see \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \", with the size of each box vertically corresponding to the proportion of instances with and without condition. On x axis we see \", _jsx(_components.em, {\n        children: \"age\"\n      }), \", with the size of each box horizontally corresponding to the size of each bin. As numeric variables are discretized with equal frequency discretization, the box will always be the same size.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The color of the box corresponds to how overrepresented (blue) or underrepresented (red) a certain combination is. For example, there are 73 patients (24 %) between 55.5 and 60.5 years old in the data set. There are also 139 patients (46%) with diameter narrowing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If these two variables would be unrelated, we would expect around 11% of patients with both values (0.24 x 0.46 = 0.11), that is patients between 55 and 60 years old with a diameter narrowing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But if we observe the data set, we can see there are actually 15% of such patients. That is more than we would expect, so a combination of the two values could be statistically significant.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the left corner of the plot we once again meet the Chi-squared, which tells us how significant the combination actually is.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": Based on the data set, it is more likely the patients between 55 and 60 years of age will have diameter narrowing. The p-value is below 0.05.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Word of caution\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data mining is about finding patterns in the data. But sometimes, patterns are random and correlation does not necessarily mean causation. So be careful when interpreting the results. Always disclose the size of your data set and how it was gathered. 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\ No newline at end of file
+<p>Data mining is about finding patterns in the data. But sometimes, patterns are random and correlation does not necessarily mean causation. So be careful when interpreting the results. Always disclose the size of your data set and how it was gathered. Report the results with the awareness, that you are only observing a sample of the population. Remember that p-values are the chance that the null hypothesis is true. Even if the p-value is 0.05, the null hypothesis could still be true - you would actually expect this to happen 5 times out of a 100, so keep this in mind, especially when you test 100+ variables!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-12-17","draft":false,"title":"Visualizations 101","thumbImage":"2021-12-17-viz.png","frontPageImage":"2021-12-17-viz.png","blog":["visualization","data mining","box plot","scatter plot","distributions"],"shortExcerpt":"How to read different visualizations?","longExcerpt":"When to use certain visualization and how to read it?","x2images":false,"oldUrl":"/blog/2021/2021-12-17-visualizations-101/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    h3: \"h3\",\n    strong: \"strong\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Orange has a wide array of visualizations, which enable exploring the data from different perspectives. But each visualization is unique - it is used for a specific purpose, which is closely related to how one interprets the plot. Let's have a look at the most common visualizations in Orange, when to use them and how to read them. We will use the \", _jsx(_components.em, {\n        children: \"heart_disease.tab\"\n      }), \" data set from the File widget in the next examples.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Scatter Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot is suitable for displaying a relationship between two numeric variables. It shows a 2-dimensional plot where points represent data instances (rows). The position of each point is defined by its value for x axis and y axis. The plot can also show relations to the third variable, either numeric or categorical, by setting the color, size, or shape of data points.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-scatter-plot.png\",\n      width: \"1928\",\n      height: \"1196\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-scatter-plot.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the above image we see a relationship between \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"max HR\"\n      }), \" for the 303 patients in the data set. The color of the point corresponds to the presence or absence of diameter narrowing. We see that the presence of heart disease is slightly more frequent at the age of 60, but the difference is not entirely clear and we should't draw too many conclusions from this. What we can conclude is that the maximum heart rate falls with age - regression lines are sloping downwards. Interestingly, the values of max HR at younger ages are already noticeably lower for patients with diameter narrowing (red points and line). That said, the average maximum heart rate for healthy patients (no diameter narrowing) approaches that for sick patients (with diameter narrowing).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"max HR\"\n      }), \" are negatively correlated. The higher the age, the lower the maximum heart rate.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Box Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Box plot is perfect for initial exploration of the data set. It shows basic statistics for each variable and it can handle both numeric and categorical variables.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-box-plot-num2.png\",\n      width: \"1616\",\n      height: \"940\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-box-plot-num2.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Box plot for numeric variables shows a whisker plot, which displays essential statistics. Above is an example with the \", _jsx(_components.em, {\n        children: \"age\"\n      }), \" variable, which is by default split by the target variable \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \" in Orange. The visualization shows two box plots, one for each target value (patients with and without diameter narrowing).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The yellow vertical line shows the median value for each box. The blue vertical line shows the mean with the standard deviation represented by the horizontal thin blue line, which shows how \\\"spread out\\\" the values in the sample are. The big blue box is the data between the 25th and 75th percentile, that is the central 50% of the data. The line on the left extends to the minimum value, while the line of the right extends to the maximum value.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-box-plot-cat.png\",\n      width: \"1646\",\n      height: \"1056\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-box-plot-cat.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Categorical variables show the number (or proportion) of data instances for each value. These are no longer box plot, but stacked bar charts.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A great option in Orange is setting the \", _jsx(_components.em, {\n        children: \"Order by relevance to subgroup\"\n      }), \" checkbox, which orders the variables in the top left box by how much their values differ between groups defined by the selected Subgroup variable. It uses Chi-square test for categorical variables and compares the means of numeric variables using Student's T for two and ANOVA for multiple groups. Values of the test statistic are displayed below the plot. Normally, higher values are preferred, but the most important is the p-value - if it is below 0.05, the difference in distributions is generally considered significant.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": \", _jsx(_components.em, {\n        children: \"thal\"\n      }), \" is the variable that best separates between patients with and without diameter narrowing. There is a higher incidence of reversable defect in patients with diameter narrowing (64% have reversable defect) than in those without diameter narrowing (17% have reversable defect).\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Distributions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Distributions show histograms for numeric variables, where values within a given range will be assigned a bin. For categorical data, they show bar plots, that is the frequency of each value. In the below example, there are two bins, one with values below 50.87 and one with values above 50.78.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-distributions.png\",\n      width: \"1894\",\n      height: \"1166\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-distributions.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget can be used to display differences in distributions.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": In the group of patients that are younger than 50 years, there is a 50% lower presence of diameter narrowing (70 % of patients don't have the condition).\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Sieve Diagram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Sieve diagram is a complex, yet extremely useful visualization. It shows the difference between the expected and observed variable frequency. In other words, if a combination of two values, i.e. being 60 years old and having a heart disease, is more frequent than expected, Sieve diagram will show it. Hence it is primarily intended for categorical variables - numeric variables will be discretized.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2021-12-visualizations-101/2021-12-17-sieve.png\",\n      width: \"1096\",\n      height: \"1340\",\n      src: \"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-sieve.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the scatter plot above, we observed a slightly higher incidence of diameter narrowing in patient around 60 years of age. Let us check this is Sieve diagram.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"On y axis we see \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \", with the size of each box vertically corresponding to the proportion of instances with and without condition. On x axis we see \", _jsx(_components.em, {\n        children: \"age\"\n      }), \", with the size of each box horizontally corresponding to the size of each bin. As numeric variables are discretized with equal frequency discretization, the box will always be the same size.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The color of the box corresponds to how overrepresented (blue) or underrepresented (red) a certain combination is. For example, there are 73 patients (24 %) between 55.5 and 60.5 years old in the data set. There are also 139 patients (46%) with diameter narrowing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If these two variables would be unrelated, we would expect around 11% of patients with both values (0.24 x 0.46 = 0.11), that is patients between 55 and 60 years old with a diameter narrowing.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"But if we observe the data set, we can see there are actually 15% of such patients. That is more than we would expect, so a combination of the two values could be statistically significant.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the left corner of the plot we once again meet the Chi-squared, which tells us how significant the combination actually is.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"How to read the plot\"\n      }), \": Based on the data set, it is more likely the patients between 55 and 60 years of age will have diameter narrowing. The p-value is below 0.05.\"]\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Word of caution\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data mining is about finding patterns in the data. But sometimes, patterns are random and correlation does not necessarily mean causation. So be careful when interpreting the results. Always disclose the size of your data set and how it was gathered. Report the results with the awareness, that you are only observing a sample of the population. Remember that p-values are the chance that the null hypothesis is true. Even if the p-value is 0.05, the null hypothesis could still be true - you would actually expect this to happen 5 times out of a 100, so keep this in mind, especially when you test 100+ variables!\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":599,"height":400,"src":"/blog/2021-12-visualizations-101/__optimized-images__/2021-12-17-viz.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"visualizations-101"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>addons, education, gsoc2016, interactive data visualization, orange3</strong></p><h1 class="sc-c67a4900-0 loxVcf">Visualizing Gradient Descent</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>PRIMOZGODEC</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 25, 2016</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><em>This is a guest blog from the Google Summer of Code project.</em></p>
 <p>Gradient Descent was implemented as a part of my <a href="https://developers.google.com/open-source/gsoc/">Google Summer of Code</a> project and it is available in the <a href="https://github.com/biolab/orange3-educational">Orange3-Educational</a> add-on. It simulates <a href="https://en.wikipedia.org/wiki/Gradient_descent">gradient descent</a> for either <a href="https://en.wikipedia.org/wiki/Logistic_regression">Logistic</a> or <a href="https://en.wikipedia.org/wiki/Linear_regression">Linear</a> regression, depending on the type of the input data. Gradient descent is iterative approach to optimize model parameters that minimize the cost function. In machine learning, the cost function corresponds to prediction error when the model is used on the training data set.</p>
 <p>Gradient Descent widget takes <em>data</em> on input and outputs the <em>model</em> and its <em>coefficients</em>.</p>
@@ -169,4 +169,4 @@
 <p>On the left we use the <a href="https://en.wikipedia.org/wiki/Gradient_descent">regular</a> and on the right the <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent">stochastic</a> gradient descent. While the regular descent goes straight to the target, the path of stochastic is not as smooth.</p>
 <p>We can use the widget to simulate some dangerous, unwanted behavior of gradient descent. The following screenshots show two extreme cases with too high learning rate where optimization function never converges, and a low learning rate where convergence is painfully slow.</p>
 <a href="/blog/2016-08-visualizing-gradient-descent/__optimized-images__/gradient-descent-extrems.png" data-gallery="true"><img alt="" loading="lazy" width="1950" height="717" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2016-08-visualizing-gradient-descent/__optimized-images__/gradient-descent-extrems.png"/></a>
-<p>The two problems as illustrated above are the reason that many implementations of numerical optimization use adaptive learning rates. We can simulate this in the widget by modifying the learning rate for each step of the optimization.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"PRIMOZGODEC","date":"2016-08-25 11:31:04+00:00","draft":false,"title":"Visualizing Gradient Descent","blog":["addons","education","gsoc2016","interactive data visualization","orange3"],"oldUrl":"/blog/2016/08/25/visualizing-gradient-descent/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gradient Descent was implemented as a part of my \", _jsx(_components.a, {\n        href: \"https://developers.google.com/open-source/gsoc/\",\n        children: \"Google Summer of Code\"\n      }), \" project and it is available in the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-educational\",\n        children: \"Orange3-Educational\"\n      }), \" add-on. 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In machine learning, the cost function corresponds to prediction error when the model is used on the training data set.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gradient Descent widget takes \", _jsx(_components.em, {\n        children: \"data\"\n      }), \" on input and outputs the \", _jsx(_components.em, {\n        children: \"model\"\n      }), \" and its \", _jsx(_components.em, {\n        children: \"coefficients\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-visualizing-gradient-descent/__optimized-images__/gradient-descent-flow.png\",\n      alt: \"\",\n      width: \"463\",\n      height: \"286\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget displays the value of the cost function given two parameters of the model. For linear regression, we consider feature from the training set with the parameters being the intercept and the slope. For logistic regression, the widget considers two feature and their associated multiplicative parameters, setting the intercept to zero. Screenshot bellow shows gradient descent on a \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Iris_flower_data_set\",\n        children: \"Iris data set\"\n      }), \", where we consider petal length and sepal width on the input and predict the probability that iris comes from the family of Iris versicolor.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-visualizing-gradient-descent/__optimized-images__/gradient-descent1-stamped.png\",\n      alt: \"\",\n      width: \"963\",\n      height: \"686\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"The type of the model used (either \", _jsx(_components.em, {\n          children: \"Logistic regression\"\n        }), \" or \", _jsx(_components.em, {\n          children: \"Linear regression\"\n        }), \")\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Input features (one for X and one for Y axis) and the target class\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learning rate is the step size of the gradient descent\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"In a single iteration step, stochastic approach considers only a single data instance (instead of entire training set). 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+<p>The two problems as illustrated above are the reason that many implementations of numerical optimization use adaptive learning rates. We can simulate this in the widget by modifying the learning rate for each step of the optimization.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"PRIMOZGODEC","date":"2016-08-25 11:31:04+00:00","draft":false,"title":"Visualizing Gradient Descent","blog":["addons","education","gsoc2016","interactive data visualization","orange3"],"oldUrl":"/blog/2016/08/25/visualizing-gradient-descent/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: _jsx(_components.em, {\n        children: \"This is a guest blog from the Google Summer of Code project.\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gradient Descent was implemented as a part of my \", _jsx(_components.a, {\n        href: \"https://developers.google.com/open-source/gsoc/\",\n        children: \"Google Summer of Code\"\n      }), \" project and it is available in the \", _jsx(_components.a, {\n        href: \"https://github.com/biolab/orange3-educational\",\n        children: \"Orange3-Educational\"\n      }), \" add-on. 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In machine learning, the cost function corresponds to prediction error when the model is used on the training data set.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gradient Descent widget takes \", _jsx(_components.em, {\n        children: \"data\"\n      }), \" on input and outputs the \", _jsx(_components.em, {\n        children: \"model\"\n      }), \" and its \", _jsx(_components.em, {\n        children: \"coefficients\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/blog/2016-08-visualizing-gradient-descent/__optimized-images__/gradient-descent-flow.png\",\n      alt: \"\",\n      width: \"463\",\n      height: \"286\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget displays the value of the cost function given two parameters of the model. For linear regression, we consider feature from the training set with the parameters being the intercept and the slope. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, classification, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Visualizing Misclassifications</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jul 24, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>In data mining <strong><a href="https://en.wikipedia.org/wiki/Statistical_classification">classification</a></strong> is one of the key methods for making predictions and gaining important information from our data. We would, for example, use classification for predicting which patients are likely to have the disease based on a given set of symptoms.</p>
 <p>In Orange an easy way to classify your data is to select several <strong>classification widgets</strong> (e.g. Naive Bayes, Classification Tree and Linear Regression), compare the prediction quality of each learner with <strong>Test Learners</strong> and <strong>Confusion Matrix</strong> and then use the best performing classifier on a new data set for classification. Below we use Iris data set for simplicity, but the same procedure works just as well on all kinds of data sets.</p>
 <p>Here we have three confusion matrices for <strong><a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier">Naive Bayes</a></strong> (top), <strong><a href="https://en.wikipedia.org/wiki/Decision_tree_learning">Classification Tree</a></strong> (middle) and <strong><a href="https://en.wikipedia.org/wiki/Multinomial_logistic_regression">Logistic Regression</a></strong> (bottom).</p>
@@ -158,4 +158,4 @@
 <p>We see that <strong>Classification Tree</strong> did the best with only 9 misclassified instances. To see which instances were assigned a false class, we select ‘<em>Misclassified</em>’ option in the widget, which highlights misclassifications and feeds them to the <strong>Scatter Plot</strong> widget. In the graph we thus see the entire data set presented with empty dots and the selected misclassifications with full dots.</p>
 <a href="/blog/2015-07-visualizing-misclassifications/__optimized-images__/misclassification-schema-scatterplot.png" data-gallery="true"><img alt="" loading="lazy" width="1200" height="960" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2015-07-visualizing-misclassifications/__optimized-images__/misclassification-schema-scatterplot.png"/></a>
 <p>Visualization of misclassified instances in scatter plot.</p>
-<p>Feel free to switch between learners in <strong>Confusion Matrix</strong> to see how the visualization changes for each of them.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-24 08:03:40+00:00","draft":false,"title":"Visualizing Misclassifications","blog":["analysis","classification","visualization"],"oldUrl":"/blog/2015/07/24/visualizing-misclassifications/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In data mining \", _jsx(_components.strong, {\n        children: _jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Statistical_classification\",\n          children: \"classification\"\n        })\n      }), \" is one of the key methods for making predictions and gaining important information from our data. 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+<p>Feel free to switch between learners in <strong>Confusion Matrix</strong> to see how the visualization changes for each of them.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2015-07-24 08:03:40+00:00","draft":false,"title":"Visualizing Misclassifications","blog":["analysis","classification","visualization"],"oldUrl":"/blog/2015/07/24/visualizing-misclassifications/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"In data mining \", _jsx(_components.strong, {\n        children: _jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Statistical_classification\",\n          children: \"classification\"\n        })\n      }), \" is one of the key methods for making predictions and gaining important information from our data. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Visualizing multiple variables: FreeViz</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Visualizing multiple variables: FreeViz"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2018-01-26 11:00:09+00:00"/><meta property="article:author" content="AJDA"/><meta property="article:tag" content="analysis"/><meta property="article:tag" content="features"/><meta property="article:tag" content="interactive data visualization"/><meta property="article:tag" content="visualization"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>analysis, features, interactive data visualization, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Visualizing multiple variables: FreeViz</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 26, 2018</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Scatter plots are great! But sometimes, we need to plot more than two variables to truly understand the data. How can we achieve this, knowing humans can only grasp up to three dimensions? With an optimization of linear projection, of course!</p>
 <p>Orange recently re-introduced <a href="https://www.sciencedirect.com/science/article/pii/S1532046407000275">FreeViz</a>, an interactive visualization for plotting multiple variables on a 2-D plane.</p>
 <p>Let&#x27;s load <em>zoo.tab</em> data with File widget and connect FreeViz to it. Zoo data has 16 features describing animals of different types - mammals, amphibians, insects and so on. We would like to use FreeViz to show us informative features and create a visualization that separates well between animal types.</p>
@@ -165,4 +165,4 @@
 <a href="/blog/2018-01-visualizing-multiple-variables-freeviz/__optimized-images__/Screen-Shot-2018-01-25-at-13.46.09.png" data-gallery="true"><img alt="" loading="lazy" width="702" height="194" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-01-visualizing-multiple-variables-freeviz/__optimized-images__/Screen-Shot-2018-01-25-at-13.46.09.png"/></a>
 <p>Finally, as in most Orange visualizations, we can select a subset of data points and explore them further. For example, let us observe which amphibians are characterized by being aquatic in a Data Table. A newt, a toad and two types of frogs, one venomous and one not.</p>
 <a href="/blog/2018-01-visualizing-multiple-variables-freeviz/__optimized-images__/Screen-Shot-2018-01-25-at-13.44.13.png" data-gallery="true"><img alt="" loading="lazy" width="1600" height="732" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2018-01-visualizing-multiple-variables-freeviz/__optimized-images__/Screen-Shot-2018-01-25-at-13.44.13.png"/></a>
-<p>Data exploration is always much easier with clever visualizations!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-01-26 11:00:09+00:00","draft":false,"title":"Visualizing multiple variables: FreeViz","blog":["analysis","features","interactive data visualization","visualization"],"oldUrl":"/blog/2018/01/26/visualizing-multiple-variables-freeviz/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Scatter plots are great! 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+<p>Data exploration is always much easier with clever visualizations!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2018-01-26 11:00:09+00:00","draft":false,"title":"Visualizing multiple variables: FreeViz","blog":["analysis","features","interactive data visualization","visualization"],"oldUrl":"/blog/2018/01/26/visualizing-multiple-variables-freeviz/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Scatter plots are great! 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diff --git a/blog/we-have-launched-a-new-project-on-the-topic-of-ai-in-schools/index.html b/blog/we-have-launched-a-new-project-on-the-topic-of-ai-in-schools/index.html
index 5230d98b5..306982b4f 100644
--- a/blog/we-have-launched-a-new-project-on-the-topic-of-ai-in-schools/index.html
+++ b/blog/we-have-launched-a-new-project-on-the-topic-of-ai-in-schools/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>Google, Tides Foundation, project, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">We have launched a new project on the topic of AI in schools</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Zala Gruden</strong></p><p class="sc-b9a2839b-4 ehbERY">May 12, 2022</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><a href="/blog/2022-05-Google-Tides/__optimized-images__/2022-05-12-google-tides.png" data-gallery="true"><img alt="" loading="lazy" width="2700" height="1204" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-05-Google-Tides/__optimized-images__/2022-05-12-google-tides.png"/></a>
 <p>We have successfully launched a new project about AI and data science in elementary and high schools.</p>
 <p>AI is the technology of the century, yet too few kids and education professionals know about its concepts, ideas and applications.
@@ -159,4 +159,4 @@
 <p>During the course of the project, between <strong>January 31st 2022</strong> and <strong>July 31st 2023</strong>, we expect to carry out several activities whose long term outcome is expected to improve overall education about AI in Slovenia. By the end of the project, we will have executed ten two-hour workshops, designed for primary and secondary schools to enhance the current curricula with AI and machine learning. We will also train 24 teacher ambassadors and five hundred elementary and high-school kids in the first round of workshops. In the second round of workshops, we will train two hundred teachers in the train-the-trainer workshops and over one thousand kids.
 This two-year project has further-reaching goals. We will establish working groups and communication channels to continuously support AI and machine learning training for the educators.</p>
 <a href="/blog/2022-05-Google-Tides/__optimized-images__/2022-05-12-google-tides-activities.png" data-gallery="true"><img alt="" loading="lazy" width="2700" height="1716" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2022-05-Google-Tides/__optimized-images__/2022-05-12-google-tides-activities.png"/></a>
-<p>All the activities are executed and supported by Orange. There are plenty of hands-on educational elementary and high-school activities available at our project website <a href="http://pumice.si/">pumice.si</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Zala Gruden","date":"2022-05-12","draft":false,"title":"We have launched a new project on the topic of AI in schools","thumbImage":"2022-05-12-tides-small.png","frontPageImage":"2022-05-12-google-tides.png","blog":["Google","Tides Foundation","project","visualization"],"shortExcerpt":"Find out more about our new project about AI in Schools.","longExcerpt":"Read more about the project in the blogpost and Check out the site to find out more about the prepaired activities.","oldUrl":"/blog/2022/2022-05-12-Google-Tides/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-Google-Tides/2022-05-12-google-tides.png\",\n      width: \"2700\",\n      height: \"1204\",\n      src: \"/blog/2022-05-Google-Tides/__optimized-images__/2022-05-12-google-tides.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have successfully launched a new project about AI and data science in elementary and high schools.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"AI is the technology of the century, yet too few kids and education professionals know about its concepts, ideas and applications.\\nThe project, funded by \", _jsx(_components.strong, {\n        children: \"Google.org\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"Tides Foundation\"\n      }), \", is aimed at bringing AI to teachers and kids by designing hands-on workshops by focusing on conceptual learning and applications.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"During the course of the project, between \", _jsx(_components.strong, {\n        children: \"January 31st 2022\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"July 31st 2023\"\n      }), \", we expect to carry out several activities whose long term outcome is expected to improve overall education about AI in Slovenia. 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There are plenty of hands-on educational elementary and high-school activities available at our project website <a href="http://pumice.si/">pumice.si</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Zala Gruden","date":"2022-05-12","draft":false,"title":"We have launched a new project on the topic of AI in schools","thumbImage":"2022-05-12-tides-small.png","frontPageImage":"2022-05-12-google-tides.png","blog":["Google","Tides Foundation","project","visualization"],"shortExcerpt":"Find out more about our new project about AI in Schools.","longExcerpt":"Read more about the project in the blogpost and Check out the site to find out more about the prepaired activities.","oldUrl":"/blog/2022/2022-05-12-Google-Tides/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2022-05-Google-Tides/2022-05-12-google-tides.png\",\n      width: \"2700\",\n      height: \"1204\",\n      src: \"/blog/2022-05-Google-Tides/__optimized-images__/2022-05-12-google-tides.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We have successfully launched a new project about AI and data science in elementary and high schools.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"AI is the technology of the century, yet too few kids and education professionals know about its concepts, ideas and applications.\\nThe project, funded by \", _jsx(_components.strong, {\n        children: \"Google.org\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"Tides Foundation\"\n      }), \", is aimed at bringing AI to teachers and kids by designing hands-on workshops by focusing on conceptual learning and applications.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"During the course of the project, between \", _jsx(_components.strong, {\n        children: \"January 31st 2022\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"July 31st 2023\"\n      }), \", we expect to carry out several activities whose long term outcome is expected to improve overall education about AI in Slovenia. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>machine, anthropology, big data, pivot table</strong></p><h1 class="sc-c67a4900-0 loxVcf">What is Machine Anthropology?</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Jan 29, 2020</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>For those unfamiliar with the field, cultural anthropology is the study of human cultures, practices and habits in a holistic and comparative manner. Its core method is ethnographic fieldwork, which means researchers spend a long time in the field with their subjects, live with them, talk with them, socialize with them, and observe relationships and behaviours. But recently, anthropology has begun to use also machine learning and data mining as a part of its method. The subdiscipline is called computational anthropology (combining ethnographic fieldwork <em>with</em> big data) or machine anthropology (ethnography <em>as</em> big data).</p>
 <p>Related: <a href="/blog/2018/11/06/data-mining-for-anthropologists/" target="_blank" rel="noreferrer">Data Mining for Anthropologists</a></p>
 <a href="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-big.png" data-gallery="true"><img alt="" loading="lazy" width="2160" height="849" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-big.png"/></a>
@@ -165,4 +165,4 @@
 <p>If we standardize the data with Preprocess (default normalization option), we see a more balanced picture, where homicides are relatively much more frequent early in the morning than at any other time of the day. Apparently, murderers are early birds.</p>
 <a href="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-line-plot-standardize.png" data-gallery="true"><img alt="" loading="lazy" width="851" height="505" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-line-plot-standardize.png"/></a>
 <p>There you are, a workflow for observing simple timeseries patterns in the data. Of course, you can create much more complicated workflows in Orange or even write a custom Python script. If you have you own examples of anthropological, sociological, or any kind of socially-oriented data analysis in Orange, we&#x27;d love to hear about it!</p>
-<a href="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="656" height="208" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-01-29","draft":false,"title":"What is Machine Anthropology?","thumbImage":"2020-01-29-MA-small.png","frontPageImage":"2020-01-29-MA-small.png","blog":["machine","anthropology","big data","pivot table"],"shortExcerpt":"At the recent Machine Anthropology workshop we used Orange to explore anthropological data.","longExcerpt":"At the recent Machine Anthropology workshop we used Orange to explore anthropological data.","oldUrl":"/blog/2020/2020-01-29-machine-anthropology/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"For those unfamiliar with the field, cultural anthropology is the study of human cultures, practices and habits in a holistic and comparative manner. Its core method is ethnographic fieldwork, which means researchers spend a long time in the field with their subjects, live with them, talk with them, socialize with them, and observe relationships and behaviours. But recently, anthropology has begun to use also machine learning and data mining as a part of its method. The subdiscipline is called computational anthropology (combining ethnographic fieldwork \", _jsx(_components.em, {\n        children: \"with\"\n      }), \" big data) or machine anthropology (ethnography \", _jsx(_components.em, {\n        children: \"as\"\n      }), \" big data).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/11/06/data-mining-for-anthropologists/\",\n        name: \"Data Mining for Anthropologists\"\n      })]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-MA-big.png\",\n      width: \"2160\",\n      height: \"849\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-big.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Copenhagen Center for Social Data Science (\", _jsx(_components.a, {\n        href: \"https://sodas.ku.dk/\",\n        children: \"SODAS\"\n      }), \") held a conference on machine anthropology just a few days ago. It was inspiring to see how the humanities benefit from computer sciences and vice versa! There, I presented my PhD research of sensor data from a smart building and used Orange to show how to detect patterns of behaviour in such data.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here I will show a different example by using a publicly available Philadelphia Crime data set that can be loaded in the Datasets widget. This data set reports crimes in the city of Philadelphia from 2006 to 2016. We will have to create two new features, one for hour of the day and one for month. In the Feature Constructor we will write two Python expressions that parse month and hour from the datetime feature.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-feat-const.png\",\n      width: \"598\",\n      height: \"419\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-feat-const.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"How does our data look like now?\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-data-table.png\",\n      width: \"720\",\n      height: \"263\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-data-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Perfect. Now say we would like to establish the frequency of crimes by the hour. We will use Pivot Table to count the occurrences of each type of crime. We set rows to type, because we are interested in the differences between different types of crimes. Then we set hour as columns. This will create a new timeseries-like data table, where each column will represent a single hour of the day. Values can be set to anything, because we will not use any special type of aggregation, but simply count occurrences of crimes.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-pivot-table.png\",\n      width: \"926\",\n      height: \"525\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-pivot-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can observe the results of the pivot table in a Line Plot. We group by type and see how crime frequencies change with respect to the hour of the day. Unsurprisingly, the liquor law violations and prostitution go up late at night. Prostitution in particular, is a very frequent crime in Philadelphia (or at least was in the time of reporting).\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-line-plot.png\",\n      width: \"796\",\n      height: \"463\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-line-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we standardize the data with Preprocess (default normalization option), we see a more balanced picture, where homicides are relatively much more frequent early in the morning than at any other time of the day. Apparently, murderers are early birds.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-line-plot-standardize.png\",\n      width: \"851\",\n      height: \"505\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-line-plot-standardize.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There you are, a workflow for observing simple timeseries patterns in the data. Of course, you can create much more complicated workflows in Orange or even write a custom Python script. If you have you own examples of anthropological, sociological, or any kind of socially-oriented data analysis in Orange, we'd love to hear about it!\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-workflow.png\",\n      width: \"656\",\n      height: \"208\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-workflow.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":300,"height":200,"src":"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"what-is-machine-anthropology"},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<a href="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-workflow.png" data-gallery="true"><img alt="" loading="lazy" width="656" height="208" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2020-01-29","draft":false,"title":"What is Machine Anthropology?","thumbImage":"2020-01-29-MA-small.png","frontPageImage":"2020-01-29-MA-small.png","blog":["machine","anthropology","big data","pivot table"],"shortExcerpt":"At the recent Machine Anthropology workshop we used Orange to explore anthropological data.","longExcerpt":"At the recent Machine Anthropology workshop we used Orange to explore anthropological data.","oldUrl":"/blog/2020/2020-01-29-machine-anthropology/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {LinkNew, Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  if (!LinkNew) _missingMdxReference(\"LinkNew\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"For those unfamiliar with the field, cultural anthropology is the study of human cultures, practices and habits in a holistic and comparative manner. Its core method is ethnographic fieldwork, which means researchers spend a long time in the field with their subjects, live with them, talk with them, socialize with them, and observe relationships and behaviours. But recently, anthropology has begun to use also machine learning and data mining as a part of its method. The subdiscipline is called computational anthropology (combining ethnographic fieldwork \", _jsx(_components.em, {\n        children: \"with\"\n      }), \" big data) or machine anthropology (ethnography \", _jsx(_components.em, {\n        children: \"as\"\n      }), \" big data).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Related: \", _jsx(LinkNew, {\n        url: \"/blog/2018/11/06/data-mining-for-anthropologists/\",\n        name: \"Data Mining for Anthropologists\"\n      })]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-MA-big.png\",\n      width: \"2160\",\n      height: \"849\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-big.png\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Copenhagen Center for Social Data Science (\", _jsx(_components.a, {\n        href: \"https://sodas.ku.dk/\",\n        children: \"SODAS\"\n      }), \") held a conference on machine anthropology just a few days ago. It was inspiring to see how the humanities benefit from computer sciences and vice versa! There, I presented my PhD research of sensor data from a smart building and used Orange to show how to detect patterns of behaviour in such data.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here I will show a different example by using a publicly available Philadelphia Crime data set that can be loaded in the Datasets widget. This data set reports crimes in the city of Philadelphia from 2006 to 2016. We will have to create two new features, one for hour of the day and one for month. In the Feature Constructor we will write two Python expressions that parse month and hour from the datetime feature.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-feat-const.png\",\n      width: \"598\",\n      height: \"419\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-feat-const.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"How does our data look like now?\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-data-table.png\",\n      width: \"720\",\n      height: \"263\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-data-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Perfect. Now say we would like to establish the frequency of crimes by the hour. We will use Pivot Table to count the occurrences of each type of crime. We set rows to type, because we are interested in the differences between different types of crimes. Then we set hour as columns. This will create a new timeseries-like data table, where each column will represent a single hour of the day. Values can be set to anything, because we will not use any special type of aggregation, but simply count occurrences of crimes.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-pivot-table.png\",\n      width: \"926\",\n      height: \"525\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-pivot-table.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can observe the results of the pivot table in a Line Plot. We group by type and see how crime frequencies change with respect to the hour of the day. Unsurprisingly, the liquor law violations and prostitution go up late at night. Prostitution in particular, is a very frequent crime in Philadelphia (or at least was in the time of reporting).\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-line-plot.png\",\n      width: \"796\",\n      height: \"463\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-line-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we standardize the data with Preprocess (default normalization option), we see a more balanced picture, where homicides are relatively much more frequent early in the morning than at any other time of the day. Apparently, murderers are early birds.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-line-plot-standardize.png\",\n      width: \"851\",\n      height: \"505\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-line-plot-standardize.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There you are, a workflow for observing simple timeseries patterns in the data. Of course, you can create much more complicated workflows in Orange or even write a custom Python script. If you have you own examples of anthropological, sociological, or any kind of socially-oriented data analysis in Orange, we'd love to hear about it!\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/blog/2020-01-machine-anthropology/2020-01-29-workflow.png\",\n      width: \"656\",\n      height: \"208\",\n      src: \"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-workflow.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":{"width":300,"height":200,"src":"/blog/2020-01-machine-anthropology/__optimized-images__/2020-01-29-MA-small.png"}},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"what-is-machine-anthropology"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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 <p><a href="https://www.linkedin.com/in/saso-jakljevic-90252095/">Sašo Jakljevič</a>, a former student of the Faculty of Computer and Information Science at University of Ljubljana, created the following motivational videos for his graduation thesis. He used two belowed datasets, iris and zoo, to showcase how to tackle real-life problems with Orange.</p>
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+<div class="sc-8f0614a2-2 jRBPMh"><iframe width="853" height="480" src="https://www.youtube.com/embed/hGdnx4tiDHI" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" title="Embedded youtube"></iframe></div></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-03-09 10:54:13+00:00","draft":false,"title":"Why Orange?","blog":["data","examples","youtube"],"oldUrl":"/blog/2017/03/09/why-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Why is Orange so great? Because it helps people solve problems quickly and efficiently.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://www.linkedin.com/in/saso-jakljevic-90252095/\",\n        children: \"Sašo Jakljevič\"\n      }), \", a former student of the Faculty of Computer and Information Science at University of Ljubljana, created the following motivational videos for his graduation thesis. He used two belowed datasets, iris and zoo, to showcase how to tackle real-life problems with Orange.\"]\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"1_vek6FkFeM\"\n    }), \"\\n\", _jsx(YouTube, {\n      embedId: \"hGdnx4tiDHI\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"thumbImage":null},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"why-orange"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Why Removing Features Isn&#x27;t Enough</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Why Removing Features Isn&#x27;t Enough"/><meta property="og:description" content="Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-09-19 05:00:00+00:00"/><meta property="article:author" content="Žan Mervič"/><meta property="article:tag" content="fairness"/><meta property="og:image" content="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-thumb.png"/><meta property="og:image:width" content="607"/><meta property="og:image:height" content="542"/><meta name="next-head-count" content="17"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>fairness</strong></p><h1 class="sc-c67a4900-0 loxVcf">Why Removing Features Isn&#x27;t Enough</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Žan Mervič</strong></p><p class="sc-b9a2839b-4 ehbERY">Sep 19, 2023</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Previously, we introduced and explained different fairness algorithms that can be used to mitigate bias in a dataset or model predictions. Here, we will discuss a common misconception: removing the protected attribute from the dataset will remove bias. We show why this is not the case and why it is essential to use fairness algorithms.</p>
 <h2>Hiding Protected Attribute:</h2>
 <p>Our setup is the following: we have two workflows, and both are using the adult data set. In the first workflow, we will train a logistic regression model using Reweighing as a preprocessor and a regular logistic regression model as a baseline on data that has not been modified. The second workflow uses the same dataset but with the protected attribute removed. We will then compare the predictions of the two workflows using a Box Plot.</p>
@@ -173,4 +173,4 @@ <h2>Why and how?</h2>
 <a href="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex.png" data-gallery="true"><img alt="" loading="lazy" width="547" height="248" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex.png"/></a>
 <p>In this workflow, we set the &quot;sex&quot; attribute as the target variable and use a Logistic Regression model to try and predict it.</p>
 <a href="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex-scores.png" data-gallery="true"><img alt="" loading="lazy" width="521" height="217" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex-scores.png"/></a>
-<p>We can see that the model has a very high accuracy score, which means that the other features in the dataset are correlated with the protected attribute. This means that even if we remove the protected attribute from the dataset, the model can still infer it from the other features. This is why it is crucial to use fairness algorithms instead of simply removing the protected attribute.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 05:00:00+00:00","draft":false,"title":"Why Removing Features Isn't Enough","thumbImage":"2023-09-19-fairness-hiding-protected-attribute-thumb.png","frontPageImage":"2023-09-19-fairness-hiding-protected-attribute-thumb.png","blog":["fairness"],"shortExcerpt":"Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation.","longExcerpt":"In this blog, we confront the common misconception that merely removing a protected attribute from a dataset eliminates bias in model predictions. Our case study reveals that models trained without these attributes still produce biased results. This is due to feature correlations that indirectly capture the protected information. Our conclusion? You cannot sidestep the need for specialized fairness algorithms.","oldUrl":"/blog/2023/2023-09-19-fairness-hiding-protected-attribute/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Previously, we introduced and explained different fairness algorithms that can be used to mitigate bias in a dataset or model predictions. Here, we will discuss a common misconception: removing the protected attribute from the dataset will remove bias. We show why this is not the case and why it is essential to use fairness algorithms.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Hiding Protected Attribute:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our setup is the following: we have two workflows, and both are using the adult data set. In the first workflow, we will train a logistic regression model using Reweighing as a preprocessor and a regular logistic regression model as a baseline on data that has not been modified. The second workflow uses the same dataset but with the protected attribute removed. 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The first Select Columns widget is used to remove the protected attribute from the data, and the second one is used to add it back after the predictions are made so that we can compare the predictions of the two workflows using a Box Plot widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this example, we will not be able to use the scores to compare the fairness metrics of the models because we could not calculate the fairness metrics for the workflow with the hidden protected attribute. This is because the protected attribute is required to calculate the fairness metrics. Saying that we can still compare the accuracy scores of the models. Here are the scores for the reweighted model, the baseline model, and the model learning on altered data:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-scores.png\",\n      width: \"815\",\n      height: \"196\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The baseline model and the model learning on altered data have very similar accuracy scores, while the reweighted model has a slight dip in accuracy.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead of using the scores to compare the models, we will visualize the fairness Disparate Impact and Statistical Parity Difference fairness metrics using the Box Plot widget, just like we did in many previous blogs. 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The ratio of favorable prediction is much more similar when using debiasing compared to the baseline model and the model learning on data with the protected attribute removed.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A similar observation can be made for the Equalized Odds Difference and Average Odds Difference fairness metrics. Using a similar setup as in the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-equal-odds-postprocessing/\",\n        children: \"previous blog post\"\n      }), \", we visualized the True Positive Rate for each group using the mosaic display widget. We trained one model using the Equalized Odds Postprocessing widget and one on data with the protected attribute \\\"age\\\" hidden. Here are the results:\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-mosaic.png\",\n      width: \"800\",\n      height: \"378\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-mosaic.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the visualizations, the red part of each column represents the true positive rate for each group; you can ignore the width of the columns as that represents the number of instances in each group, which is irrelevant to us. We can see that the difference in True Positive Rate between the two groups is much smaller when using the Equal Odds Postprocessing widget.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why and how?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we removed the protected attribute from the dataset on which we train and test the model, how is it that the predictions are still biased towards one of the groups?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The answer is that features in a dataset are often correlated. This means that even if we remove the protected attribute, other features can still be used to infer the protected attribute. For example, suppose the protected attribute is race, and we have a feature like zip code in the data. Since certain races might be predominant in particular zip codes, the model may still indirectly learn the bias. We can test this correlation by predicting the protected attribute from the other features. If we can predict the protected attribute with high accuracy, we know that the other features are correlated with the protected attribute. Here is an example of such a workflow:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-sex.png\",\n      width: \"547\",\n      height: \"248\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this workflow, we set the \\\"sex\\\" attribute as the target variable and use a Logistic Regression model to try and predict it.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-sex-scores.png\",\n      width: \"521\",\n      height: \"217\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that the model has a very high accuracy score, which means that the other features in the dataset are correlated with the protected attribute. This means that even if we remove the protected attribute from the dataset, the model can still infer it from the other features. 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\ No newline at end of file
+<p>We can see that the model has a very high accuracy score, which means that the other features in the dataset are correlated with the protected attribute. This means that even if we remove the protected attribute from the dataset, the model can still infer it from the other features. This is why it is crucial to use fairness algorithms instead of simply removing the protected attribute.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Žan Mervič","date":"2023-09-19 05:00:00+00:00","draft":false,"title":"Why Removing Features Isn't Enough","thumbImage":"2023-09-19-fairness-hiding-protected-attribute-thumb.png","frontPageImage":"2023-09-19-fairness-hiding-protected-attribute-thumb.png","blog":["fairness"],"shortExcerpt":"Find out why merely removing protected attributes will not fix bias. Features often correlate, letting models infer biases. Fairness algorithms are key for genuine bias mitigation.","longExcerpt":"In this blog, we confront the common misconception that merely removing a protected attribute from a dataset eliminates bias in model predictions. Our case study reveals that models trained without these attributes still produce biased results. This is due to feature correlations that indirectly capture the protected information. Our conclusion? You cannot sidestep the need for specialized fairness algorithms.","oldUrl":"/blog/2023/2023-09-19-fairness-hiding-protected-attribute/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Previously, we introduced and explained different fairness algorithms that can be used to mitigate bias in a dataset or model predictions. Here, we will discuss a common misconception: removing the protected attribute from the dataset will remove bias. We show why this is not the case and why it is essential to use fairness algorithms.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Hiding Protected Attribute:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Our setup is the following: we have two workflows, and both are using the adult data set. In the first workflow, we will train a logistic regression model using Reweighing as a preprocessor and a regular logistic regression model as a baseline on data that has not been modified. The second workflow uses the same dataset but with the protected attribute removed. We will then compare the predictions of the two workflows using a Box Plot.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-use-case-1.png\",\n      width: \"968\",\n      height: \"412\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-use-case-1.png\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-use-case-2.png\",\n      width: \"957\",\n      height: \"301\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-use-case-2.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The two workflows are very similar, the only difference being the extra learner used in the first workflow, the reweighted learner, and two Select Columns widgets used in the second workflow. The first Select Columns widget is used to remove the protected attribute from the data, and the second one is used to add it back after the predictions are made so that we can compare the predictions of the two workflows using a Box Plot widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this example, we will not be able to use the scores to compare the fairness metrics of the models because we could not calculate the fairness metrics for the workflow with the hidden protected attribute. This is because the protected attribute is required to calculate the fairness metrics. Saying that we can still compare the accuracy scores of the models. Here are the scores for the reweighted model, the baseline model, and the model learning on altered data:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-scores.png\",\n      width: \"815\",\n      height: \"196\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The baseline model and the model learning on altered data have very similar accuracy scores, while the reweighted model has a slight dip in accuracy.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Instead of using the scores to compare the models, we will visualize the fairness Disparate Impact and Statistical Parity Difference fairness metrics using the Box Plot widget, just like we did in many previous blogs. Here we will show three different box plots, the first one will be from the reweighted model, the second one from the baseline, and the third from the model learning on data with the protected attribute removed:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-box-plot.png\",\n      width: \"796\",\n      height: \"693\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-box-plot.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The box plots show that the baseline model results are very similar to the model learning on data with the protected attribute removed. In contrast, the reweighted model results are very different. The ratio of favorable prediction is much more similar when using debiasing compared to the baseline model and the model learning on data with the protected attribute removed.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A similar observation can be made for the Equalized Odds Difference and Average Odds Difference fairness metrics. Using a similar setup as in the \", _jsx(_components.a, {\n        href: \"/blog/2023-09-19-fairness-equal-odds-postprocessing/\",\n        children: \"previous blog post\"\n      }), \", we visualized the True Positive Rate for each group using the mosaic display widget. We trained one model using the Equalized Odds Postprocessing widget and one on data with the protected attribute \\\"age\\\" hidden. Here are the results:\"]\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-mosaic.png\",\n      width: \"800\",\n      height: \"378\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-mosaic.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the visualizations, the red part of each column represents the true positive rate for each group; you can ignore the width of the columns as that represents the number of instances in each group, which is irrelevant to us. We can see that the difference in True Positive Rate between the two groups is much smaller when using the Equal Odds Postprocessing widget.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Why and how?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If we removed the protected attribute from the dataset on which we train and test the model, how is it that the predictions are still biased towards one of the groups?\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The answer is that features in a dataset are often correlated. This means that even if we remove the protected attribute, other features can still be used to infer the protected attribute. For example, suppose the protected attribute is race, and we have a feature like zip code in the data. Since certain races might be predominant in particular zip codes, the model may still indirectly learn the bias. 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Here is an example of such a workflow:\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-sex.png\",\n      width: \"547\",\n      height: \"248\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this workflow, we set the \\\"sex\\\" attribute as the target variable and use a Logistic Regression model to try and predict it.\"\n    }), \"\\n\", _jsx(WindowScreenshot, {\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/2023-09-19-fairness-hiding-protected-attribute-sex-scores.png\",\n      width: \"521\",\n      height: \"217\",\n      src: \"/blog/2023-09-fairness-hiding-protected-attribute/__optimized-images__/2023-09-19-fairness-hiding-protected-attribute-sex-scores.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that the model has a very high accuracy score, which means that the other features in the dataset are correlated with the protected attribute. 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\ No newline at end of file
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - Why You Should Use Apply Domain</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - Why You Should Use Apply Domain"/><meta property="og:description" content="Apply Domain is a mystery widget with an amazing functionality."/><meta property="og:type" content="article"/><meta property="article:published_time" content="2021-08-13"/><meta property="article:author" content="Ajda Pretnar"/><meta property="article:tag" content="domain"/><meta property="article:tag" content="PCA"/><meta property="article:tag" content="transformation"/><meta property="article:tag" content="apply domain"/><meta property="og:image" content="/blog/2021-08-apply-domain/__optimized-images__/2021-08-13-applydomain.png"/><meta property="og:image:width" content="600"/><meta property="og:image:height" content="399"/><meta name="next-head-count" content="20"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-f211bd4808f46605.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>domain, PCA, transformation, apply domain</strong></p><h1 class="sc-c67a4900-0 loxVcf">Why You Should Use Apply Domain</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>Ajda Pretnar</strong></p><p class="sc-b9a2839b-4 ehbERY">Aug 13, 2021</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>It can happen you&#x27;d see a widget in Orange and think: &quot;What on Earth does this even do?&quot; We admit, finding informative widget names is not always easy and Apply Domain had a least 5 different names so far. While it might not be clear what the widget does from its name, the actual functionality is one of the nicer ones Orange has to offer.</p>
 <p>Say you are transforming your data with PCA. There&#x27;s training data and test data (say you expect to get new data at some later point, so we are simulating the split here). For this example, we&#x27;ll be using <em>Wine</em> data from Datasets widget.</p>
 <p>Transforming the data with PCA is straightforward. Apply the PCA, select a number of components that cover a solid amount of variance and output the transformed data. We can observe the 2-dimensional PCA plot in Scatter Plot.</p>
@@ -164,4 +164,4 @@
 <p>Apply Domain outputs transformed data, which can be once again merged with Concatenate (keep the same settings as before). Now, observe the results in a scatter plot.</p>
 <a href="/blog/2021-08-apply-domain/__optimized-images__/2021-08-13-workflow2.png" data-gallery="true"><img alt="" loading="lazy" width="1570" height="476" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-08-apply-domain/__optimized-images__/2021-08-13-workflow2.png"/></a>
 <a href="/blog/2021-08-apply-domain/__optimized-images__/2021-08-13-scatterplot3.png" data-gallery="true"><img alt="" loading="lazy" width="1093" height="801" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2021-08-apply-domain/__optimized-images__/2021-08-13-scatterplot3.png"/></a>
-<p>Well, look at that! The data is properly transformed and can be nicely discriminated by wine type in the PCA space!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-08-13","draft":false,"title":"Why You Should Use Apply Domain","thumbImage":"2021-08-13-applydomain.png","frontPageImage":"2021-08-13-applydomain.png","blog":["domain","PCA","transformation","apply domain"],"shortExcerpt":"Apply Domain is a mystery widget with an amazing functionality.","longExcerpt":"What does Apply Domain even do? Actually, it is an extremely useful widget for all your data transformation problems!","x2images":true,"oldUrl":"/blog/2021/2021-08-13-apply-domain/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It can happen you'd see a widget in Orange and think: \\\"What on Earth does this even do?\\\" We admit, finding informative widget names is not always easy and Apply Domain had a least 5 different names so far. 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This is because the test data and the train data were not transformed to the same PCA space.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Enter Apply Domain. The widget uses template data to transform new data to the same domain (essentially the same data space). Connect \", _jsx(_components.em, {\n        children: \"Transformed Data\"\n      }), \" to Apply Domain as \", _jsx(_components.em, {\n        children: \"Template Data\"\n      }), \". And \", _jsx(_components.em, {\n        children: \"Remaining Data\"\n      }), \" from Data Sampler as \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Apply Domain outputs transformed data, which can be once again merged with Concatenate (keep the same settings as before). 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+<p>Well, look at that! The data is properly transformed and can be nicely discriminated by wine type in the PCA space!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Ajda Pretnar","date":"2021-08-13","draft":false,"title":"Why You Should Use Apply Domain","thumbImage":"2021-08-13-applydomain.png","frontPageImage":"2021-08-13-applydomain.png","blog":["domain","PCA","transformation","apply domain"],"shortExcerpt":"Apply Domain is a mystery widget with an amazing functionality.","longExcerpt":"What does Apply Domain even do? Actually, it is an extremely useful widget for all your data transformation problems!","x2images":true,"oldUrl":"/blog/2021/2021-08-13-apply-domain/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"It can happen you'd see a widget in Orange and think: \\\"What on Earth does this even do?\\\" We admit, finding informative widget names is not always easy and Apply Domain had a least 5 different names so far. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>orange3, sql, visualization</strong></p><h1 class="sc-c67a4900-0 loxVcf">Working with SQL data in Orange 3</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>LAN</strong></p><p class="sc-b9a2839b-4 ehbERY">May 05, 2015</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p><a href="http://orange.biolab.si/orange3/">Orange 3</a> is slowly, but steadily, gaining support for working with data stored in a SQL database. The main focus is to allow huge data sets that do not fit into RAM to be analyzed and visualized efficiently. Many widgets already recognize the type of input data and perform the necessary computations intelligently. This means that data is not downloaded from the database and analyzed locally, but is retained on the remote server, with the computation tasks translated into SQL queries and offloaded to the database engine. This approach takes advantage of the state-of-the-art optimizations relational databases have for working with data that does not fit into working memory, as well as minimizes the transfer of required information to the client.</p>
 <p>We demonstrate how to explore and visualize data stored in a SQL table on a remote server in the following short video. It shows how to connect to the server and load the data with the SqlTable widget, manipulate the data (Select Columns, Select Rows), obtain the summary statistics (Box plot, Distributions), and visualize the data (Heat map, Mosaic Display).</p>
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-<p><em>The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 318633</em></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"LAN","date":"2015-05-05 07:20:47+00:00","draft":false,"title":"Working with SQL data in Orange 3","blog":["orange3","sql","visualization"],"oldUrl":"/blog/2015/05/05/working-with-sql-data-in-orange-3/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://orange.biolab.si/orange3/\",\n        children: \"Orange 3\"\n      }), \" is slowly, but steadily, gaining support for working with data stored in a SQL database. 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+<p><em>The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 318633</em></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"LAN","date":"2015-05-05 07:20:47+00:00","draft":false,"title":"Working with SQL data in Orange 3","blog":["orange3","sql","visualization"],"oldUrl":"/blog/2015/05/05/working-with-sql-data-in-orange-3/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components), {YouTube} = _components;\n  if (!YouTube) _missingMdxReference(\"YouTube\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://orange.biolab.si/orange3/\",\n        children: \"Orange 3\"\n      }), \" is slowly, but steadily, gaining support for working with data stored in a SQL database. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>infraorange, infrared spectra, interactive data visualization, orange3, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Workshop on InfraOrange</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Mar 08, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Thanks to the collaboration with synchrotrons <a href="https://www.elettra.trieste.it/">Elettra</a> (Trieste) and <a href="https://www.synchrotron-soleil.fr/">Soleil</a> (Paris), Orange is getting an add-on <a href="https://github.com/markotoplak/orange-infrared">InfraOrange</a>, with widgets for analysis of infrared spectra. Its primary users obviously come from these two institutions, hence we organized the first workshop for InfraOrange at one of them.</p>
 <p>Some 20 participants spent the first day of the workshop in Trieste learning the basics of Orange and its use for data mining. With Janez at the helm and Marko assisting in the back, we traversed the standard list of visual and statistical techniques and a bit of unsupervised and supervised learning. The workshop was perhaps a bit unusual as most attendees were already quite familiar with these methods, but most haven&#x27;t yet used them in such an interactive fashion.</p>
 <a href="/blog/2017-03-workshop-on-infraorange/__optimized-images__/Curves2.jpg" data-gallery="true"><img alt="" loading="lazy" width="3467" height="2533" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-03-workshop-on-infraorange/__optimized-images__/Curves2.jpg"/></a>
@@ -160,4 +160,4 @@
 <a href="/blog/2017-03-workshop-on-infraorange/__optimized-images__/Group_photo.jpg" data-gallery="true"><img alt="" loading="lazy" width="4608" height="3456" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-03-workshop-on-infraorange/__optimized-images__/Group_photo.jpg"/></a>
 <p>Group photo!</p>
 <p>We now have a lot of realistic feedback on what to improve. There is a lot of work to do still, but a week after the workshop the most often occurring bugs have already been fixed.</p>
-<p>The future of InfraOrange looks bright and.... khm... well, colorful! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-03-08 14:51:43+00:00","draft":false,"title":"Workshop on InfraOrange","blog":["infraorange","infrared spectra","interactive data visualization","orange3","workshop"],"oldUrl":"/blog/2017/03/08/workshop-on-infraorange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Thanks to the collaboration with synchrotrons \", _jsx(_components.a, {\n        href: \"https://www.elettra.trieste.it/\",\n        children: \"Elettra\"\n      }), \" (Trieste) and \", _jsx(_components.a, {\n        href: \"https://www.synchrotron-soleil.fr/\",\n        children: \"Soleil\"\n      }), \" (Paris), Orange is getting an add-on \", _jsx(_components.a, {\n        href: \"https://github.com/markotoplak/orange-infrared\",\n        children: \"InfraOrange\"\n      }), \", with widgets for analysis of infrared spectra. 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+<p>The future of InfraOrange looks bright and.... khm... well, colorful! :)</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-03-08 14:51:43+00:00","draft":false,"title":"Workshop on InfraOrange","blog":["infraorange","infrared spectra","interactive data visualization","orange3","workshop"],"oldUrl":"/blog/2017/03/08/workshop-on-infraorange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"Thanks to the collaboration with synchrotrons \", _jsx(_components.a, {\n        href: \"https://www.elettra.trieste.it/\",\n        children: \"Elettra\"\n      }), \" (Trieste) and \", _jsx(_components.a, {\n        href: \"https://www.synchrotron-soleil.fr/\",\n        children: \"Soleil\"\n      }), \" (Paris), Orange is getting an add-on \", _jsx(_components.a, {\n        href: \"https://github.com/markotoplak/orange-infrared\",\n        children: \"InfraOrange\"\n      }), \", with widgets for analysis of infrared spectra. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>education, text mining, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Workshop: Text Analysis for Social Scientists</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>AJDA</strong></p><p class="sc-b9a2839b-4 ehbERY">Jun 09, 2017</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Yesterday was no ordinary day at the Faculty of Computer and Information Science, University of Ljubljana - there was an unusually high proportion of Social Sciences students, researchers and other professionals in our classrooms. It was all because of a Text Analysis for Social Scientists workshop.</p>
 <p><strong>Related:</strong> <a href="/blog/2016/11/30/data-mining-for-political-scientists/">Data Mining for Political Scientists</a></p>
 <p>Text mining is becoming a popular method across sciences and it was time to showcase what it (and Orange) can do. In this 5-hour hands-on workshop we explained text preprocessing, clustering, and predictive models, and applied them in the analysis of selected Grimm&#x27;s Tales. We discovered that predictive models can nicely distinguish between animal tales and tales of magic and that foxes and kings play a particularly important role in separating between the two types.</p>
@@ -161,4 +161,4 @@
 <p>Five hours was very little time to cover all the interesting topics in text analytics. But Orange came to the rescue once again. Interactive visualization and the possibility of close reading in Corpus Viewer were such a great help! Instead of reading 6400 tweets &#x27;by hand&#x27;, now the workshop participants can cluster them in interesting groups, find important words in each cluster and plot them in a 2D visualization.</p>
 <p><a href="/blog/2017-06-workshop-text-analysis-for-social-scientists/__optimized-images__/IMG_0969.jpg" data-gallery="true"><img alt="" loading="lazy" width="1600" height="1067" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2017-06-workshop-text-analysis-for-social-scientists/__optimized-images__/IMG_0969.jpg"/></a>
 Participants at work.</p>
-<p>Here, we&#x27;d like to thank <a href="https://www.numfocus.org/">NumFocus</a> for providing financial support for the course. This enabled us to bring in students from a wide variety of fields (linguists, geographers, marketers) and prove (once again) that you don&#x27;t have to be a computer scientists to do machine learning!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-06-09 13:04:32+00:00","draft":false,"title":"Workshop: Text Analysis for Social Scientists","blog":["education","text mining","workshop"],"oldUrl":"/blog/2017/06/09/workshop-text-analysis-for-social-scientists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Yesterday was no ordinary day at the Faculty of Computer and Information Science, University of Ljubljana - there was an unusually high proportion of Social Sciences students, researchers and other professionals in our classrooms. 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+<p>Here, we&#x27;d like to thank <a href="https://www.numfocus.org/">NumFocus</a> for providing financial support for the course. This enabled us to bring in students from a wide variety of fields (linguists, geographers, marketers) and prove (once again) that you don&#x27;t have to be a computer scientists to do machine learning!</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"AJDA","date":"2017-06-09 13:04:32+00:00","draft":false,"title":"Workshop: Text Analysis for Social Scientists","blog":["education","text mining","workshop"],"oldUrl":"/blog/2017/06/09/workshop-text-analysis-for-social-scientists/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    strong: \"strong\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Yesterday was no ordinary day at the Faculty of Computer and Information Science, University of Ljubljana - there was an unusually high proportion of Social Sciences students, researchers and other professionals in our classrooms. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-b9a2839b-0 cyZFCR"><div class="sc-61db3aea-0 CVQuR"><div class="sc-b9a2839b-1 cIBHvM"><p class="sc-b9a2839b-4 cdlBoX"><strong>bioinformatics, workshop</strong></p><h1 class="sc-c67a4900-0 loxVcf">Workshops at Baylor College of Medicine</h1></div><div class="sc-b9a2839b-2 kBOXgB"><div class="sc-b9a2839b-3 fMPIzd"><p class="sc-b9a2839b-4 ehbERY"><strong>BIOLAB</strong></p><p class="sc-b9a2839b-4 ehbERY">May 26, 2014</p></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>On May 22nd and May 23rd, we (<a href="http://www.biolab.si/en/blaz-zupan/default.html">Blaz Zupan</a> and <a href="http://www.fri.uni-lj.si/en/janez-demsar">Janez Demsar</a>, assisted by <a href="http://www.fri.uni-lj.si/en/marinka-zitnik/default.html">Marinka Zitnik</a> and Balaji Santhanam) have given two hands-on workshops called <a href="https://www.bcm.edu/research/centers/computational-and-integrative-biomedical-research/index.cfm?pmid=25960">Data Mining without Programming</a> at <a href="https://www.bcm.edu/">Baylor College of Medicine</a> in Houston, Texas.</p>
 <p>Actually, there was a lot of programming, but no Python or alike. The workshop was designed for biomedical students and Baylor&#x27;s faculty members. We have presented a visual programming approach for development of data mining workflows for interactive data exploration. A three-hour workshop consisted of <a href="http://goo.gl/vNsk54">15 data mining lessons</a> on visual data exploration, classification, clustering, network analysis, and gene expression analytics. Each lesson focused on a particular data analysis task that the attendees solved with Orange.</p>
 <p>The two workshops were organized by <a href="https://www.bcm.edu/research/centers/computational-and-integrative-biomedical-research">Baylor&#x27;s Computational and Integrative Biomedical Research Center</a>. Over two days, the event was attended by a large audience of 120 attendees.</p>
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@@ -156,4 +156,4 @@
 <li>from the Orange Canvas GUI. Select  &quot;Options / Add-ons...&quot; in the menu.</li>
 </ul>
 <p>Everything should just work. Writing add-ons is as easy as writing your own <a href="http://docs.biolab.si/orange/2/extend-widgets/rst/">Orange Widgets</a> or <a href="http://docs.biolab.si/orange/2/tutorial/rst/">Orange Scripts</a>. Just follow <a href="http://orange.biolab.si/trac/wiki/AddOns">this tutorial</a> and you will have your brand-new Orange add-on on PyPi in no time (an hour at most).</p>
-<a href="/blog/2013-01-writing-orange-add-ons/__optimized-images__/orange-add-ons.png__400x382_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="400" height="382" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-01-writing-orange-add-ons/__optimized-images__/orange-add-ons.png__400x382_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-01-29 09:00:00+00:00","draft":false,"title":"Writing Orange Add-ons","blog":["addons","pypi"],"oldUrl":"/blog/2013/01/29/writing-orange-add-ons/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We officially supported add-ons in Orange 2.6. 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+<a href="/blog/2013-01-writing-orange-add-ons/__optimized-images__/orange-add-ons.png__400x382_q95_crop_upscale.png" data-gallery="true"><img alt="" loading="lazy" width="400" height="382" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2013-01-writing-orange-add-ons/__optimized-images__/orange-add-ons.png__400x382_q95_crop_upscale.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"BIOLAB","date":"2013-01-29 09:00:00+00:00","draft":false,"title":"Writing Orange Add-ons","blog":["addons","pypi"],"oldUrl":"/blog/2013/01/29/writing-orange-add-ons/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    ul: \"ul\",\n    li: \"li\",\n    code: \"code\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsxs(_components.p, {\n      children: [\"We officially supported add-ons in Orange 2.6. 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@@ -166,4 +166,4 @@ <h2>Is this it?</h2>
 <p>No, this is obviously not it, yet. Having a separate release for each language is not the best practice. Add-ons cannot be downloaded separately, We don&#x27;t have anything for Linux. But it&#x27;s a huge step forward.</p>
 <h2>Orange for other languages?</h2>
 <p>Sure. With the initial work completed, translating Orange to other languages should be significantly easier. Although we have not yet prepared general instructions, if you are genuinely interested in translating to another language, we would be happy to assist you.</p>
-<a href="/blog/2023-03-zivjo-orange/__optimized-images__/2023-03-31-orange-slo.png" data-gallery="true"><img alt="" loading="lazy" width="2782" height="1790" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-03-zivjo-orange/__optimized-images__/2023-03-31-orange-slo.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2023-04-24","draft":false,"title":"Živjo, Orange!","thumbImage":"2023-03-31-zemljevid.png","frontPageImage":"2023-03-31-zemljevid.png","blog":["gui"],"shortExcerpt":"Orange speaks Slovenian!","longExcerpt":"Orange speaks Slovenian!","oldUrl":"/blog/2023/2021-03-31-zivjo-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-zivjo-orange/2023-03-31-zemljevid.png\",\n      width: \"1416\",\n      height: \"776\",\n      src: \"/blog/2023-03-zivjo-orange/__optimized-images__/2023-03-31-zemljevid.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Slovenian language exhibits a complicated grammar, featuring three genders and four plural terms, along with a substantial amount of declensions and conjugations, (and unreasonable number of exceptions) that require all kinds of agreements between nouns, adjectives and verbs. 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+<a href="/blog/2023-03-zivjo-orange/__optimized-images__/2023-03-31-orange-slo.png" data-gallery="true"><img alt="" loading="lazy" width="2782" height="1790" decoding="async" data-nimg="1" class="sc-8f0614a2-1 cHIhER" style="color:transparent" src="/blog/2023-03-zivjo-orange/__optimized-images__/2023-03-31-orange-slo.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"frontmatter":{"author":"Janez Demšar","date":"2023-04-24","draft":false,"title":"Živjo, Orange!","thumbImage":"2023-03-31-zemljevid.png","frontPageImage":"2023-03-31-zemljevid.png","blog":["gui"],"shortExcerpt":"Orange speaks Slovenian!","longExcerpt":"Orange speaks Slovenian!","oldUrl":"/blog/2023/2021-03-31-zivjo-orange/"},"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {WindowScreenshot} = _components;\n  if (!WindowScreenshot) _missingMdxReference(\"WindowScreenshot\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(WindowScreenshot, {\n      src: \"/blog/2023-03-zivjo-orange/2023-03-31-zemljevid.png\",\n      width: \"1416\",\n      height: \"776\",\n      src: \"/blog/2023-03-zivjo-orange/__optimized-images__/2023-03-31-zemljevid.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The Slovenian language exhibits a complicated grammar, featuring three genders and four plural terms, along with a substantial amount of declensions and conjugations, (and unreasonable number of exceptions) that require all kinds of agreements between nouns, adjectives and verbs. 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@@ -155,4 +155,4 @@
 }
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+Orange is an open source project. 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+<a href="http://docs.biolab.si/2/">Orange 2.7 documentation</a></p></div></div></div></div><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>For a list of frequently asked questions, see <a href="/faq/">FAQ</a>. Also, feel free to reach out to us in our <a href="https://discord.gg/FWrfeXV">Discord chatroom</a>.</p></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Visual Programming","mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [_jsx(_components.a, {\n      href: \"/getting-started\",\n      children: \"Getting started\"\n    }), \"\\n\", _jsx(_components.a, {\n      href: \"https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g\",\n      children: \"YouTube tutorials\"\n    }), \"\\n\", _jsx(_components.a, {\n      href: \"https://orange3.readthedocs.io/projects/orange-visual-programming/en/latest/loading-your-data/index.html\",\n      children: \"Loading your data\"\n    }), \"\\n\", _jsx(_components.a, {\n      href: \"/widget-catalog\",\n      children: \"Widget catalog\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? 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Just extract the archive and open the shortcut in the extracted folder.</p></div><div><h2 id="mac">macOS</h2><h3>Orange for Apple silicon</h3><a class="sc-9e86dd37-6 iUtQwu" href="https://download.biolab.si/download/files/Orange3-3.36.1-Python3.9.12-arm64.dmg"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M505.7 661a8 8 0 0 0 12.6 0l112-141.7c4.1-5.2.4-12.9-6.3-12.9h-74.1V168c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v338.3H400c-6.7 0-10.4 7.7-6.3 12.9l112 141.8zM878 626h-60c-4.4 0-8 3.6-8 8v154H214V634c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v198c0 17.7 14.3 32 32 32h684c17.7 0 32-14.3 32-32V634c0-4.4-3.6-8-8-8z"></path></svg>Orange3-3.36.1-Python3.9.12-arm64.dmg</a><h3>Orange for Intel</h3><a class="sc-9e86dd37-6 iUtQwu" href="https://download.biolab.si/download/files/Orange3-3.36.1-Python3.9.12.dmg"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M505.7 661a8 8 0 0 0 12.6 0l112-141.7c4.1-5.2.4-12.9-6.3-12.9h-74.1V168c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v338.3H400c-6.7 0-10.4 7.7-6.3 12.9l112 141.8zM878 626h-60c-4.4 0-8 3.6-8 8v154H214V634c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v198c0 17.7 14.3 32 32 32h684c17.7 0 32-14.3 32-32V634c0-4.4-3.6-8-8-8z"></path></svg>Orange3-3.36.1-Python3.8.8.dmg</a><p class="sc-9e86dd37-1 hOptom"><b>Not sure which installer to select?</b> Click the Apple logo in the top-left corner of your screen, select About This Mac, and check the Chip or Processor field. If you see Apple, select the Orange for Apple Silicon installer. If you see Intel, select the Orange for Intel.</p></div><div><h2>Other platforms</h2><h3>Anaconda</h3><p>If you are using python provided by Anaconda distribution, you are almost ready to go. Add conda-forge to the list of channels you can install packages from (and make it default)</p><pre><code>conda config --add channels conda-forge conda config --set channel_priority strict</code></pre><p>and run</p><pre><code>conda install orange3</code></pre><p>A universal bundle with everything packed in and ready to use.</p><h3>Pip</h3><p>Orange can also be installed from the Python Package Index. You may need additional system packages provided by your distribution.</p><pre><code>pip install orange3</code></pre><h3>Installing from source</h3><p>Clone our repository from<!-- --> <a href="https://github.com/biolab/orange3">GitHub</a> or download the<!-- --> <a href="https://github.com/biolab/orange3/archive/stable.tar.gz">source code tarball</a>. Then follow the instructions in<!-- --> <a href="https://github.com/biolab/orange3/blob/stable/README.md">README.md</a></p><p>To run Orange Canvas run</p><pre><code>python -m Orange.canvas</code></pre></div><div><h2>Download archive</h2>Download older versions from<!-- --> <a href="https://download.biolab.si/download/files/" target="_blank" rel="noreferrer">our archive</a>.</div></div><aside class="sc-9e86dd37-3 kDdGha"><img alt="Addons" loading="lazy" width="575" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/download-addons.a17fe177.png"/><img alt="Addons" loading="lazy" width="575" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/download-addons.a17fe177.png"/><div class="sc-9e86dd37-4 bGpcux"><h2>Installing add-ons</h2><p>Additional features can be added to Orange by installing add-ons. You can find add-on manager in Options menu.</p></div></aside></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{}},"page":"/download","query":{},"buildId":"EvXyAVV-yObR_-FUjGdn6","nextExport":true,"autoExport":true,"isFallback":false,"scriptLoader":[]}</script></body></html>
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+</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div><div class="sc-7f613caa-0 cpanWS"><div class="sc-61db3aea-0 eVggRJ"><img alt="Download orange" loading="lazy" width="470" height="191" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/download-hero.c2cfe7d2.png"/><h1>Download Orange</h1></div></div><div class="sc-61db3aea-0 sc-e168ad2d-5 eVggRJ ellMcr"><div class="sc-e168ad2d-0 hhkjgZ"><div class="sc-e168ad2d-2 eLikCu"><div><h2 id="win">Windows</h2><h3>Standalone installer (default)</h3><a class="sc-e168ad2d-6 jUZmrF" href="https://download.biolab.si/download/files/Orange3-3.36.2-Miniconda-x86_64.exe"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M505.7 661a8 8 0 0 0 12.6 0l112-141.7c4.1-5.2.4-12.9-6.3-12.9h-74.1V168c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v338.3H400c-6.7 0-10.4 7.7-6.3 12.9l112 141.8zM878 626h-60c-4.4 0-8 3.6-8 8v154H214V634c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v198c0 17.7 14.3 32 32 32h684c17.7 0 32-14.3 32-32V634c0-4.4-3.6-8-8-8z"></path></svg>Orange3-3.36.2-Miniconda-x86_64.exe (64 bit)</a><p>Can be used without administrative priviledges.</p><h3>Portable Orange</h3><a class="sc-e168ad2d-6 jUZmrF" href="https://download.biolab.si/download/files/Orange3-3.36.2.zip"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M505.7 661a8 8 0 0 0 12.6 0l112-141.7c4.1-5.2.4-12.9-6.3-12.9h-74.1V168c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v338.3H400c-6.7 0-10.4 7.7-6.3 12.9l112 141.8zM878 626h-60c-4.4 0-8 3.6-8 8v154H214V634c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v198c0 17.7 14.3 32 32 32h684c17.7 0 32-14.3 32-32V634c0-4.4-3.6-8-8-8z"></path></svg>Orange3-3.36.2.zip</a><p>No installation needed. Just extract the archive and open the shortcut in the extracted folder.</p></div><div><h2 id="mac">macOS</h2><h3>Orange for Apple silicon</h3><a class="sc-e168ad2d-6 jUZmrF" href="https://download.biolab.si/download/files/Orange3-3.36.2-Python3.9.12-arm64.dmg"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M505.7 661a8 8 0 0 0 12.6 0l112-141.7c4.1-5.2.4-12.9-6.3-12.9h-74.1V168c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v338.3H400c-6.7 0-10.4 7.7-6.3 12.9l112 141.8zM878 626h-60c-4.4 0-8 3.6-8 8v154H214V634c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v198c0 17.7 14.3 32 32 32h684c17.7 0 32-14.3 32-32V634c0-4.4-3.6-8-8-8z"></path></svg>Orange3-3.36.2-Python3.9.12-arm64.dmg</a><h3>Orange for Intel</h3><a class="sc-e168ad2d-6 jUZmrF" href="https://download.biolab.si/download/files/Orange3-3.36.2-Python3.9.12.dmg"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M505.7 661a8 8 0 0 0 12.6 0l112-141.7c4.1-5.2.4-12.9-6.3-12.9h-74.1V168c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v338.3H400c-6.7 0-10.4 7.7-6.3 12.9l112 141.8zM878 626h-60c-4.4 0-8 3.6-8 8v154H214V634c0-4.4-3.6-8-8-8h-60c-4.4 0-8 3.6-8 8v198c0 17.7 14.3 32 32 32h684c17.7 0 32-14.3 32-32V634c0-4.4-3.6-8-8-8z"></path></svg>Orange3-3.36.2-Python3.9.12.dmg</a><p class="sc-e168ad2d-1 foirNp"><b>Not sure which installer to select?</b> Click the Apple logo in the top-left corner of your screen, select About This Mac, and check the Chip or Processor field. If you see Apple, select the Orange for Apple Silicon installer. If you see Intel, select the Orange for Intel.</p></div><div><h2>Other platforms</h2><h3>Anaconda</h3><p>If you are using python provided by Anaconda distribution, you are almost ready to go. Add conda-forge to the list of channels you can install packages from (and make it default)</p><pre><code>conda config --add channels conda-forge conda config --set channel_priority strict</code></pre><p>and run</p><pre><code>conda install orange3</code></pre><p>A universal bundle with everything packed in and ready to use.</p><h3>Pip</h3><p>Orange can also be installed from the Python Package Index. You may need additional system packages provided by your distribution.</p><pre><code>pip install orange3</code></pre><h3>Installing from source</h3><p>Clone our repository from<!-- --> <a href="https://github.com/biolab/orange3">GitHub</a> or download the<!-- --> <a href="https://github.com/biolab/orange3/archive/stable.tar.gz">source code tarball</a>. Then follow the instructions in<!-- --> <a href="https://github.com/biolab/orange3/blob/stable/README.md">README.md</a></p><p>To run Orange Canvas run</p><pre><code>python -m Orange.canvas</code></pre></div><div><h2>Download archive</h2>Download older versions from<!-- --> <a href="https://download.biolab.si/download/files/" target="_blank" rel="noreferrer">our archive</a>.</div></div><aside class="sc-e168ad2d-3 bIvfKt"><img alt="Addons" loading="lazy" width="575" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/download-addons.a17fe177.png"/><img alt="Addons" loading="lazy" width="575" height="546" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/download-addons.a17fe177.png"/><div class="sc-e168ad2d-4 hBQQCi"><h2>Installing add-ons</h2><p>Additional features can be added to Orange by installing add-ons. You can find add-on manager in Options menu.</p></div></aside></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{}},"page":"/download","query":{},"buildId":"94QTGfoo8u2d9JarndNd4","nextExport":true,"autoExport":true,"isFallback":false,"scriptLoader":[]}</script></body></html>
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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu dXFGRw"> <!-- -->Examples</h1><div class="sc-889b783c-0 hvTfLC"><button class="sc-d3c41432-0 gTOGMU">Text Mining</button><button class="sc-d3c41432-0 gTOGMU">Bioinformatics</button><button class="sc-d3c41432-0 gTOGMU">Fairness</button><button class="sc-d3c41432-0 gTOGMU">Survival Analysis</button><button class="sc-d3c41432-0 gTOGMU">Classification</button><button class="sc-d3c41432-0 gTOGMU">Clustering</button><button class="sc-d3c41432-0 gTOGMU">Hierarchical Clustering</button><button class="sc-d3c41432-0 gTOGMU">Cox Regression</button><button class="sc-d3c41432-0 gTOGMU">Scatter Plot</button><button class="sc-d3c41432-0 gTOGMU">Visualization</button></div><ul class="sc-9e4f7d84-0 ENlF"><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Data Table</span>, </span><span><span>Data Loading</span></span></p><h2>File and Data Table</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>The basic data mining units in Orange are called widgets. In this workflow, the File widget reads the data. File widget communicates this data to Data Table widget that shows the data in a spreadsheet. The output of File is connected to the input of Data Table.</p></div><a href="/examples/file-and-data-table-widget/110-file-and-data-table-widget.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1380" height="840" decoding="async" data-nimg="1" style="color:transparent" src="/examples/file-and-data-table-widget/file-and-data-table-widget.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Scatter Plot</span>, </span><span><span>Visualization</span></span></p><h2>Interactive Visualizations</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>Most visualizations in Orange are interactive. Scatter Plot for example. Double click its icon to open it and click-and-drag to select a few data points from the plot. Selected data will automatically propagate to Data Table. Double click it to check which data was selected. Change selection and observe the change in the Data Table. This works best if both widgets are open.</p></div><a href="/examples/scatterplot-data-table/120-scatterplot-data-table.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1241" height="763" decoding="async" data-nimg="1" style="color:transparent" src="/examples/scatterplot-data-table/scatterplot-data-table.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Scatter Plot</span>, </span><span><span>Visualization</span></span></p><h2>Visalization of Data Subsets</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>Some visualization widget, like Scatter Plot and several data projection widgets, can expose the data instances in the data subset. In this workflow, Scatter Plot visualizes the data from the input data file, but also marks the data points that have been selected in the Data Table (selected rows).</p></div><a href="/examples/data-subsets/130-scatterplot-visualize-subset.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1316" height="801" decoding="async" data-nimg="1" style="color:transparent" src="/examples/data-subsets/scatterplot-visualize-subset.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Data</span>, </span><span><span>Pivot Table</span></span></p><h2>Pivot Table</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>Pivot Table can help us aggregate and transform the data. This workflow takes Kickstarter projects and aggregates them by month. We can inspect the frequency of the published projects per month and observe the difference between funded and non-funded projects. Try constructing several tables with pivot and experiment with different aggregation methods.</p></div><a href="/examples/pivot-table/140-pivot-table.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1592" height="969" decoding="async" data-nimg="1" style="color:transparent" src="/examples/pivot-table/pivot-table.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Classification Tree</span>, </span><span><span>Classification</span></span></p><h2>Classification Tree</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>This workflow combines the interface and visualization of classification trees with scatter plot. When both the tree viewer and the scatter plot are open, selection of any node of the tree sends the related data instances to scatter plot. In the workflow, the selected data is treated as a subset of the entire dataset and is highlighted in the scatter plot. With simple combination of widgets we have constructed an interactive classification tree browser.</p></div><a href="/examples/tree-scatterplot/250-tree-scatterplot.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1437" height="875" decoding="async" data-nimg="1" style="color:transparent" src="/examples/tree-scatterplot/tree-scatterplot.png"/></div></li></ul><div class="sc-a2f31186-0 bdlyrE"><button class="sc-a2f31186-1 jQYhVi">1</button><button class="sc-a2f31186-1 ewzjsY">2</button><button class="sc-a2f31186-1 ewzjsY">3</button><span>...</span><button class="sc-a2f31186-1 ewzjsY">9</button><button aria-label="next page" class="sc-a2f31186-2 fQlHUc"><span>Next</span><img alt="" loading="lazy" width="7" height="10" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-arrow.a5093233.svg"/></button></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"examples":[{"title":"File and Data Table","images":[{"width":1380,"height":840,"src":"/examples/file-and-data-table-widget/file-and-data-table-widget.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/file-and-data-table-widget/110-file-and-data-table-widget.ows","workflows":["Data Table","Data Loading"],"weight":120,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The basic data mining units in Orange are called widgets. In this workflow, the File widget reads the data. File widget communicates this data to Data Table widget that shows the data in a spreadsheet. The output of File is connected to the input of Data Table.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Interactive Visualizations","images":[{"width":1241,"height":763,"src":"/examples/scatterplot-data-table/scatterplot-data-table.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/scatterplot-data-table/120-scatterplot-data-table.ows","workflows":["Scatter Plot","Visualization"],"weight":120,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Most visualizations in Orange are interactive. Scatter Plot for example. Double click its icon to open it and click-and-drag to select a few data points from the plot. Selected data will automatically propagate to Data Table. Double click it to check which data was selected. Change selection and observe the change in the Data Table. This works best if both widgets are open.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Visalization of Data Subsets","images":[{"width":1316,"height":801,"src":"/examples/data-subsets/scatterplot-visualize-subset.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/data-subsets/130-scatterplot-visualize-subset.ows","workflows":["Scatter Plot","Visualization"],"weight":130,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Some visualization widget, like Scatter Plot and several data projection widgets, can expose the data instances in the data subset. In this workflow, Scatter Plot visualizes the data from the input data file, but also marks the data points that have been selected in the Data Table (selected rows).\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Pivot Table","images":[{"width":1592,"height":969,"src":"/examples/pivot-table/pivot-table.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/pivot-table/140-pivot-table.ows","workflows":["Data","Pivot Table"],"weight":140,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Pivot Table can help us aggregate and transform the data. This workflow takes Kickstarter projects and aggregates them by month. We can inspect the frequency of the published projects per month and observe the difference between funded and non-funded projects. Try constructing several tables with pivot and experiment with different aggregation methods.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Classification Tree","images":[{"width":1437,"height":875,"src":"/examples/tree-scatterplot/tree-scatterplot.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/tree-scatterplot/250-tree-scatterplot.ows","workflows":["Classification Tree","Classification"],"weight":250,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"This workflow combines the interface and visualization of classification trees with scatter plot. When both the tree viewer and the scatter plot are open, selection of any node of the tree sends the related data instances to scatter plot. In the workflow, the selected data is treated as a subset of the entire dataset and is highlighted in the scatter plot. With simple combination of widgets we have constructed an interactive classification tree browser.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Inspecting Outliers with Silhouette","images":[{"width":1329,"height":809,"src":"/examples/outliers/silhouette.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/outliers/260-outliers.ows","workflows":["Silhouette","Outliers","Visualization"],"weight":260,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Silhouette Plot shows how 'well-centered' each data instance is with respect to its cluster or class label. In this workflow we use iris' class labels to observe which flowers are typical representatives of their class and which are the outliers. Select instances left of zero in the plot and observe which flowers are these. Try connecting the selection with the Scatter Plot to highlight the outliers.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Principal Component Analysis","images":[{"width":1167,"height":711,"src":"/examples/principal-component-analysis/pca.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/principal-component-analysis/305-pca.ows","workflows":["PCA","Dimensionality Reduction"],"weight":305,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"PCA transforms the data into a dataset with uncorrelated variables, also called principal components. PCA widget displays a graph (scree diagram) showing a degree of explained variance by best principal components and allows to interactively set the number of components to be included in the output dataset. In this workflow, we can observe the transformation in the Data Table and in Scatter Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Hierarchical Clustering","images":[{"width":1610,"height":980,"src":"/examples/hierarchical-clustering/hierarchical-clustering.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/hierarchical-clustering/310-clustering.ows","workflows":["Hierarchical Clustering","Clustering"],"weight":310,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The workflow clusters the data items in iris dataset by first examining the distances between data instances. Distance matrix is passed to Hierarchical Clustering, which renders the dendrogram. Select different parts of the dendrogram to further analyze the corresponding data.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cluster Inspection","images":[{"width":1380,"height":840,"src":"/examples/cluster-inspection/cluster-inspection.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/cluster-inspection/315-cluster-inspection.ows","workflows":["Clustering","Hierarchical Clustering","Box Plot"],"weight":315,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We use the zoo data set in combination with Hierarchical Clustering to discover groups of animals. Now that we have the clusters we want to find out what is significant for each cluster! Pass the clusters to Box Plot and use 'Order by relevance' to discover what defines a cluster. Seems like they are well-separated by the type, even though the clustering was unaware of the class label!\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Feature Ranking","images":[{"width":1196,"height":728,"src":"/examples/feature-ranking/feature-ranking.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/feature-ranking/410-feature-ranking.ows","workflows":["Feature Ranking","Feature Selection"],"weight":410,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"For supervised problems, where data instances are annotated with class labels, we would like to know which are the most informative features. Rank widget provides a table of features and their informativity scores, and supports manual feature selection. In the workflow, we used it to find the best two features (of initial 79 from brown-selected dataset) and display its scatter plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Train and Test Data","images":[{"width":1637,"height":997,"src":"/examples/data-sampler/data-sampler.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/data-sampler/420-data-sampler.ows","workflows":["Classification","Data Sampler","Predictive models"],"weight":420,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"In building predictive models it is important to have a separate train and test data sets in order to avoid overfitting and to properly score the models. Here we use Data Sampler to split the data into training and test data, use training data for building a model and, finally, test on test data. Try several other classifiers to see how the scores change.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cross Validation","images":[{"width":1135,"height":691,"src":"/examples/cross-validation/cross-validation.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/cross-validation/450-cross-validation.ows","workflows":["Cross Validation","Predictive models","Classification"],"weight":450,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"How good are supervised data mining methods on your classification dataset? Here's a workflow that scores various classification techniques on a dataset from medicine. The central widget here is the one for testing and scoring, which is given the data and a set of learners, does cross-validation and scores predictive accuracy, and outputs the scores for further examination.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Where Are Misclassifications","images":[{"width":1079,"height":657,"src":"/examples/where-are-misclassifications/misclassifications.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/where-are-misclassifications/470-misclassification-scatterplot.ows","workflows":["Confusion Matrix","Classification","Scatter Plot"],"weight":470,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Cross-validation of, say, logistic regression can expose the data instances which were misclassified. There are six such instances for iris dataset and ridge-regularized logistic regression. We can select different types of misclassification in Confusion Matrix and highlight them in the Scatter Plot. No surprise: the misclassified instances are close to the class-bordering regions in the scatter plot projection.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Text Preprocessing","images":[{"width":1271,"height":774,"src":"/examples/text-preprocessing/text-preprocessing.png"}],"type":"workflows","blog_link":"/blog/2017/06/19/text-preprocessing/","video":"https://youtu.be/V70UwJZWkZ8","download":"/examples/text-preprocessing/610-text-preprocessing.ows","workflows":["Text Mining","Preprocessing","Tokenization"],"weight":610,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Text mining requires careful preprocessing. Here's a workflow that uses simple preprocessing for creating tokens from documents. First, it applies lowercase, then splits text into words, and finally, it removes frequent stopwords. Preprocessing is language specific, so change the language to the language of texts where required. Results of preprocessing can be observe in a Word Cloud.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Text Clustering","images":[{"width":1518,"height":924,"src":"/examples/text-clustering/text-clustering.png"}],"type":"workflows","blog_link":"","video":"https://youtu.be/rH_vQxQL6oM","download":"/examples/text-clustering/620-text-clustering.ows","workflows":["Text Mining","Clustering","Tokenization"],"weight":620,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The workflow clusters Grimm's tales corpus. We start by preprocessing the data and constructing the bag of words matrix. Then we compute cosine distances between documents and use Hierarchical Clustering, which displays the dendrogram. We observe how well the type of the tale corresponds to the cluster in the MDS.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Text Classification","images":[{"width":1748,"height":1064,"src":"/examples/text-classification/text-classification.png"}],"type":"workflows","blog_link":"","video":"https://youtu.be/zO_zwKZCULo","download":"/examples/text-classification/630-text-classification.ows","workflows":["Text Mining","Classification","Nomogram","Bag of Words"],"weight":630,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can use predictive models to classify documents by authorship, their type, sentiment and so on. In this workflow we classify documents by their Aarne-Thompshon-Uther index, that is the defining topic of the tale. We use two simple learners, Logistic Regression and Naive Bayes, both of which can be inspected in the Nomogram.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Twitter Data Analysis","images":[{"width":1326,"height":807,"src":"/examples/twitter/twitter.png"}],"type":"workflows","blog_link":"","video":"https://youtu.be/HDkI6G4slzQ","download":"/examples/twitter/640-twitter.ows","workflows":["Text Mining","Twitter","Topic Modeling"],"weight":640,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Tweets are a valuable source of information, for social scientists, marketing managers, linguists, economists, and so on. In this workflow we retrieve data from Twitter, preprocess it, and uncover latent topics with topic modeling. We observe the topics in a Heat Map.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Story Arcs","images":[{"width":1278,"height":778,"src":"/examples/story-arcs/story-arcs.png"}],"type":"workflows","blog_link":"/blog/2020/2020-07-27-story-arcs/","video":"","download":"/examples/story-arcs/650-story-arcs.ows","workflows":["Text Mining","Timeseries","Sentiment Analysis"],"weight":649,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"In this workflow we explore story arcs in the Little Match Seller story. First we select the story from the corpus of Andersen tales. Then we create a table, where each sentence of the tale is a separate row. We use sentiment analysis to compute the sentiment of each sentence, then observe the emotional arcs through the story. We also inspect sentences with similar scores in the Heat Map and Corpus Viewer.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Load Text Corpus from the Server Repository","images":[{"width":553,"height":319,"src":"/examples/text-loading-from-repository/text-loading-from-repository.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-loading-from-repository/text-loading-from-repository.ows","workflows":["Text Mining"],"weight":650,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"The workflow loads the corpus from the \", _jsx(_components.a, {\n      href: \"http://file.biolab.si/text-semantics/data/predlogi-vladi-1k/\",\n      children: \"text repository on the server\"\n    }), \". The repository contains documents with raw text and associated YAML files with meta-features. We here use some pre-processing and then display the most frequent words in a word cloud. This workflow could work on your repository: just change the URL in the Import Documents widget.\"]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Semantic Document Map","images":[{"width":671,"height":329,"src":"/examples/text-semantic-document-map/text-semantic-document-map.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-semantic-document-map/text-semantic-document-map.ows","workflows":["Text Mining"],"weight":652,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Document maps may reveal clusters of documents with semantically similar content. Here we show a workflow that loads the corpus, performs some text preprocessing and embeds the documents in the vector space using the fastText deep model. The t-SNE widget reveals the document map, where we can select a set of documents and then explore them in Corpus Viewer or characterize them in the display of the most frequent words in the Word Cloud.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Semantic Word Map","images":[{"width":701,"height":395,"src":"/examples/text-semantic-word-map/text-semantic-word-map.png"},{"width":758,"height":581,"src":"/examples/text-semantic-word-map/text-semantic-word-map-tsne.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-semantic-word-map/text-semantic-word-map.ows","workflows":["Text Mining"],"weight":654,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can find clusters of semantically related words either by hierarchical clustering or t-SNE visualizations. Here, we show a workflow that loads the documents, extracts frequent words, embeds them in a vector space, and explores word clusters.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Semantic search","images":[{"width":956,"height":718,"src":"/examples/text-semantic-viewer/text-semantic-search.png"},{"width":985,"height":560,"src":"/examples/text-semantic-viewer/text-semantic-search-widget.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-semantic-viewer/text-semantic-search.ows","workflows":["Text Mining"],"weight":655,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can find relevant parts of a document by searching for exact words or parts of documents with similar meanings. The Semantic Viewer widget in this workflow searches for pertinent sentences of the documents by comparing the meaning of words from the Word List widget to the meaning of sentences in the text via SBERT text embeddings. The widget ranks documents by scores measuring their relevance, shows selected documents, and highlights the relevant parts.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Keyword Extraction from a Set of Text Documents","images":[{"width":587,"height":336,"src":"/examples/text-keyword-extraction/text-keyword-extraction.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-keyword-extraction/text-keyword-extraction.ows","workflows":["Text Mining"],"weight":656,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The Extract Keywords widget can characterize a set of textual documents. In this workflow, we load the documents from the server, preprocess them and embed them in the vector space, and display a semantic document map in the t-SNE widget. In this widget, we can select a set of similar documents and then characterize them through keyword extraction. Extract keywords support different inference techniques, including TF-IDF and deep network-based characterization.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Keyword-Based Text Document Scoring","images":[{"width":796,"height":392,"src":"/examples/text-keyword-based-scoring/text-keyword-based-scoring.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-keyword-based-scoring/text-keyword-based-scoring.ows","workflows":["Text Mining"],"weight":658,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can score the text documents based on a list of keywords, say, to find the documents which include the keywords or are semantically related to the list of keywords. This workflow shows the Score Documents widget for scoring and the Word List widget to compose a list of keywords. The scores are visualized in the t-SNE document map.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Corpus and Word Maps","images":[{"width":2226,"height":892,"src":"/examples/text-corpus-and-word-map/text-corpus-and-word-map.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-corpus-and-word-map/text-corpus-and-word-map.ows","workflows":["Text Mining"],"weight":660,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"This workflow shows how to extract the most common words from the documents and observe clusters of semantically similar words with Hierarchical Clustering. We select a group of words (connected to the traffic and roads) and use them to score documents according to selection with the Score Documents widget. The scores are visualized in the document map by the Self-Organizing Maps widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Document Map Annotation","images":[{"width":1606,"height":590,"src":"/examples/text-annotator/text-annotator.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-annotator/text-annotator.ows","workflows":["Text Mining"],"weight":664,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Documents maps can be enhanced with the keywords annotations. This workflow embeds documents in vector space, computes a t-SNE document map and annotates it. The Annotator widget identifies clusters on the map and annotates them with keywords representing a cluster.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Ontology Generation from Keywords","images":[{"width":1438,"height":534,"src":"/examples/text-ontology/text-ontology.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-ontology/text-ontology.ows","workflows":["Text Mining"],"weight":668,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can automatically build the otology from the set of words. In the workflow, we select a group of documents with similar content. From the selected documents, we extract keywords and generate a new ontology from the subset of keywords with the Ontology widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Survival Curve Estimation","images":[{"width":581,"height":310,"src":"/examples/survival-curve-estimation/700_survival_curve_estimation.png"}],"type":"workflows","blog":"","video":"","download":"/examples/survival-curve-estimation/700_survival_curve_estimation.ows","workflows":["Survival Analysis"],"weight":700,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"One of the primary objectives of survival analysis is to estimate the survival probability from observed survival times of different patients. The workflow plots the Kaplan-Meier approximation of the survival curve for the investigated population in the German breast cancer study group. The Kaplan-Meier plot is interactive; we select the longest-surviving patients and use Box Plot to analyze features that best characterize them.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Exploring Survival Features","images":[{"width":621,"height":343,"src":"/examples/exploring-survival-features/710_survival_features.png"}],"type":"workflows","blog":"","video":"","download":"/examples/exploring-survival-features/710_survival_features.ows","workflows":["Survival Analysis","Cox Regression"],"weight":710,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"In the workflow, we show how to find and analyze variables related to survival. We start with variables ranked by univariate Cox regression analysis, where we can select the feature of interest. The Distribution widget shows its distribution and allows us to choose interactively a group of patients related to its values. We compare the survival of this group to all other patients in the Kaplan-Meier plot widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cohort Construction and Validation","images":[{"width":1796,"height":686,"src":"/examples/cohort-construction-and-validation/720_cohort_construction_and_validation.png"}],"type":"workflows","blog":"","video":"","download":"/examples/cohort-construction-and-validation/720_cohort_construction_and_validation.ows","workflows":["Survival Analysis","Kaplan-Meier","Cox Regression"],"weight":720,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"Stratification of patients into low and high-risk groups is a common task in survival analysis to identify clinical and biological factors that contribute to survival. One approach to stratification is by computing risk score values based on the Cox regression model. With the clever use of Orange widgets, we can split the data into training and validation sets and then interactively generate risk score models on training data to observe the difference in cohorts' survival rate on training and validation samples side-by-side. Read more on how \", _jsx(_components.a, {\n      href: \"blog/why-you-should-use-apply-domain\",\n      children: \"Apply domain\"\n    }), \" enables this kind of workflows.\"]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cross Validation for Survival Models","images":[{"width":487,"height":343,"src":"/examples/c-index-cross-validation/730_c_index_cross_validation.png"}],"type":"workflows","blog":"","video":"","download":"/examples/c-index-cross-validation/730_c_index_cross_validation.ows","workflows":["Survival Analysis","Cox Regression","Concordance Index","Cross Validation"],"weight":730,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Orange built-in methods for testing and scoring the predictive models now support survival-related models like Cox regression. Here we demonstrate cross-validation to estimate the concordance index for the Cox regression model trained on data instances from selected features.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Explore Subpopulations with Distinct Risk Profiles","images":[{"width":647,"height":419,"src":"/examples/explore-subpopulations-with-distinct-risk-profiles/740_hierarhical_clustering.png"}],"type":"workflows","blog":"","video":"","download":"/examples/explore-subpopulations-with-distinct-risk-profiles/740_hierarhical_clustering.ows","workflows":["Survival Analysis","Clustering","Hierarchical Clustering","Box Plot"],"weight":740,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can visualize the difference in subpopulations of breast cancer patients in the METABRIC dataset through clustering, that is, by identifying groups of data instances similar to each other. We can observe the difference in survival rate between clusters with Kaplan-Meier Plot and explore features that characterize patients of each cluster with the Box Plot widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Identify Differentially Expressed Genes with Volcano Plot","images":[{"width":902,"height":414,"src":"/examples/bioinformatics-volcano-plot/bioinformatics_volcano_plot.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-volcano-plot/bioinformatics_volcano_plot.ows","workflows":["Bioinformatics"],"weight":800,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Explore gene expression differences with a Volcano Plot in genomic analysis. Visualize significant upregulation or downregulation of genes between two conditions and identify critical genes with substantial changes and high statistical significance, potentially revealing key biomarkers or therapeutic targets.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Find Homologs for Differentially Expressed Genes","images":[{"width":982,"height":401,"src":"/examples/bioinformatics-homologs/bioinformatics_homologs.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-homologs/bioinformatics_homologs.ows","workflows":["Bioinformatics"],"weight":810,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Homologous genes, although not identical, often perform analogous functions in different organisms. Here, we show a workflow that loads a mouse gene expression dataset, annotates the genes, select the top 100 differentially expressed genes, and finds their human homologs.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Score Overexpressed Genes in Individual Samples","images":[{"width":1190,"height":443,"src":"/examples/bioinformatics-gene-sets/bioinformatics_gene_sets.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-gene-sets/bioinformatics_gene_sets.ows","workflows":["Bioinformatics"],"weight":820,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Studying specific genes linked to a biological process or disease can yield valuable insights. We present a workflow to analyze the expression of 284 human homologs of E.coli DNA damage-up proteins (DDPs). These DDPs fall into three gene sets: All DDPs, excluding known cancer drivers, and validated DDPs causing DNA damage in human cells. Enrichment scores are assigned using the Single Sample Scoring widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cluster gene expression and enrichment","images":[{"width":1026,"height":443,"src":"/examples/bioinformatics-cluster-analysis/bioinformatics_cluster_analysis.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-cluster-analysis/bioinformatics_cluster_analysis.ows","workflows":["Bioinformatics"],"weight":830,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Analyzing the differential expression of genes characterizing clusters and enriched gene sets helps uncover specific molecular pathways and regulatory mechanisms that contribute to distinct biological functions or disease states. In this workflow, we use the Cluster Analysis to identify differentially expressed genes and the enriched gene sets that define the clusters.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Mental health of tech employees in times of COVID-19","images":[{"width":1372,"height":850,"src":"/examples/mental-health/mental-health.png"}],"type":"workflows","blog_link":"https://ocean.sagepub.com/blog/mental-health-of-tech-employees-in-times-of-covid-19","video":"","download":"/examples/mental-health/900_mental-health.ows","workflows":["mental health","survey","Hierarchical Clustering"],"weight":900,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"The COVID-19 pandemic brought about many societal changes, including a serious effect on mental health. The Open Sourcing Mental Health 2021 survey measures attitudes towards mental health in the tech workplace and examines the frequency of mental health disorders among tech workers. The workflow presents how to uncover different types of tech employees based on their responses. The procedure is fully described in the \", _jsx(_components.a, {\n      href: \"https://ocean.sagepub.com/blog/mental-health-of-tech-employees-in-times-of-covid-19\",\n      children: \"SAGE Ocean blog\"\n    }), \".\"]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Annotate Cells Using Marker Genes","images":[{"width":894,"height":569,"src":"/examples/bioinformatics-annotator/bioinformatics_annotator.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-annotator/bioinformatics_annotator.ows","workflows":["Bioinformatics"],"weight":940,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Annotating cells with marker genes identifies cell types, enhancing the understanding of functions and interactions in studies such as single-cell analysis. This workflow involves loading a single-cell dataset, identifying marker genes, and annotating cells based on gene expression, utilizing the t-SNE widget for visualization.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Gene Set Enrichment Analysis","images":[{"width":666,"height":374,"src":"/examples/bioinformatics-gsea/bioinformatics_gsea.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-gsea/bioinformatics_gsea.ows","workflows":["Bioinformatics"],"weight":950,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Find gene sets overrepresented in a large group of genes, possibly associated with different phenotypes. Gene Set Enrichment Analysis widget provides a list of gene sets and their enrichment scores, and supports manual selection of gene sets.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Dataset Bias Examination","images":[{"width":957,"height":435,"src":"/examples/fairness-dataset-bias/fairness-dataset-bias.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-18-fairness-dataset-bias/","video":"","download":"/examples/fairness-dataset-bias/fairness-dataset-bias.ows","workflows":["Fairness"],"weight":1000,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Understanding the potential biases within datasets is crucial for fair machine-learning outcomes. This workflow detects dataset bias using a straightforward algorithm. After loading the dataset, we add specific fairness attributes to it, which are essential for our calculations. We then compute the fairness metrics via the Dataset Bias widget and explain the results in a Box Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Reweighing a Dataset","images":[{"width":1260,"height":548,"src":"/examples/fairness-reweighing-dataset/fairness-reweighing-dataset.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-reweighing-dataset/","video":"","download":"/examples/fairness-reweighing-dataset/fairness-reweighing-dataset.ows","workflows":["Fairness"],"weight":1001,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Detecting bias is only the first step in ensuring fair machine learning. The next step is to mitigate the bias. This workflow illustrates removing bias at the dataset level using the Reweighing widget. Initially, split the data into training and validation subsets. We then check for bias in the validation set before reweighing. Using the training set, we train the reweighing algorithm and apply it to the validation set. Finally, we check for bias in the reweighed validation set. We can also visualize the effect of the reweighing using a Box Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Reweighing as a preprocessor","images":[{"width":1355,"height":652,"src":"/examples/fairness-reweighing-preprocessor/fairness-reweighing-preprocessor.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-reweighing-preprocessor/","video":"","download":"/examples/fairness-reweighing-preprocessor/fairness-reweighing-preprocessor.ows","workflows":["Fairness"],"weight":1002,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can use the reweighing fairness algorithm for more than just adding weights to a dataset. It can also be used as a preprocessor for a specific model. This workflow illustrates how to use the Reweighing widget as a preprocessor for the Logistic Regression model. Initially, we load the dataset and input it into the Test and Score widget, which we will use to evaluate our model. Now, we need to connect a Logistic Regression model, which has the Reweighing widget as a preprocessor, to the Test and Score widget. Doing so ensures our data gets reweighed before the model learns from it. We compare these results against those derived from a Logistic Regression model without reweighing and visualize both using a Box Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Adversarial Debiasing","images":[{"width":937,"height":511,"src":"/examples/fairness-adversarial-debiasing/fairness-adversarial-debiasing.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-adversarial-debiasing/","video":"","download":"/examples/fairness-adversarial-debiasing/fairness-adversarial-debiasing.ows","workflows":["Fairness"],"weight":1003,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The easiest method to address bias in machine learning is to use a bias-aware model. This approach eliminates the need for fairness preprocessing or postprocessing. In this workflow, we will employ a bias-aware model named Adversarial Debasing for classification. We will train two versions of this model: one with and one without debiasing. Finally, we will compare and display the fairness metrics using a box plot widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Equal Odds Postprocessing","images":[{"width":1197,"height":615,"src":"/examples/fairness-equal-odds-postprocessing/fairness-equal-odds-postprocessing.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-equal-odds-postprocessing/","video":"","download":"/examples/fairness-equal-odds-postprocessing/fairness-equal-odds-postprocessing.ows","workflows":["Fairness"],"weight":1004,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Another way to mitigate bias is to use a postprocessing algorithm on the model's predictions. This workflow illustrates using the Equal Odds widget as a post-processor for the Logistic Regression model. To use the post-processor, we need to connect any model to the Equalized Odds Postprocessing widget along with any needed pre-processors. Doing so ensures our model's predictions get post-processed before we evaluate them.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Hiding Protected Attribute","images":[{"width":1300,"height":667,"src":"/examples/fairness-hiding-protected-attribute/fairness-hiding-protected-attribute.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-hiding-protected-attribute/","video":"","download":"/examples/fairness-hiding-protected-attribute/fairness-hiding-protected-attribute.ows","workflows":["Fairness"],"weight":1005,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Why would we use fairness algorithms instead of simply removing the protected attribute to avoid bias? This workflow illustrates why that is not such a good idea. We compare the predictions of a model using Reweighing to those of a model on data with the protected attribute removed. We use Box Plot to visualize and compare common fairness metrics.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}}],"tags":["Text Mining","Bioinformatics","Fairness","Survival Analysis","Classification","Clustering","Hierarchical Clustering","Cox Regression","Scatter Plot","Visualization"]},"__N_SSG":true},"page":"/examples","query":{},"buildId":"EvXyAVV-yObR_-FUjGdn6","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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+data-styled.g54[id="sc-9e4f7d84-4"]{content:"hufBEB,"}/*!sc*/
+</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu dXFGRw"> <!-- -->Examples</h1><div class="sc-889b783c-0 hvTfLC"><button class="sc-d3c41432-0 gTOGMU">Text Mining</button><button class="sc-d3c41432-0 gTOGMU">Bioinformatics</button><button class="sc-d3c41432-0 gTOGMU">Fairness</button><button class="sc-d3c41432-0 gTOGMU">Survival Analysis</button><button class="sc-d3c41432-0 gTOGMU">Classification</button><button class="sc-d3c41432-0 gTOGMU">Clustering</button><button class="sc-d3c41432-0 gTOGMU">Hierarchical Clustering</button><button class="sc-d3c41432-0 gTOGMU">Cox Regression</button><button class="sc-d3c41432-0 gTOGMU">Scatter Plot</button><button class="sc-d3c41432-0 gTOGMU">Visualization</button></div><ul class="sc-9e4f7d84-0 ENlF"><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Data Table</span>, </span><span><span>Data Loading</span></span></p><h2>File and Data Table</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>The basic data mining units in Orange are called widgets. In this workflow, the File widget reads the data. File widget communicates this data to Data Table widget that shows the data in a spreadsheet. The output of File is connected to the input of Data Table.</p></div><a href="/examples/file-and-data-table-widget/110-file-and-data-table-widget.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1380" height="840" decoding="async" data-nimg="1" style="color:transparent" src="/examples/file-and-data-table-widget/file-and-data-table-widget.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Scatter Plot</span>, </span><span><span>Visualization</span></span></p><h2>Interactive Visualizations</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>Most visualizations in Orange are interactive. Scatter Plot for example. Double click its icon to open it and click-and-drag to select a few data points from the plot. Selected data will automatically propagate to Data Table. Double click it to check which data was selected. Change selection and observe the change in the Data Table. This works best if both widgets are open.</p></div><a href="/examples/scatterplot-data-table/120-scatterplot-data-table.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1241" height="763" decoding="async" data-nimg="1" style="color:transparent" src="/examples/scatterplot-data-table/scatterplot-data-table.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Scatter Plot</span>, </span><span><span>Visualization</span></span></p><h2>Visalization of Data Subsets</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>Some visualization widget, like Scatter Plot and several data projection widgets, can expose the data instances in the data subset. In this workflow, Scatter Plot visualizes the data from the input data file, but also marks the data points that have been selected in the Data Table (selected rows).</p></div><a href="/examples/data-subsets/130-scatterplot-visualize-subset.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1316" height="801" decoding="async" data-nimg="1" style="color:transparent" src="/examples/data-subsets/scatterplot-visualize-subset.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Data</span>, </span><span><span>Pivot Table</span></span></p><h2>Pivot Table</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>Pivot Table can help us aggregate and transform the data. This workflow takes Kickstarter projects and aggregates them by month. We can inspect the frequency of the published projects per month and observe the difference between funded and non-funded projects. Try constructing several tables with pivot and experiment with different aggregation methods.</p></div><a href="/examples/pivot-table/140-pivot-table.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1592" height="969" decoding="async" data-nimg="1" style="color:transparent" src="/examples/pivot-table/pivot-table.png"/></div></li><li class="sc-9e4f7d84-2 jLRiqb"><div class="sc-9e4f7d84-3 bVdaXF"><p class="sc-3c206b28-3 gRZIks"><span><span>Classification Tree</span>, </span><span><span>Classification</span></span></p><h2>Classification Tree</h2><div class="sc-9e4f7d84-1 gEwLqo"><p>This workflow combines the interface and visualization of classification trees with scatter plot. When both the tree viewer and the scatter plot are open, selection of any node of the tree sends the related data instances to scatter plot. In the workflow, the selected data is treated as a subset of the entire dataset and is highlighted in the scatter plot. With simple combination of widgets we have constructed an interactive classification tree browser.</p></div><a href="/examples/tree-scatterplot/250-tree-scatterplot.ows" class="sc-b6ea565a-0 jyxOJT">Download</a></div><div class="sc-9e4f7d84-4 hufBEB"><img alt="" loading="lazy" width="1437" height="875" decoding="async" data-nimg="1" style="color:transparent" src="/examples/tree-scatterplot/tree-scatterplot.png"/></div></li></ul><div class="sc-a2f31186-0 bdlyrE"><button class="sc-a2f31186-1 jQYhVi">1</button><button class="sc-a2f31186-1 ewzjsY">2</button><button class="sc-a2f31186-1 ewzjsY">3</button><span>...</span><button class="sc-a2f31186-1 ewzjsY">9</button><button aria-label="next page" class="sc-a2f31186-2 fQlHUc"><span>Next</span><img alt="" loading="lazy" width="7" height="10" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-arrow.a5093233.svg"/></button></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"examples":[{"title":"File and Data Table","images":[{"width":1380,"height":840,"src":"/examples/file-and-data-table-widget/file-and-data-table-widget.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/file-and-data-table-widget/110-file-and-data-table-widget.ows","workflows":["Data Table","Data Loading"],"weight":120,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The basic data mining units in Orange are called widgets. In this workflow, the File widget reads the data. File widget communicates this data to Data Table widget that shows the data in a spreadsheet. The output of File is connected to the input of Data Table.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Interactive Visualizations","images":[{"width":1241,"height":763,"src":"/examples/scatterplot-data-table/scatterplot-data-table.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/scatterplot-data-table/120-scatterplot-data-table.ows","workflows":["Scatter Plot","Visualization"],"weight":120,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Most visualizations in Orange are interactive. Scatter Plot for example. Double click its icon to open it and click-and-drag to select a few data points from the plot. Selected data will automatically propagate to Data Table. Double click it to check which data was selected. Change selection and observe the change in the Data Table. This works best if both widgets are open.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Visalization of Data Subsets","images":[{"width":1316,"height":801,"src":"/examples/data-subsets/scatterplot-visualize-subset.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/data-subsets/130-scatterplot-visualize-subset.ows","workflows":["Scatter Plot","Visualization"],"weight":130,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Some visualization widget, like Scatter Plot and several data projection widgets, can expose the data instances in the data subset. In this workflow, Scatter Plot visualizes the data from the input data file, but also marks the data points that have been selected in the Data Table (selected rows).\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Pivot Table","images":[{"width":1592,"height":969,"src":"/examples/pivot-table/pivot-table.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/pivot-table/140-pivot-table.ows","workflows":["Data","Pivot Table"],"weight":140,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Pivot Table can help us aggregate and transform the data. This workflow takes Kickstarter projects and aggregates them by month. We can inspect the frequency of the published projects per month and observe the difference between funded and non-funded projects. Try constructing several tables with pivot and experiment with different aggregation methods.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Classification Tree","images":[{"width":1437,"height":875,"src":"/examples/tree-scatterplot/tree-scatterplot.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/tree-scatterplot/250-tree-scatterplot.ows","workflows":["Classification Tree","Classification"],"weight":250,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"This workflow combines the interface and visualization of classification trees with scatter plot. When both the tree viewer and the scatter plot are open, selection of any node of the tree sends the related data instances to scatter plot. In the workflow, the selected data is treated as a subset of the entire dataset and is highlighted in the scatter plot. With simple combination of widgets we have constructed an interactive classification tree browser.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Inspecting Outliers with Silhouette","images":[{"width":1329,"height":809,"src":"/examples/outliers/silhouette.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/outliers/260-outliers.ows","workflows":["Silhouette","Outliers","Visualization"],"weight":260,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Silhouette Plot shows how 'well-centered' each data instance is with respect to its cluster or class label. In this workflow we use iris' class labels to observe which flowers are typical representatives of their class and which are the outliers. Select instances left of zero in the plot and observe which flowers are these. Try connecting the selection with the Scatter Plot to highlight the outliers.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Principal Component Analysis","images":[{"width":1167,"height":711,"src":"/examples/principal-component-analysis/pca.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/principal-component-analysis/305-pca.ows","workflows":["PCA","Dimensionality Reduction"],"weight":305,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"PCA transforms the data into a dataset with uncorrelated variables, also called principal components. PCA widget displays a graph (scree diagram) showing a degree of explained variance by best principal components and allows to interactively set the number of components to be included in the output dataset. In this workflow, we can observe the transformation in the Data Table and in Scatter Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Hierarchical Clustering","images":[{"width":1610,"height":980,"src":"/examples/hierarchical-clustering/hierarchical-clustering.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/hierarchical-clustering/310-clustering.ows","workflows":["Hierarchical Clustering","Clustering"],"weight":310,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The workflow clusters the data items in iris dataset by first examining the distances between data instances. Distance matrix is passed to Hierarchical Clustering, which renders the dendrogram. Select different parts of the dendrogram to further analyze the corresponding data.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cluster Inspection","images":[{"width":1380,"height":840,"src":"/examples/cluster-inspection/cluster-inspection.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/cluster-inspection/315-cluster-inspection.ows","workflows":["Clustering","Hierarchical Clustering","Box Plot"],"weight":315,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We use the zoo data set in combination with Hierarchical Clustering to discover groups of animals. Now that we have the clusters we want to find out what is significant for each cluster! Pass the clusters to Box Plot and use 'Order by relevance' to discover what defines a cluster. Seems like they are well-separated by the type, even though the clustering was unaware of the class label!\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Feature Ranking","images":[{"width":1196,"height":728,"src":"/examples/feature-ranking/feature-ranking.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/feature-ranking/410-feature-ranking.ows","workflows":["Feature Ranking","Feature Selection"],"weight":410,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"For supervised problems, where data instances are annotated with class labels, we would like to know which are the most informative features. Rank widget provides a table of features and their informativity scores, and supports manual feature selection. In the workflow, we used it to find the best two features (of initial 79 from brown-selected dataset) and display its scatter plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Train and Test Data","images":[{"width":1637,"height":997,"src":"/examples/data-sampler/data-sampler.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/data-sampler/420-data-sampler.ows","workflows":["Classification","Data Sampler","Predictive models"],"weight":420,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"In building predictive models it is important to have a separate train and test data sets in order to avoid overfitting and to properly score the models. Here we use Data Sampler to split the data into training and test data, use training data for building a model and, finally, test on test data. Try several other classifiers to see how the scores change.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cross Validation","images":[{"width":1135,"height":691,"src":"/examples/cross-validation/cross-validation.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/cross-validation/450-cross-validation.ows","workflows":["Cross Validation","Predictive models","Classification"],"weight":450,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"How good are supervised data mining methods on your classification dataset? Here's a workflow that scores various classification techniques on a dataset from medicine. The central widget here is the one for testing and scoring, which is given the data and a set of learners, does cross-validation and scores predictive accuracy, and outputs the scores for further examination.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Where Are Misclassifications","images":[{"width":1079,"height":657,"src":"/examples/where-are-misclassifications/misclassifications.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/where-are-misclassifications/470-misclassification-scatterplot.ows","workflows":["Confusion Matrix","Classification","Scatter Plot"],"weight":470,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Cross-validation of, say, logistic regression can expose the data instances which were misclassified. There are six such instances for iris dataset and ridge-regularized logistic regression. We can select different types of misclassification in Confusion Matrix and highlight them in the Scatter Plot. No surprise: the misclassified instances are close to the class-bordering regions in the scatter plot projection.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Text Preprocessing","images":[{"width":1271,"height":774,"src":"/examples/text-preprocessing/text-preprocessing.png"}],"type":"workflows","blog_link":"/blog/2017/06/19/text-preprocessing/","video":"https://youtu.be/V70UwJZWkZ8","download":"/examples/text-preprocessing/610-text-preprocessing.ows","workflows":["Text Mining","Preprocessing","Tokenization"],"weight":610,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Text mining requires careful preprocessing. Here's a workflow that uses simple preprocessing for creating tokens from documents. First, it applies lowercase, then splits text into words, and finally, it removes frequent stopwords. Preprocessing is language specific, so change the language to the language of texts where required. Results of preprocessing can be observe in a Word Cloud.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Text Clustering","images":[{"width":1518,"height":924,"src":"/examples/text-clustering/text-clustering.png"}],"type":"workflows","blog_link":"","video":"https://youtu.be/rH_vQxQL6oM","download":"/examples/text-clustering/620-text-clustering.ows","workflows":["Text Mining","Clustering","Tokenization"],"weight":620,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The workflow clusters Grimm's tales corpus. We start by preprocessing the data and constructing the bag of words matrix. Then we compute cosine distances between documents and use Hierarchical Clustering, which displays the dendrogram. We observe how well the type of the tale corresponds to the cluster in the MDS.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Text Classification","images":[{"width":1748,"height":1064,"src":"/examples/text-classification/text-classification.png"}],"type":"workflows","blog_link":"","video":"https://youtu.be/zO_zwKZCULo","download":"/examples/text-classification/630-text-classification.ows","workflows":["Text Mining","Classification","Nomogram","Bag of Words"],"weight":630,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can use predictive models to classify documents by authorship, their type, sentiment and so on. In this workflow we classify documents by their Aarne-Thompshon-Uther index, that is the defining topic of the tale. We use two simple learners, Logistic Regression and Naive Bayes, both of which can be inspected in the Nomogram.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Twitter Data Analysis","images":[{"width":1326,"height":807,"src":"/examples/twitter/twitter.png"}],"type":"workflows","blog_link":"","video":"https://youtu.be/HDkI6G4slzQ","download":"/examples/twitter/640-twitter.ows","workflows":["Text Mining","Twitter","Topic Modeling"],"weight":640,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Tweets are a valuable source of information, for social scientists, marketing managers, linguists, economists, and so on. In this workflow we retrieve data from Twitter, preprocess it, and uncover latent topics with topic modeling. We observe the topics in a Heat Map.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Story Arcs","images":[{"width":1278,"height":778,"src":"/examples/story-arcs/story-arcs.png"}],"type":"workflows","blog_link":"/blog/2020/2020-07-27-story-arcs/","video":"","download":"/examples/story-arcs/650-story-arcs.ows","workflows":["Text Mining","Timeseries","Sentiment Analysis"],"weight":649,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"In this workflow we explore story arcs in the Little Match Seller story. First we select the story from the corpus of Andersen tales. Then we create a table, where each sentence of the tale is a separate row. We use sentiment analysis to compute the sentiment of each sentence, then observe the emotional arcs through the story. We also inspect sentences with similar scores in the Heat Map and Corpus Viewer.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Load Text Corpus from the Server Repository","images":[{"width":553,"height":319,"src":"/examples/text-loading-from-repository/text-loading-from-repository.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-loading-from-repository/text-loading-from-repository.ows","workflows":["Text Mining"],"weight":650,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"The workflow loads the corpus from the \", _jsx(_components.a, {\n      href: \"http://file.biolab.si/text-semantics/data/predlogi-vladi-1k/\",\n      children: \"text repository on the server\"\n    }), \". The repository contains documents with raw text and associated YAML files with meta-features. We here use some pre-processing and then display the most frequent words in a word cloud. This workflow could work on your repository: just change the URL in the Import Documents widget.\"]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Semantic Document Map","images":[{"width":671,"height":329,"src":"/examples/text-semantic-document-map/text-semantic-document-map.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-semantic-document-map/text-semantic-document-map.ows","workflows":["Text Mining"],"weight":652,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Document maps may reveal clusters of documents with semantically similar content. Here we show a workflow that loads the corpus, performs some text preprocessing and embeds the documents in the vector space using the fastText deep model. The t-SNE widget reveals the document map, where we can select a set of documents and then explore them in Corpus Viewer or characterize them in the display of the most frequent words in the Word Cloud.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Semantic Word Map","images":[{"width":701,"height":395,"src":"/examples/text-semantic-word-map/text-semantic-word-map.png"},{"width":758,"height":581,"src":"/examples/text-semantic-word-map/text-semantic-word-map-tsne.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-semantic-word-map/text-semantic-word-map.ows","workflows":["Text Mining"],"weight":654,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can find clusters of semantically related words either by hierarchical clustering or t-SNE visualizations. Here, we show a workflow that loads the documents, extracts frequent words, embeds them in a vector space, and explores word clusters.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Semantic search","images":[{"width":956,"height":718,"src":"/examples/text-semantic-viewer/text-semantic-search.png"},{"width":985,"height":560,"src":"/examples/text-semantic-viewer/text-semantic-search-widget.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-semantic-viewer/text-semantic-search.ows","workflows":["Text Mining"],"weight":655,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can find relevant parts of a document by searching for exact words or parts of documents with similar meanings. The Semantic Viewer widget in this workflow searches for pertinent sentences of the documents by comparing the meaning of words from the Word List widget to the meaning of sentences in the text via SBERT text embeddings. The widget ranks documents by scores measuring their relevance, shows selected documents, and highlights the relevant parts.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Keyword Extraction from a Set of Text Documents","images":[{"width":587,"height":336,"src":"/examples/text-keyword-extraction/text-keyword-extraction.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-keyword-extraction/text-keyword-extraction.ows","workflows":["Text Mining"],"weight":656,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The Extract Keywords widget can characterize a set of textual documents. In this workflow, we load the documents from the server, preprocess them and embed them in the vector space, and display a semantic document map in the t-SNE widget. In this widget, we can select a set of similar documents and then characterize them through keyword extraction. Extract keywords support different inference techniques, including TF-IDF and deep network-based characterization.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Keyword-Based Text Document Scoring","images":[{"width":796,"height":392,"src":"/examples/text-keyword-based-scoring/text-keyword-based-scoring.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-keyword-based-scoring/text-keyword-based-scoring.ows","workflows":["Text Mining"],"weight":658,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can score the text documents based on a list of keywords, say, to find the documents which include the keywords or are semantically related to the list of keywords. This workflow shows the Score Documents widget for scoring and the Word List widget to compose a list of keywords. The scores are visualized in the t-SNE document map.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Corpus and Word Maps","images":[{"width":2226,"height":892,"src":"/examples/text-corpus-and-word-map/text-corpus-and-word-map.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-corpus-and-word-map/text-corpus-and-word-map.ows","workflows":["Text Mining"],"weight":660,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"This workflow shows how to extract the most common words from the documents and observe clusters of semantically similar words with Hierarchical Clustering. We select a group of words (connected to the traffic and roads) and use them to score documents according to selection with the Score Documents widget. The scores are visualized in the document map by the Self-Organizing Maps widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Document Map Annotation","images":[{"width":1606,"height":590,"src":"/examples/text-annotator/text-annotator.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-annotator/text-annotator.ows","workflows":["Text Mining"],"weight":664,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Documents maps can be enhanced with the keywords annotations. This workflow embeds documents in vector space, computes a t-SNE document map and annotates it. The Annotator widget identifies clusters on the map and annotates them with keywords representing a cluster.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Ontology Generation from Keywords","images":[{"width":1438,"height":534,"src":"/examples/text-ontology/text-ontology.png"}],"type":"workflows","blog_link":"","video":"","download":"/examples/text-ontology/text-ontology.ows","workflows":["Text Mining"],"weight":668,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can automatically build the otology from the set of words. In the workflow, we select a group of documents with similar content. From the selected documents, we extract keywords and generate a new ontology from the subset of keywords with the Ontology widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Survival Curve Estimation","images":[{"width":581,"height":310,"src":"/examples/survival-curve-estimation/700_survival_curve_estimation.png"}],"type":"workflows","blog":"","video":"","download":"/examples/survival-curve-estimation/700_survival_curve_estimation.ows","workflows":["Survival Analysis"],"weight":700,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"One of the primary objectives of survival analysis is to estimate the survival probability from observed survival times of different patients. The workflow plots the Kaplan-Meier approximation of the survival curve for the investigated population in the German breast cancer study group. The Kaplan-Meier plot is interactive; we select the longest-surviving patients and use Box Plot to analyze features that best characterize them.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Exploring Survival Features","images":[{"width":621,"height":343,"src":"/examples/exploring-survival-features/710_survival_features.png"}],"type":"workflows","blog":"","video":"","download":"/examples/exploring-survival-features/710_survival_features.ows","workflows":["Survival Analysis","Cox Regression"],"weight":710,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"In the workflow, we show how to find and analyze variables related to survival. We start with variables ranked by univariate Cox regression analysis, where we can select the feature of interest. The Distribution widget shows its distribution and allows us to choose interactively a group of patients related to its values. We compare the survival of this group to all other patients in the Kaplan-Meier plot widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cohort Construction and Validation","images":[{"width":1796,"height":686,"src":"/examples/cohort-construction-and-validation/720_cohort_construction_and_validation.png"}],"type":"workflows","blog":"","video":"","download":"/examples/cohort-construction-and-validation/720_cohort_construction_and_validation.ows","workflows":["Survival Analysis","Kaplan-Meier","Cox Regression"],"weight":720,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"Stratification of patients into low and high-risk groups is a common task in survival analysis to identify clinical and biological factors that contribute to survival. One approach to stratification is by computing risk score values based on the Cox regression model. With the clever use of Orange widgets, we can split the data into training and validation sets and then interactively generate risk score models on training data to observe the difference in cohorts' survival rate on training and validation samples side-by-side. Read more on how \", _jsx(_components.a, {\n      href: \"blog/why-you-should-use-apply-domain\",\n      children: \"Apply domain\"\n    }), \" enables this kind of workflows.\"]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cross Validation for Survival Models","images":[{"width":487,"height":343,"src":"/examples/c-index-cross-validation/730_c_index_cross_validation.png"}],"type":"workflows","blog":"","video":"","download":"/examples/c-index-cross-validation/730_c_index_cross_validation.ows","workflows":["Survival Analysis","Cox Regression","Concordance Index","Cross Validation"],"weight":730,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Orange built-in methods for testing and scoring the predictive models now support survival-related models like Cox regression. Here we demonstrate cross-validation to estimate the concordance index for the Cox regression model trained on data instances from selected features.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Explore Subpopulations with Distinct Risk Profiles","images":[{"width":647,"height":419,"src":"/examples/explore-subpopulations-with-distinct-risk-profiles/740_hierarhical_clustering.png"}],"type":"workflows","blog":"","video":"","download":"/examples/explore-subpopulations-with-distinct-risk-profiles/740_hierarhical_clustering.ows","workflows":["Survival Analysis","Clustering","Hierarchical Clustering","Box Plot"],"weight":740,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can visualize the difference in subpopulations of breast cancer patients in the METABRIC dataset through clustering, that is, by identifying groups of data instances similar to each other. We can observe the difference in survival rate between clusters with Kaplan-Meier Plot and explore features that characterize patients of each cluster with the Box Plot widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Identify Differentially Expressed Genes with Volcano Plot","images":[{"width":902,"height":414,"src":"/examples/bioinformatics-volcano-plot/bioinformatics_volcano_plot.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-volcano-plot/bioinformatics_volcano_plot.ows","workflows":["Bioinformatics"],"weight":800,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Explore gene expression differences with a Volcano Plot in genomic analysis. Visualize significant upregulation or downregulation of genes between two conditions and identify critical genes with substantial changes and high statistical significance, potentially revealing key biomarkers or therapeutic targets.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Find Homologs for Differentially Expressed Genes","images":[{"width":982,"height":401,"src":"/examples/bioinformatics-homologs/bioinformatics_homologs.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-homologs/bioinformatics_homologs.ows","workflows":["Bioinformatics"],"weight":810,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Homologous genes, although not identical, often perform analogous functions in different organisms. Here, we show a workflow that loads a mouse gene expression dataset, annotates the genes, select the top 100 differentially expressed genes, and finds their human homologs.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Score Overexpressed Genes in Individual Samples","images":[{"width":1190,"height":443,"src":"/examples/bioinformatics-gene-sets/bioinformatics_gene_sets.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-gene-sets/bioinformatics_gene_sets.ows","workflows":["Bioinformatics"],"weight":820,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Studying specific genes linked to a biological process or disease can yield valuable insights. We present a workflow to analyze the expression of 284 human homologs of E.coli DNA damage-up proteins (DDPs). These DDPs fall into three gene sets: All DDPs, excluding known cancer drivers, and validated DDPs causing DNA damage in human cells. Enrichment scores are assigned using the Single Sample Scoring widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Cluster gene expression and enrichment","images":[{"width":1026,"height":443,"src":"/examples/bioinformatics-cluster-analysis/bioinformatics_cluster_analysis.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-cluster-analysis/bioinformatics_cluster_analysis.ows","workflows":["Bioinformatics"],"weight":830,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Analyzing the differential expression of genes characterizing clusters and enriched gene sets helps uncover specific molecular pathways and regulatory mechanisms that contribute to distinct biological functions or disease states. In this workflow, we use the Cluster Analysis to identify differentially expressed genes and the enriched gene sets that define the clusters.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Mental health of tech employees in times of COVID-19","images":[{"width":1372,"height":850,"src":"/examples/mental-health/mental-health.png"}],"type":"workflows","blog_link":"https://ocean.sagepub.com/blog/mental-health-of-tech-employees-in-times-of-covid-19","video":"","download":"/examples/mental-health/900_mental-health.ows","workflows":["mental health","survey","Hierarchical Clustering"],"weight":900,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_components.p, {\n    children: [\"The COVID-19 pandemic brought about many societal changes, including a serious effect on mental health. The Open Sourcing Mental Health 2021 survey measures attitudes towards mental health in the tech workplace and examines the frequency of mental health disorders among tech workers. The workflow presents how to uncover different types of tech employees based on their responses. The procedure is fully described in the \", _jsx(_components.a, {\n      href: \"https://ocean.sagepub.com/blog/mental-health-of-tech-employees-in-times-of-covid-19\",\n      children: \"SAGE Ocean blog\"\n    }), \".\"]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Annotate Cells Using Marker Genes","images":[{"width":894,"height":569,"src":"/examples/bioinformatics-annotator/bioinformatics_annotator.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-annotator/bioinformatics_annotator.ows","workflows":["Bioinformatics"],"weight":940,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Annotating cells with marker genes identifies cell types, enhancing the understanding of functions and interactions in studies such as single-cell analysis. This workflow involves loading a single-cell dataset, identifying marker genes, and annotating cells based on gene expression, utilizing the t-SNE widget for visualization.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Gene Set Enrichment Analysis","images":[{"width":666,"height":374,"src":"/examples/bioinformatics-gsea/bioinformatics_gsea.png"}],"type":"workflows","blog":"","video":"","download":"/examples/bioinformatics-gsea/bioinformatics_gsea.ows","workflows":["Bioinformatics"],"weight":950,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Find gene sets overrepresented in a large group of genes, possibly associated with different phenotypes. Gene Set Enrichment Analysis widget provides a list of gene sets and their enrichment scores, and supports manual selection of gene sets.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Dataset Bias Examination","images":[{"width":957,"height":435,"src":"/examples/fairness-dataset-bias/fairness-dataset-bias.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-18-fairness-dataset-bias/","video":"","download":"/examples/fairness-dataset-bias/fairness-dataset-bias.ows","workflows":["Fairness"],"weight":1000,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Understanding the potential biases within datasets is crucial for fair machine-learning outcomes. This workflow detects dataset bias using a straightforward algorithm. After loading the dataset, we add specific fairness attributes to it, which are essential for our calculations. We then compute the fairness metrics via the Dataset Bias widget and explain the results in a Box Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Reweighing a Dataset","images":[{"width":1260,"height":548,"src":"/examples/fairness-reweighing-dataset/fairness-reweighing-dataset.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-reweighing-dataset/","video":"","download":"/examples/fairness-reweighing-dataset/fairness-reweighing-dataset.ows","workflows":["Fairness"],"weight":1001,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Detecting bias is only the first step in ensuring fair machine learning. The next step is to mitigate the bias. This workflow illustrates removing bias at the dataset level using the Reweighing widget. Initially, split the data into training and validation subsets. We then check for bias in the validation set before reweighing. Using the training set, we train the reweighing algorithm and apply it to the validation set. Finally, we check for bias in the reweighed validation set. We can also visualize the effect of the reweighing using a Box Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Reweighing as a preprocessor","images":[{"width":1355,"height":652,"src":"/examples/fairness-reweighing-preprocessor/fairness-reweighing-preprocessor.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-reweighing-preprocessor/","video":"","download":"/examples/fairness-reweighing-preprocessor/fairness-reweighing-preprocessor.ows","workflows":["Fairness"],"weight":1002,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"We can use the reweighing fairness algorithm for more than just adding weights to a dataset. It can also be used as a preprocessor for a specific model. This workflow illustrates how to use the Reweighing widget as a preprocessor for the Logistic Regression model. Initially, we load the dataset and input it into the Test and Score widget, which we will use to evaluate our model. Now, we need to connect a Logistic Regression model, which has the Reweighing widget as a preprocessor, to the Test and Score widget. Doing so ensures our data gets reweighed before the model learns from it. We compare these results against those derived from a Logistic Regression model without reweighing and visualize both using a Box Plot.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Adversarial Debiasing","images":[{"width":937,"height":511,"src":"/examples/fairness-adversarial-debiasing/fairness-adversarial-debiasing.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-adversarial-debiasing/","video":"","download":"/examples/fairness-adversarial-debiasing/fairness-adversarial-debiasing.ows","workflows":["Fairness"],"weight":1003,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"The easiest method to address bias in machine learning is to use a bias-aware model. This approach eliminates the need for fairness preprocessing or postprocessing. In this workflow, we will employ a bias-aware model named Adversarial Debasing for classification. We will train two versions of this model: one with and one without debiasing. Finally, we will compare and display the fairness metrics using a box plot widget.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Equal Odds Postprocessing","images":[{"width":1197,"height":615,"src":"/examples/fairness-equal-odds-postprocessing/fairness-equal-odds-postprocessing.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-equal-odds-postprocessing/","video":"","download":"/examples/fairness-equal-odds-postprocessing/fairness-equal-odds-postprocessing.ows","workflows":["Fairness"],"weight":1004,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Another way to mitigate bias is to use a postprocessing algorithm on the model's predictions. This workflow illustrates using the Equal Odds widget as a post-processor for the Logistic Regression model. To use the post-processor, we need to connect any model to the Equalized Odds Postprocessing widget along with any needed pre-processors. Doing so ensures our model's predictions get post-processed before we evaluate them.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}},{"title":"Hiding Protected Attribute","images":[{"width":1300,"height":667,"src":"/examples/fairness-hiding-protected-attribute/fairness-hiding-protected-attribute.png"}],"type":"workflows","blog_link":"/blog/2023/2023-09-19-fairness-hiding-protected-attribute/","video":"","download":"/examples/fairness-hiding-protected-attribute/fairness-hiding-protected-attribute.ows","workflows":["Fairness"],"weight":1005,"content":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Why would we use fairness algorithms instead of simply removing the protected attribute to avoid bias? This workflow illustrates why that is not such a good idea. We compare the predictions of a model using Reweighing to those of a model on data with the protected attribute removed. We use Box Plot to visualize and compare common fairness metrics.\"\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}}}],"tags":["Text Mining","Bioinformatics","Fairness","Survival Analysis","Classification","Clustering","Hierarchical Clustering","Cox Regression","Scatter Plot","Visualization"]},"__N_SSG":true},"page":"/examples","query":{},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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diff --git a/faq/index.html b/faq/index.html
index d9f983726..53cf4d06f 100644
--- a/faq/index.html
+++ b/faq/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu dXFGRw"> <!-- -->FAQ</h1><h2 class="sc-89e95305-1 sc-61331446-0 gjppcK dUbIAT">Troubleshooting</h2>
 <div data-szh-adn="" class="szh-accordion"><div class="szh-accordion__item szh-accordion__item--status-exited sc-61331446-1 dFDZjz"><h3 style="margin:0" class="szh-accordion__item-heading"><button id=":R9qa6:" aria-controls=":R9qa6:-" aria-expanded="false" data-szh-adn-btn="" type="button" class="szh-accordion__item-btn">I am having trouble installing Orange</button></h3><div style="display:none" class="szh-accordion__item-content"><div id=":R9qa6:-" aria-labelledby=":R9qa6:" role="region" class="szh-accordion__item-panel"><p>Make sure you are downloading the latest version. You can also try to download a standalone installer or Anaconda distribution.</p><p>Still no luck? Check our <a href="https://github.com/biolab/orange3/issues">issue tracker</a> for similar issues or report a new one.</p></div></div></div><div class="szh-accordion__item szh-accordion__item--status-exited sc-61331446-1 dFDZjz"><h3 style="margin:0" class="szh-accordion__item-heading"><button id=":Rhqa6:" aria-controls=":Rhqa6:-" aria-expanded="false" data-szh-adn-btn="" type="button" class="szh-accordion__item-btn">I am having trouble installing an add-on.</button></h3><div style="display:none" class="szh-accordion__item-content"><div id=":Rhqa6:-" aria-labelledby=":Rhqa6:" role="region" class="szh-accordion__item-panel"><p>Make sure you are using the latest version of Orange. Encountering issues, please raise them on the relevant <a href="https://github.com/biolab?q=orange3-">add-on&#x27;s issue tracker</a>. Alternatively, you can try with Anaconda distribution and install the add-on in the terminal. You can use pip or conda to install a package directly. E.g.:</p><pre><code class="language-bash">pip install orange3-text
 conda install orange3-timeseries
@@ -177,4 +177,4 @@ <h2 class="sc-89e95305-1 sc-61331446-0 gjppcK dUbIAT">About Orange</h2>
 be a very big project to do well and would cost more than the expected
 benefits.</p></div></div></div><div class="szh-accordion__item szh-accordion__item--status-exited sc-61331446-1 dFDZjz"><h3 style="margin:0" class="szh-accordion__item-heading"><button id=":R1vqa6:" aria-controls=":R1vqa6:-" aria-expanded="false" data-szh-adn-btn="" type="button" class="szh-accordion__item-btn">Is Orange available in other languages?</button></h3><div style="display:none" class="szh-accordion__item-content"><div id=":R1vqa6:-" aria-labelledby=":R1vqa6:" role="region" class="szh-accordion__item-panel"><p>Are you willing to help? Native speakers welcome! Contact us. Currently, Orange and several add-ons are translated to Slovenian, and other languages should be easy to add (though the number of required translations is huge). Before announcing this functionality, we just need to resolve a few remaining (hopefully minor) technical issues.</p></div></div></div><div class="szh-accordion__item szh-accordion__item--status-exited sc-61331446-1 dFDZjz"><h3 style="margin:0" class="szh-accordion__item-heading"><button id=":R27qa6:" aria-controls=":R27qa6:-" aria-expanded="false" data-szh-adn-btn="" type="button" class="szh-accordion__item-btn">Does Orange work with R?</button></h3><div style="display:none" class="szh-accordion__item-content"><div id=":R27qa6:-" aria-labelledby=":R27qa6:" role="region" class="szh-accordion__item-panel"><p>No. Orange is a Python-based software and is as such not compatible with R.</p></div></div></div><div class="szh-accordion__item szh-accordion__item--status-exited sc-61331446-1 dFDZjz"><h3 style="margin:0" class="szh-accordion__item-heading"><button id=":R2fqa6:" aria-controls=":R2fqa6:-" aria-expanded="false" data-szh-adn-btn="" type="button" class="szh-accordion__item-btn">Does Orange have oversampling, such as SMOTE?</button></h3><div style="display:none" class="szh-accordion__item-content"><div id=":R2fqa6:-" aria-labelledby=":R2fqa6:" role="region" class="szh-accordion__item-panel"><p>No. There are reasons why<!-- --> <!-- -->
 <a href="https://github.com/biolab/orange3/pull/3269">we refrain from implementing SMOTE</a> <!-- -->
-. If you really wish to go ahead with it, you can use <a href="https://datascience.stackexchange.com/questions/50944/unbalanced-target-variable-in-orange-how-do-i-balance-it">Python Script.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {Accordion, AccordionItem, Link} = _components;\n  if (!Accordion) _missingMdxReference(\"Accordion\", true);\n  if (!AccordionItem) _missingMdxReference(\"AccordionItem\", true);\n  if (!Link) _missingMdxReference(\"Link\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"Troubleshooting\"\n    }), \"\\n\", _jsxs(Accordion, {\n      children: [_jsxs(AccordionItem, {\n        header: \"I am having trouble installing Orange\",\n        children: [_jsx(_components.p, {\n          children: \"Make sure you are downloading the latest version. You can also try to download a standalone installer or Anaconda distribution.\"\n        }), _jsxs(_components.p, {\n          children: [\"Still no luck? Check our \", _jsx(_components.a, {\n            href: \"https://github.com/biolab/orange3/issues\",\n            children: \"issue tracker\"\n          }), \" for similar issues or report a new one.\"]\n        })]\n      }), _jsxs(AccordionItem, {\n        header: \"I am having trouble installing an add-on.\",\n        children: [_jsxs(_components.p, {\n          children: [\"Make sure you are using the latest version of Orange. Encountering issues, please raise them on the relevant \", _jsx(Link, {\n            url: \"https://github.com/biolab?q=orange3-\",\n            name: \"add-on's issue tracker\"\n          }), \". Alternatively, you can try with Anaconda distribution and install the add-on in the terminal. You can use pip or conda to install a package directly. 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If this is not possible, please report the issue to our \", _jsx(_components.a, {\n            href: \"https://github.com/biolab/orange3/issues\",\n            children: \"issue tracker\"\n          }), \".\"]\n        })\n      }), _jsxs(AccordionItem, {\n        header: \"I believe there's a bug in Orange.\",\n        children: [_jsxs(_components.p, {\n          children: [\"First, make sure you are using the latest version of Orange. If the bug is still present, please submit an issue to our \", _jsx(Link, {\n            url: \"https://github.com/biolab/orange3/issues\",\n            name: \"issue tracker\"\n          }), \". Follow the template and describe the issue as well as possible.\"]\n        }), _jsx(_components.p, {\n          children: \"Before submitting a new issue, check if someone has already reported the same problem. Upvote the issue with a +1 and/or add your comment. 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In the meantime, see some examples on our\", \" \", \"\\n\", _jsx(Link, {\n            url: \"/blog/?tag=education\",\n            name: \"blog\"\n          }), \".\"]\n        })\n      })]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"About Orange\"\n    }), \"\\n\", _jsxs(Accordion, {\n      children: [_jsx(AccordionItem, {\n        header: \"What types of data does Orange support?\",\n        children: _jsx(_components.p, {\n          children: \"Core Orange supports Excel, comma- and tab-delimited files (.xlsx, .csv,\\n.tab). It also reads online data, such as Google Spreadsheets.\\nOrange3-ImageAnalytics add-on can import images (.jpg, .tiff, .png).\\nOrange3-Corpus add-on can import text files (.txt, .docx, .odt). 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You need \", _jsx(Link, {\n            url: \"http://initd.org/psycopg/\",\n            name: \"psycopg2\"\n          }), \" module installed to be able to see the widget. Install it directly in Python with\"]\n        }), _jsx(_components.pre, {\n          children: _jsx(_components.code, {\n            children: \"  pip install psycopg2\\n\"\n          })\n        }), _jsxs(_components.p, {\n          children: [\"command. You can also use \", _jsx(Link, {\n            url: \"https://www.2ndquadrant.com/en/resources/2uda/\",\n            name: \"2UDA package\"\n          }), \" for easier installation. Please note that Orange supports only PostgreSQL and MSSQL databases for now.\"]\n        })]\n      }), _jsx(AccordionItem, {\n        header: \"How do I add comments/annotations to my workflows?\",\n        children: _jsx(_components.p, {\n          children: \"There is an option in the lower left corner of the canvas. 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Before announcing this functionality, we just need to resolve a few remaining (hopefully minor) technical issues.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Does Orange work with R?\",\n        children: _jsx(_components.p, {\n          children: \"No. Orange is a Python-based software and is as such not compatible with R.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Does Orange have oversampling, such as SMOTE?\",\n        children: _jsxs(_components.p, {\n          children: [\"No. There are reasons why\", \" \", \"\\n\", _jsx(Link, {\n            url: \"https://github.com/biolab/orange3/pull/3269\",\n            name: \"we refrain from implementing SMOTE\"\n          }), \" \", \"\\n. 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\ No newline at end of file
+. If you really wish to go ahead with it, you can use <a href="https://datascience.stackexchange.com/questions/50944/unbalanced-target-variable-in-orange-how-do-i-balance-it">Python Script.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\",\n    a: \"a\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components), {Accordion, AccordionItem, Link} = _components;\n  if (!Accordion) _missingMdxReference(\"Accordion\", true);\n  if (!AccordionItem) _missingMdxReference(\"AccordionItem\", true);\n  if (!Link) _missingMdxReference(\"Link\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"Troubleshooting\"\n    }), \"\\n\", _jsxs(Accordion, {\n      children: [_jsxs(AccordionItem, {\n        header: \"I am having trouble installing Orange\",\n        children: [_jsx(_components.p, {\n          children: \"Make sure you are downloading the latest version. You can also try to download a standalone installer or Anaconda distribution.\"\n        }), _jsxs(_components.p, {\n          children: [\"Still no luck? Check our \", _jsx(_components.a, {\n            href: \"https://github.com/biolab/orange3/issues\",\n            children: \"issue tracker\"\n          }), \" for similar issues or report a new one.\"]\n        })]\n      }), _jsxs(AccordionItem, {\n        header: \"I am having trouble installing an add-on.\",\n        children: [_jsxs(_components.p, {\n          children: [\"Make sure you are using the latest version of Orange. Encountering issues, please raise them on the relevant \", _jsx(Link, {\n            url: \"https://github.com/biolab?q=orange3-\",\n            name: \"add-on's issue tracker\"\n          }), \". Alternatively, you can try with Anaconda distribution and install the add-on in the terminal. You can use pip or conda to install a package directly. 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If this is not possible, please report the issue to our \", _jsx(_components.a, {\n            href: \"https://github.com/biolab/orange3/issues\",\n            children: \"issue tracker\"\n          }), \".\"]\n        })\n      }), _jsxs(AccordionItem, {\n        header: \"I believe there's a bug in Orange.\",\n        children: [_jsxs(_components.p, {\n          children: [\"First, make sure you are using the latest version of Orange. If the bug is still present, please submit an issue to our \", _jsx(Link, {\n            url: \"https://github.com/biolab/orange3/issues\",\n            name: \"issue tracker\"\n          }), \". Follow the template and describe the issue as well as possible.\"]\n        }), _jsx(_components.p, {\n          children: \"Before submitting a new issue, check if someone has already reported the same problem. Upvote the issue with a +1 and/or add your comment. Only consider opening a new issue if you cannot find a corresponding existing open or closed issue.\"\n        })]\n      })]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Development and collaboration\"\n    }), \"\\n\", _jsxs(Accordion, {\n      children: [_jsxs(AccordionItem, {\n        header: \"There’s a feature I wish to see in Orange.\",\n        children: [_jsxs(_components.p, {\n          children: [\"Please submit a pull request with the feature. We welcome all contributions! Once you've made a PR, developer(s) will leave comments and when everything is fixed and tested, the pull request will be merged into Orange. See our \", _jsx(Link, {\n            url: \"https://orange.biolab.si/contributing/\",\n            name: \"Contribute guide\"\n          }), \" for more information.\"]\n        }), _jsxs(_components.p, {\n          children: [\"If you are not a developer, you can submit a feature request to our \", _jsx(Link, {\n            url: \"https://github.com/biolab/orange3/issues\",\n            name: \"issue tracker\"\n          }), \". Describe what you wish to see in Orange and, if possible, provide a visual example.\"]\n        }), _jsx(_components.p, {\n          children: \"Before submitting a new feature request, check if someone has already requested the same feature. Upvote the request with a +1 and/or add your comment. Only consider opening a new request if you cannot find a corresponding existing request.\"\n        })]\n      }), _jsx(AccordionItem, {\n        header: \"I wish to propose a collaboration and/or a project with the Orange team.\",\n        children: _jsxs(_components.p, {\n          children: [\"Please submit a \", _jsx(\"a\", {\n            href: \"/contact/\",\n            children: \"contact request\"\n          }), \" and we will respond as\\nsoon as we can.\"]\n        })\n      }), _jsx(AccordionItem, {\n        header: \"I wish to develop a widget / an add-on for Orange.\",\n        children: _jsxs(_components.p, {\n          children: [\"We are always happy to receive contributions! Development documentation for\", \" \", \"\\n\", _jsx(\"a\", {\n            href: \"https://orange-widget-base.readthedocs.io/\",\n            children: \" widgets\"\n          }), \" and\", \" \", \"\\n\", _jsx(\"a\", {\n            href: \"https://github.com/biolab/orange3-example-addon\",\n            children: \"add-ons\"\n          }), \" is\\navailable on the web. Next, submit a pull request and our team will review it.\\nIf you have specific questions, use\", \" \", \"\\n\", _jsx(\"a\", {\n            href: \"https://discord.gg/47gRDUQ\",\n            children: \"Discord\"\n          }), \" for direct communication with\\nthe developers.\"]\n        })\n      }), _jsx(AccordionItem, {\n        header: \"I wish to commission a custom module and/or an add-on for Orange.\",\n        children: _jsxs(_components.p, {\n          children: [\"Please submit a \", _jsx(\"a\", {\n            href: \"/contact\",\n            children: \"contact request\"\n          }), \" to discuss the idea\\nand rates.\"]\n        })\n      })]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Education\"\n    }), \"\\n\", _jsxs(Accordion, {\n      children: [_jsxs(AccordionItem, {\n        header: \"I would like to learn how to use Orange. Where can I find the resources?\",\n        children: [_jsxs(_components.p, {\n          children: [\"Widget documentation is available on our \", _jsx(Link, {\n            url: \"/widget-catalog\",\n            name: \"website\"\n          }), \". You can also access the same documentation directly from Orange by selecting the widget and pressing F1.\"]\n        }), _jsxs(_components.p, {\n          children: [\"We also provide an e-mail course (\", _jsx(Link, {\n            url: \"https://biolab.us15.list-manage.com/subscribe?u=2cad1f1cbf3a532a77efe4354\u0026id=6f257f15ff\",\n            name: \"subscribe here\"\n          }), \"), beginner-level \", _jsx(Link, {\n            url: \"https://www.youtube.com/playlist?list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\",\n            name: \"YouTube tutorials\"\n          }), \" and a regularly updated \", _jsx(Link, {\n            url: \"https://blog.biolab.si/\",\n            name: \"blog\"\n          }), \".\"]\n        })]\n      }), _jsx(AccordionItem, {\n        header: \"I have a question concerning data mining, statistics and machine learning algorithms.\",\n        children: _jsxs(_components.p, {\n          children: [\"Visit\", \" \", \"\\n\", _jsx(Link, {\n            url: \"https://datascience.stackexchange.com/questions/tagged/orange\",\n            name: \"Data Science StackExchange\"\n          }), \" \", \"\\nand ask the community!\"]\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Does Orange have tutorials?\",\n        children: _jsxs(_components.p, {\n          children: [\"Yes. There is a\", \" \", \"\\n\", _jsx(\"a\", {\n            href: \"https://www.youtube.com/c/OrangeDataMining\",\n            children: \"YouTube channel\"\n          }), \" where\\nyou can learn how to use Orange.\"]\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Is there an online training?\",\n        children: _jsxs(_components.p, {\n          children: [\"here are no online webinars or MOOCs. You can still visit our\", \" \", \"\\n\", _jsx(\"a\", {\n            href: \"https://www.youtube.com/c/OrangeDataMining\",\n            children: \"YouTube channel\"\n          }), \" and\\nlearn the basics there.\"]\n        })\n      }), _jsx(AccordionItem, {\n        header: \"How to teach with Orange?\",\n        children: _jsxs(_components.p, {\n          children: [\"We are preparing materials that will show you how to teach with Orange. Stayed\\ntuned for details! In the meantime, see some examples on our\", \" \", \"\\n\", _jsx(Link, {\n            url: \"/blog/?tag=education\",\n            name: \"blog\"\n          }), \".\"]\n        })\n      })]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"About Orange\"\n    }), \"\\n\", _jsxs(Accordion, {\n      children: [_jsx(AccordionItem, {\n        header: \"What types of data does Orange support?\",\n        children: _jsx(_components.p, {\n          children: \"Core Orange supports Excel, comma- and tab-delimited files (.xlsx, .csv,\\n.tab). It also reads online data, such as Google Spreadsheets.\\nOrange3-ImageAnalytics add-on can import images (.jpg, .tiff, .png).\\nOrange3-Corpus add-on can import text files (.txt, .docx, .odt). SQL widget\\nsupports PostgreSQL and MSSQL databases.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"How can I load my data in Orange?\",\n        children: _jsxs(_components.p, {\n          children: [\"Follow the\", \" \", \"\\n\", _jsx(Link, {\n            url: \"https://orange3.readthedocs.io/projects/orange-visual-programming/en/latest/loading-your-data/index.html\",\n            name: \"Loading your data\"\n          }), \" \", \"\\nguide or check out the\", \" \", \"\\n\", _jsx(Link, {\n            url: \"https://www.youtube.com/watch?v=MHcGdQeYCMg\u0026list=PLmNPvQr9Tf-ZSDLwOzxpvY-HrE0yv-8Fy\u0026index=5\u0026t=0s\",\n            name: \"Loading your data\"\n          }), \" \", \"\\nYouTube tutorial.\"]\n        })\n      }), _jsxs(AccordionItem, {\n        header: \"I want to use Orange on big data.\",\n        children: [_jsxs(_components.p, {\n          children: [\"Orange provides the SQL widget, which samples data instances and enables exploratory data analysis on large datasets. You need \", _jsx(Link, {\n            url: \"http://initd.org/psycopg/\",\n            name: \"psycopg2\"\n          }), \" module installed to be able to see the widget. Install it directly in Python with\"]\n        }), _jsx(_components.pre, {\n          children: _jsx(_components.code, {\n            children: \"  pip install psycopg2\\n\"\n          })\n        }), _jsxs(_components.p, {\n          children: [\"command. You can also use \", _jsx(Link, {\n            url: \"https://www.2ndquadrant.com/en/resources/2uda/\",\n            name: \"2UDA package\"\n          }), \" for easier installation. Please note that Orange supports only PostgreSQL and MSSQL databases for now.\"]\n        })]\n      }), _jsx(AccordionItem, {\n        header: \"How do I add comments/annotations to my workflows?\",\n        children: _jsx(_components.p, {\n          children: \"There is an option in the lower left corner of the canvas. The \\\"T\\\" icon adds\\ntext comments, while the arrow icon adds an arrow. To change text size, press\\nand hold the down arrow in the upper right corner of the text icon. To change\\nthe arrow color, press and hold the down arrow in the upper right corner of\\nthe arrow icon.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"How safe is my data?\",\n        children: _jsx(_components.p, {\n          children: \"Orange does not store any data. It is a locally installed software that can be\\nused without internet connection. The only exceptions are embedding widgets,\\nwhich send data to the server, compute and return the result. The data is\\nnever stored on the server.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Can I export Orange workflow as a Python script?\",\n        children: _jsxs(_components.p, {\n          children: [\"Unfortunately, no. We've\", \" \", \"\\n\", _jsx(Link, {\n            url: \"https://github.com/biolab/orange3/issues/1341\",\n            name: \"debated this long and hard\"\n          }), \" \", \"\\n. A (limited) functionality of this type would probably be possible, but would\\nbe a very big project to do well and would cost more than the expected\\nbenefits.\"]\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Is Orange available in other languages?\",\n        children: _jsx(_components.p, {\n          children: \"Are you willing to help? Native speakers welcome! Contact us. Currently, Orange and several add-ons are translated to Slovenian, and other languages should be easy to add (though the number of required translations is huge). Before announcing this functionality, we just need to resolve a few remaining (hopefully minor) technical issues.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Does Orange work with R?\",\n        children: _jsx(_components.p, {\n          children: \"No. Orange is a Python-based software and is as such not compatible with R.\"\n        })\n      }), _jsx(AccordionItem, {\n        header: \"Does Orange have oversampling, such as SMOTE?\",\n        children: _jsxs(_components.p, {\n          children: [\"No. There are reasons why\", \" \", \"\\n\", _jsx(Link, {\n            url: \"https://github.com/biolab/orange3/pull/3269\",\n            name: \"we refrain from implementing SMOTE\"\n          }), \" \", \"\\n. If you really wish to go ahead with it, you can use \", _jsx(\"a\", {\n            href: \"https://datascience.stackexchange.com/questions/50944/unbalanced-target-variable-in-orange-how-do-i-balance-it\",\n            children: \"Python Script.\"\n          })]\n        })\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}},"frontmatter":{"title":"FAQ","url":"/faq"}},"__N_SSG":true},"page":"/faq","query":{},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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index dced3ed57..ea6f9e15f 100644
--- a/getting-started/index.html
+++ b/getting-started/index.html
@@ -1,4 +1,4 @@
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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 eVggRJ"><section><div class="sc-61db3aea-0 eVggRJ"><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Download and install</h2><p>Download Orange distribution package and run the installation file on your local computer. Follow installation guides for your operating system.</p><a class="sc-87ede70a-0 gZWJQM" href="/download/">Download Orange</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1543" height="1044" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/interactive-viz1.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Widget catalog</h2><p>Orange widgets are building blocks of data analysis workflows that are assembled in Orange’s visual programming environment. Widgets are grouped into classes according to their function. A typical workflow may mix widgets for data input and filtering, visualization, and predictive data mining. Here you can get list of all widgets available in Orange.</p><a class="sc-87ede70a-0 gZWJQM" href="/widget-catalog/">Widget catalog</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1891" height="1339" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/markers-tsne.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Workflow examples</h2><p>Software Orange includes a wide array of workflow templates designed to help you get familiar with the application. Pick Templates on the Welcome screen to explore.</p></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1780" height="1404" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/template_workflow.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">YouTube tutorials</h2><p>Introduction to the Orange data mining software. Learn about the development of Orange workflows, data loading, basic machine learning algorithms and interactive visualizations. Video tutorials are available in button below.</p><a class="sc-87ede70a-0 gZWJQM" href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube tutorials</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1201" height="705" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/youtube_tutorials.png"/></figure></div></div></section></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Download and install","image":{"width":1543,"height":1044,"src":"/getting-started/interactive-viz1.png"},"buttonURL":"/download/","buttonText":"Download Orange","mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Download Orange distribution package and run the installation file on your local computer. 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+</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 eVggRJ"><section><div class="sc-61db3aea-0 eVggRJ"><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Download and install</h2><p>Download Orange distribution package and run the installation file on your local computer. Follow installation guides for your operating system.</p><a class="sc-87ede70a-0 gZWJQM" href="/download/">Download Orange</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1543" height="1044" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/interactive-viz1.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Widget catalog</h2><p>Orange widgets are building blocks of data analysis workflows that are assembled in Orange’s visual programming environment. Widgets are grouped into classes according to their function. A typical workflow may mix widgets for data input and filtering, visualization, and predictive data mining. Here you can get list of all widgets available in Orange.</p><a class="sc-87ede70a-0 gZWJQM" href="/widget-catalog/">Widget catalog</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1891" height="1339" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/markers-tsne.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Workflow examples</h2><p>Software Orange includes a wide array of workflow templates designed to help you get familiar with the application. Pick Templates on the Welcome screen to explore.</p></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1780" height="1404" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/template_workflow.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">YouTube tutorials</h2><p>Introduction to the Orange data mining software. Learn about the development of Orange workflows, data loading, basic machine learning algorithms and interactive visualizations. Video tutorials are available in button below.</p><a class="sc-87ede70a-0 gZWJQM" href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube tutorials</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="1201" height="705" decoding="async" data-nimg="1" style="color:transparent" src="/getting-started/youtube_tutorials.png"/></figure></div></div></section></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Download and install","image":{"width":1543,"height":1044,"src":"/getting-started/interactive-viz1.png"},"buttonURL":"/download/","buttonText":"Download Orange","mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Download Orange distribution package and run the installation file on your local computer. 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@@ -154,4 +154,4 @@ <h2>Intelligent Visualizations</h2>
 <a href="/home/[slug]/interactive_data_visualization/__optimized-images__/projection-ranking.thumb.png" data-gallery="true"><img alt="" loading="lazy" width="555" height="443" decoding="async" data-nimg="1" style="color:transparent" src="/home/[slug]/interactive_data_visualization/__optimized-images__/projection-ranking.thumb.png"/></a>
 <h2>Reporting</h2>
 <p>Finally, we can include the most important visualizations, statistics and information about the models into the report with a single click. Orange includes clever reporting where you can access workflow history for every widget and visualization directly from the report.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 cBZuFd"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu fkGYYn"> <!-- -->Orange users</h1><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h2>Education in Data Science</h2>
 <p>Orange is the perfect tool for hands-on training. Teachers enjoy the clear program design and the visual explorations of data and models. Students benefit from the flexibility of the tool and the power to invent new combinations of data mining methods. The educational strength of Orange comes from the combination of visual programming and interactive visualizations. We have also designed some educational widgets that have been explicitly created to support teaching.</p>
 <p>Here are a few example workflows that we have used recently in data mining training (yes, we do not only develop Orange, we teach with it as well).</p>
@@ -161,4 +161,4 @@ <h2>Experimenting with k-Means Clustering</h2>
 <a href="/home/[slug]/orange_users/__optimized-images__/play-with-k-means.thumb.png" data-gallery="true"><img alt="" loading="lazy" width="555" height="462" decoding="async" data-nimg="1" style="color:transparent" src="/home/[slug]/orange_users/__optimized-images__/play-with-k-means.thumb.png"/></a>
 <h2>Scoring of Clustering Models</h2>
 <p>We did mention the clustering silhouette, right? It is the easiest approach to score the clustering. Silhouettes are estimated on data instances, and the silhouette of a clustering is the mean across data instance silhouettes. A high silhouette means that a data instance is surrounded by instances from the same cluster, while a low silhouette score indicates that data instances are close to another cluster. Orange has a widget that can plot the silhouette scores. And because Orange is all about interactive visualization, you can select silhouettes and check where their data instances are. Like in the workflow below, where we showcase that low silhouettes are assigned to borderline data instances. Silhouette Plot is great when explaining pros and cons of different clustering methods (yes, it works with any method, not just k-means).</p>
-<a href="/home/[slug]/orange_users/__optimized-images__/silhouette.thumb.png" data-gallery="true"><img alt="" loading="lazy" width="555" height="322" decoding="async" data-nimg="1" style="color:transparent" src="/home/[slug]/orange_users/__optimized-images__/silhouette.thumb.png"/></a></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"title":"Orange users","weight":40,"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"Education in Data Science\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is the perfect tool for hands-on training. Teachers enjoy the clear program design and the visual explorations of data and models. Students benefit from the flexibility of the tool and the power to invent new combinations of data mining methods. The educational strength of Orange comes from the combination of visual programming and interactive visualizations. We have also designed some educational widgets that have been explicitly created to support teaching.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Here are a few example workflows that we have used recently in data mining training (yes, we do not only develop Orange, we teach with it as well).\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Linear Regression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Wouldn't be great if we could just paint data and with each new data point observe how linear regression fits the line? 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+<a href="/home/[slug]/orange_users/__optimized-images__/silhouette.thumb.png" data-gallery="true"><img alt="" loading="lazy" width="555" height="322" decoding="async" data-nimg="1" style="color:transparent" src="/home/[slug]/orange_users/__optimized-images__/silhouette.thumb.png"/></a></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"title":"Orange users","weight":40,"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h2: \"h2\",\n    p: \"p\"\n  }, _provideComponents(), props.components), {Figure} = _components;\n  if (!Figure) _missingMdxReference(\"Figure\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h2, {\n      children: \"Education in Data Science\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is the perfect tool for hands-on training. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 cBZuFd"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu fkGYYn"> <!-- -->Visual Programming</h1><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange is a great data mining tool for beginners as well as for expert data scientists. Thanks to its user interface users can focus on data analysis instead on laborious coding, making a construction of complex data analytics pipelines simple.</p>
 <h2>Component-Based Data Mining</h2>
 <p>In Orange, data analysis is done by stacking components into workflows. Each component, called a widget, embeds some data retrieval, preprocessing, visualization, modeling or evaluation task. Combining different widgets in a workflow enables you to build comprehensive data analysis schemas as you go. With a large library of widgets you won&#x27;t be short for choice. Additional widgets are available through add-ons and allow for a more focused and topic-oriented research.</p>
@@ -151,4 +151,4 @@ <h2>Interactive Data Exploration</h2>
 <p>Through the choice of the right widgets and their connections, it is easy to construct complex workflows for a broad variety of data analysis tasks.</p>
 <h2>Clever Workflow Design Interface</h2>
 <p>Orange is easy to use even for complete novices. Start with the File widget and Orange will automatically suggest the next widgets that can be connected to it. For example, Orange knows you are likely to want Hierarchical Clustering after you&#x27;ve set up your Distances widget. All other defaults in the widgets are also set in a way that enables a simple analysis even without knowing a whole lot about statistics, machine learning, or exploratory data mining in general.</p>
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For example, in a simple workflow below, where selection of the data in the spreadsheet propagates to a scatter plot, which marks the selected data instances.\\n\", _jsx(_components.br, {}), \"\\n\", \"\\\\\"]\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/home/[slug]/visual _programming/interactive-table-browser.thumb.png\",\n      width: \"555\",\n      height: \"391\",\n      src: \"/home/[slug]/visual _programming/__optimized-images__/interactive-table-browser.thumb.png\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"\\\\\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Through the choice of the right widgets and their connections, it is easy to construct complex workflows for a broad variety of data analysis tasks.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Clever Workflow Design Interface\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is easy to use even for complete novices. Start with the File widget and Orange will automatically suggest the next widgets that can be connected to it. For example, Orange knows you are likely to want Hierarchical Clustering after you've set up your Distances widget. All other defaults in the widgets are also set in a way that enables a simple analysis even without knowing a whole lot about statistics, machine learning, or exploratory data mining in general.\"\n    }), \"\\n\", _jsx(Figure, {\n      src: \"/home/[slug]/visual _programming/channel-completion.thumb.png\",\n      width: \"555\",\n      height: \"513\",\n      src: \"/home/[slug]/visual _programming/__optimized-images__/channel-completion.thumb.png\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n  throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{}}},"__N_SSG":true},"page":"/home/[slug]","query":{"slug":"visual-programming"},"buildId":"94QTGfoo8u2d9JarndNd4","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>
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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div><section class="sc-759389b1-0 kExdHC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-759389b1-1 kA-DvWL"><div class="sc-759389b1-2 fiHQsZ"><h1 class="sc-c67a4900-0 bPBwpI">Data Mining Fruitful and Fun</h1><p class="sc-759389b1-4 cwUnZq">Open source machine learning and data visualization.</p><a href="/download" class="sc-b6ea565a-0 jyxOJT">Download Orange <!-- -->3.36.1</a></div><figure class="sc-759389b1-3 lfiYvh"><img alt="" fetchpriority="high" width="810" height="471" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/orange_illustration_landing.58a920f0.png"/></figure></div></div></section><section class="sc-d3922312-0 sIQQE"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-d3922312-1 cVjHJu"><div class="sc-d3922312-2 dwrjVL"><p class="sc-d3922312-8 ibIPsD">Dec 22, 2023</p><h2><a href="/blog/cookie-mining-2023/">Cookie Mining</a></h2><p class="sc-89e95305-2 innjCp">A companion to our Orange Data Mining Holiday Special video on how we mined cookie descriptions and how to create cookie clustering.</p><a href="/blog/cookie-mining-2023/"><img alt="Cookie Mining" fetchpriority="high" width="512" height="512" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-12-cookie-mining-2023/__optimized-images__/cookie-mining-thumb.jpg"/></a></div><div class="sc-d3922312-3 cjFRvN"><div class="sc-d3922312-4 jFOLBW"><div class="sc-d3922312-5 SNkzN"><p class="sc-d3922312-8 iWxTEA">Dec 20, 2023</p><h3><a href="/blog/our-christmas-present-free-orange-t-shirt/">Our Christmas Present: Free Orange T-Shirt!</a></h3><p class="sc-d3922312-7 dzYBwb">Enroll in our survival analysis tutorial, and you might just unwrap a free Orange T-shirt along with a hefty dose of data science cheer!</p></div><figure class="sc-d3922312-6 kYYQpy"><a href="/blog/our-christmas-present-free-orange-t-shirt/"><img alt="Our Christmas Present: Free Orange T-Shirt!" fetchpriority="high" width="2000" height="2000" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-12-survival-analysis-tutorial/__optimized-images__/t-shirt.png"/></a></figure></div><div class="sc-d3922312-4 jFOLBW"><div class="sc-d3922312-5 SNkzN"><p class="sc-d3922312-8 iWxTEA">Nov 08, 2023</p><h3><a href="/blog/dask-survival-analysis/">From Data Portals to Portals of Doom: Avoiding it with Dask</a></h3><p class="sc-d3922312-7 dzYBwb">Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source&#x27;s authenticity and enabling custom workflows.</p></div><figure class="sc-d3922312-6 kYYQpy"><a href="/blog/dask-survival-analysis/"><img alt="From Data Portals to Portals of Doom: Avoiding it with Dask" fetchpriority="high" width="1396" height="854" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-11-dask-survival-analysis/__optimized-images__/orange-not-responding-thumb.png"/></a></figure></div><div class="sc-d3922312-4 jFOLBW"><div class="sc-d3922312-5 SNkzN"><p class="sc-d3922312-8 iWxTEA">Oct 28, 2023</p><h3><a href="/blog/dask-spectroscopy/">Can Orange explore a 13 GB data set?</a></h3><p class="sc-d3922312-7 dzYBwb">An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.</p></div><figure class="sc-d3922312-6 kYYQpy"><a href="/blog/dask-spectroscopy/"><img alt="Can Orange explore a 13 GB data set?" fetchpriority="high" width="591" height="352" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-dask-spectroscopy/__optimized-images__/du.png"/></a></figure></div></div></div></div></section><section><div class="sc-61db3aea-0 eVggRJ"><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Visual Programming</h2><p>Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight!</p><a class="sc-87ede70a-0 gZWJQM" href="/home/visual-programming/"> <!-- -->Learn more</a><a target="_blank" class="sc-87ede70a-0 gZWJQM" href="https://www.youtube.com/watch?v=H1ibqB_cvlE&amp;autoplay=1">Watch video</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="629" height="236" decoding="async" data-nimg="1" style="color:transparent" src="/home/visual_programming.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Interactive Data Visualization</h2><p>Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections.</p><a class="sc-87ede70a-0 gZWJQM" href="/home/interactive-data-visualization/"> <!-- -->Learn more</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="808" height="445" decoding="async" data-nimg="1" style="color:transparent" src="/home/interactive_data_visualization.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Add-ons Extend Functionality</h2><p>Use various add-ons available within Orange to mine data from external data sources, perform natural language processing and text mining, conduct network analysis, infer frequent itemset and do association rules mining. Additionally, bioinformaticians and molecular biologists can use Orange to rank genes by their differential expression and perform enrichment analysis. Check out also Orange cousins <a href="https://singlecell.biolab.si/">Single Cell</a> and <a href="https://quasar.codes/">Quasar</a>.</p><a target="_blank" class="sc-87ede70a-0 gZWJQM" href="https://www.youtube.com/watch?v=OANsA6fMJKg?start=6&amp;autoplay=1">Watch video</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="275" height="576" decoding="async" data-nimg="1" style="color:transparent" src="/home/add_ons.png"/></figure></div></div></section><section><div class="sc-61db3aea-0 eVggRJ"><div class="sc-94000d72-0 dCBAW"><div class="sc-94000d72-1 iWhSxi"><h2 class="sc-c67a4900-1 hGaqjR">Orange users</h2><p class="sc-c67a4900-2 hEbdcK">When teaching data mining, we like to illustrate rather than only explain. And Orange is great at that. Used at schools, universities and in professional training courses across the world, Orange supports hands-on training and visual illustrations of concepts from data science. There are even widgets that were especially designed for teaching.</p><a class="sc-87ede70a-0 gZWJQM" href="/home/orange-users/">Learn more</a></div><div class="sc-94000d72-2 dpYnje"><div class="keen-slider"><div class="sc-94000d72-3 WBWLb keen-slider__slide"><p>My laboratory produces large amounts of data from RNA-seq, ChIP-seq and genome resequencing experiments.  Orange allows me to analyze my data even though I don’t know how to program.  It also allows me to communicate with my collaborators, who are experts in data mining, and with my colleagues and trainees.</p><div class="sc-94000d72-4 ezxxnj"><img alt="" loading="lazy" width="500" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/home/Shaulsky_Gad_small.jpg"/><div><p><strong>Gad Shaulsky, Ph.D.</strong></p><p>Molecular biologist and Director of Graduate Studies<!-- --> (<!-- -->Baylor College of Medicine, Houston, USA<!-- -->)</p></div></div></div><div class="sc-94000d72-3 WBWLb keen-slider__slide"><p>The scientific community is in need of tools that allow easy construction of workflows and visualizations and are capable of analyzing large amounts of data. Orange is a powerful platform to perform data analysis and visualization, see data flow and become more productive. It provides a clean, open source platform and the possibility to add further functionality for all fields of science.</p><div class="sc-94000d72-4 ezxxnj"><img alt="" loading="lazy" width="500" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/home/Borondics_Ferenc_small.jpg"/><div><p><strong>Ferenc Borondics, Ph.D.</strong></p><p>Principal beamline scientist at SMIS<!-- --> (<!-- -->SOLEIL synchrotron, France<!-- -->)</p></div></div></div><div class="sc-94000d72-3 WBWLb keen-slider__slide"><p>I teach Orange workshops monthly to a diverse audience, from undergrad students to expert researchers. Orange is very intuitive, and, by the end of the workshop, the participants are able to perform complex data visualization and basic machine learning analyses. Most of our attendees have been able to incorporate this tool in their research practice.</p><div class="sc-94000d72-4 ezxxnj"><img alt="" loading="lazy" width="500" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/home/Vitali_Francesca_small.jpg"/><div><p><strong>Francesca Vitali, Ph.D.</strong></p><p>Research Assistant Professor<!-- --> (<!-- -->Center for Biomedical Informatics &amp; Biostatistics, The University of Arizona<!-- -->)</p></div></div></div></div></div></div></div></section><section class="sc-be182fa8-0 cnFKKh"><div class="sc-61db3aea-0 iMwRKb"><h2 class="sc-c67a4900-1 hGaqjR">Contribute to Orange</h2><p class="sc-c67a4900-2 hEbdcK">If you love using Orange and want to support us, a donation would be very much appreciated. The funds help us fix bugs, implement new features, provide free educational content, and maintain computational infrastructure.</p><a href="https://www.paypal.com/donate/?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" class="sc-b6ea565a-0 jyxOJT">Donate</a></div></section></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Visual Programming","image":{"width":629,"height":236,"src":"/home/visual_programming.png"},"learnMore":true,"weight":30,"video":"H1ibqB_cvlE","mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Interactive data exploration for rapid qualitative analysis with clean visualizations. 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It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.</p></div><figure class="sc-d3922312-6 kYYQpy"><a href="/blog/dask-spectroscopy/"><img alt="Can Orange explore a 13 GB data set?" fetchpriority="high" width="591" height="352" decoding="async" data-nimg="1" style="color:transparent" src="/blog/2023-10-dask-spectroscopy/__optimized-images__/du.png"/></a></figure></div></div></div></div></section><section><div class="sc-61db3aea-0 eVggRJ"><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Visual Programming</h2><p>Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight!</p><a class="sc-87ede70a-0 gZWJQM" href="/home/visual-programming/"> <!-- -->Learn more</a><a target="_blank" class="sc-87ede70a-0 gZWJQM" href="https://www.youtube.com/watch?v=H1ibqB_cvlE&amp;autoplay=1">Watch video</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="629" height="236" decoding="async" data-nimg="1" style="color:transparent" src="/home/visual_programming.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Interactive Data Visualization</h2><p>Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections.</p><a class="sc-87ede70a-0 gZWJQM" href="/home/interactive-data-visualization/"> <!-- -->Learn more</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="808" height="445" decoding="async" data-nimg="1" style="color:transparent" src="/home/interactive_data_visualization.png"/></figure></div><div class="sc-c2ee5a42-0 ecbiPN"><div class="sc-c2ee5a42-1 fSzYjK"><h2 class="sc-c67a4900-1 hGaqjR">Add-ons Extend Functionality</h2><p>Use various add-ons available within Orange to mine data from external data sources, perform natural language processing and text mining, conduct network analysis, infer frequent itemset and do association rules mining. Additionally, bioinformaticians and molecular biologists can use Orange to rank genes by their differential expression and perform enrichment analysis. Check out also Orange cousins <a href="https://singlecell.biolab.si/">Single Cell</a> and <a href="https://quasar.codes/">Quasar</a>.</p><a target="_blank" class="sc-87ede70a-0 gZWJQM" href="https://www.youtube.com/watch?v=OANsA6fMJKg?start=6&amp;autoplay=1">Watch video</a></div><figure class="sc-c2ee5a42-2 bDYsof"><img alt="" loading="lazy" width="275" height="576" decoding="async" data-nimg="1" style="color:transparent" src="/home/add_ons.png"/></figure></div></div></section><section><div class="sc-61db3aea-0 eVggRJ"><div class="sc-94000d72-0 dCBAW"><div class="sc-94000d72-1 iWhSxi"><h2 class="sc-c67a4900-1 hGaqjR">Orange users</h2><p class="sc-c67a4900-2 hEbdcK">When teaching data mining, we like to illustrate rather than only explain. And Orange is great at that. Used at schools, universities and in professional training courses across the world, Orange supports hands-on training and visual illustrations of concepts from data science. There are even widgets that were especially designed for teaching.</p><a class="sc-87ede70a-0 gZWJQM" href="/home/orange-users/">Learn more</a></div><div class="sc-94000d72-2 dpYnje"><div class="keen-slider"><div class="sc-94000d72-3 WBWLb keen-slider__slide"><p>My laboratory produces large amounts of data from RNA-seq, ChIP-seq and genome resequencing experiments.  Orange allows me to analyze my data even though I don’t know how to program.  It also allows me to communicate with my collaborators, who are experts in data mining, and with my colleagues and trainees.</p><div class="sc-94000d72-4 ezxxnj"><img alt="" loading="lazy" width="500" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/home/Shaulsky_Gad_small.jpg"/><div><p><strong>Gad Shaulsky, Ph.D.</strong></p><p>Molecular biologist and Director of Graduate Studies<!-- --> (<!-- -->Baylor College of Medicine, Houston, USA<!-- -->)</p></div></div></div><div class="sc-94000d72-3 WBWLb keen-slider__slide"><p>The scientific community is in need of tools that allow easy construction of workflows and visualizations and are capable of analyzing large amounts of data. Orange is a powerful platform to perform data analysis and visualization, see data flow and become more productive. It provides a clean, open source platform and the possibility to add further functionality for all fields of science.</p><div class="sc-94000d72-4 ezxxnj"><img alt="" loading="lazy" width="500" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/home/Borondics_Ferenc_small.jpg"/><div><p><strong>Ferenc Borondics, Ph.D.</strong></p><p>Principal beamline scientist at SMIS<!-- --> (<!-- -->SOLEIL synchrotron, France<!-- -->)</p></div></div></div><div class="sc-94000d72-3 WBWLb keen-slider__slide"><p>I teach Orange workshops monthly to a diverse audience, from undergrad students to expert researchers. Orange is very intuitive, and, by the end of the workshop, the participants are able to perform complex data visualization and basic machine learning analyses. Most of our attendees have been able to incorporate this tool in their research practice.</p><div class="sc-94000d72-4 ezxxnj"><img alt="" loading="lazy" width="500" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/home/Vitali_Francesca_small.jpg"/><div><p><strong>Francesca Vitali, Ph.D.</strong></p><p>Research Assistant Professor<!-- --> (<!-- -->Center for Biomedical Informatics &amp; Biostatistics, The University of Arizona<!-- -->)</p></div></div></div></div></div></div></div></section><section class="sc-be182fa8-0 cnFKKh"><div class="sc-61db3aea-0 iMwRKb"><h2 class="sc-c67a4900-1 hGaqjR">Contribute to Orange</h2><p class="sc-c67a4900-2 hEbdcK">If you love using Orange and want to support us, a donation would be very much appreciated. The funds help us fix bugs, implement new features, provide free educational content, and maintain computational infrastructure.</p><a href="https://www.paypal.com/donate/?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" class="sc-b6ea565a-0 jyxOJT">Donate</a></div></section></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Visual Programming","image":{"width":629,"height":236,"src":"/home/visual_programming.png"},"learnMore":true,"weight":30,"video":"H1ibqB_cvlE","mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {jsx: _jsx} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components);\n  return _jsx(_components.p, {\n    children: \"Interactive data exploration for rapid qualitative analysis with clean visualizations. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 ixZKYg"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu dXFGRw"> <!-- -->License</h1><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><p>Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, together with open source community.</p>
 <p>Orange is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3.0 of the License, or (at your option) any later version.</p>
 <p>Orange is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.</p>
 <p>Orange documentation, content on its website and other non-code content are all available under Creative Commons Attribution-ShareAlike license unless specified otherwise. Attribution should be made to Orange, Data Mining Fruitful &amp; Fun, with a link to its website <a href="https://orange.biolab.si/">https://orange.biolab.si/</a>.</p>
 <p>Orange Widgets and Canvas are based on Qt, which is distributed under GPL 3.0 (as well as LGPL 2.1, see <a href="https://www.qt.io/licensing/">https://www.qt.io/licensing/</a>.</p>
 <p>Orange add-ons may have additional licensing requirements. Please, see their respective license files.</p>
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+<p>If you want to cite Orange or give it an attribution, you can find more information <a href="/citation/">here</a>.</p></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\"\n  }, _provideComponents(), props.components), {Link} = _components;\n  if (!Link) _missingMdxReference(\"Link\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, together with open source community.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3.0 of the License, or (at your option) any later version.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Orange is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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 <p>Laboratory of Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana (&#x27;&#x27;us&#x27;&#x27;, &#x27;&#x27;we&#x27;&#x27;, or &#x27;&#x27;ours&#x27;&#x27;) operates https://orangedatamining.com (the &#x27;&#x27;Site&#x27;&#x27;). This page informs you of our policies regarding the collection, use and disclosure of Personal Information we receive from users of the Site.</p>
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@@ -155,4 +155,4 @@ <h2>Changes to this Privacy Policy</h2>
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+<p>If you have any questions about this Privacy Policy, please contact us through the <a href="/contact/">contact form</a> on our website.</p></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    p: \"p\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components), {Link} = _components;\n  if (!Link) _missingMdxReference(\"Link\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"Last updated: 26.10.2023\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Laboratory of Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana (''us'', ''we'', or ''ours'') operates https://orangedatamining.com (the ''Site''). 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-</style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><h1 class="sc-89e95305-0 sc-ba990baf-0 iNzSUu dXFGRw"> <!-- -->Screenshots</h1><div class="lg-react-element "><ul class="sc-9737fb3c-0 dGMvau"><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/file.png" data-gallery="true"><img alt="Load and edit your data in the File widget." loading="lazy" width="555" height="485" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/file.thumb.png"/></a><h2>Load and edit your data in the File widget.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/paint-data.png" data-gallery="true"><img alt="Paint a two-dimensional data set." loading="lazy" width="555" height="356" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/paint-data.thumb.png"/></a><h2>Paint a two-dimensional data set.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/explorative-analysis.png" data-gallery="true"><img alt="Data selection in Scatter Plot is visualised in a Box Plot." loading="lazy" width="555" height="311" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/explorative-analysis.thumb.png"/></a><h2>Data selection in Scatter Plot is visualised in a Box Plot.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/suggestion.png" data-gallery="true"><img alt="Orange can suggest which widget to add to the workflow." loading="lazy" width="555" height="410" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/suggestion.thumb.png"/></a><h2>Orange can suggest which widget to add to the workflow.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/concatenate.png" data-gallery="true"><img alt="Join two data sets." loading="lazy" width="555" height="327" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/concatenate.thumb.png"/></a><h2>Join two data sets.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/box-plot.png" data-gallery="true"><img alt="Box plot displays basic statistics of attributes." loading="lazy" width="555" height="291" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/box-plot.thumb.png"/></a><h2>Box plot displays basic statistics of attributes.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/sieve-diagram.png" data-gallery="true"><img alt="Sieve diagram on Titanic data set." loading="lazy" width="555" height="477" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/sieve-diagram.thumb.png"/></a><h2>Sieve diagram on Titanic data set.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/heat-map.png" data-gallery="true"><img alt="Heatmap visualisation." loading="lazy" width="555" height="387" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/heat-map.thumb.png"/></a><h2>Heatmap visualisation.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/tree-explorative.png" data-gallery="true"><img alt="Explorative analysis with classification trees." loading="lazy" width="555" height="355" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/tree-explorative.thumb.png"/></a><h2>Explorative analysis with classification trees.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/image-viewer.png" data-gallery="true"><img alt="Data can contain references to images." loading="lazy" width="555" height="330" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/image-viewer.thumb.png"/></a><h2>Data can contain references to images.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/hierarchical-clustering.png" data-gallery="true"><img alt="Hierarchial clustering supports interactive cluster selection." loading="lazy" width="555" height="455" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/hierarchical-clustering.thumb.png"/></a><h2>Hierarchial clustering supports interactive cluster selection.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/k-means.png" data-gallery="true"><img alt="Playing with Paint Data and an automatic selection of clusters in k-Means." loading="lazy" width="555" height="404" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/k-means.thumb.png"/></a><h2>Playing with Paint Data and an automatic selection of clusters in k-Means.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/mds.png" data-gallery="true"><img alt="Multidimensional scaling of Zoo data set reveals phylogeny groups." loading="lazy" width="555" height="322" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/mds.thumb.png"/></a><h2>Multidimensional scaling of Zoo data set reveals phylogeny groups.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/pca.png" data-gallery="true"><img alt="Principal component analysis with scree diagram." loading="lazy" width="555" height="448" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/pca.thumb.png"/></a><h2>Principal component analysis with scree diagram.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/roc.png" data-gallery="true"><img alt="Receiver operating characteristics (ROC) analysis." loading="lazy" width="555" height="404" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/roc.thumb.png"/></a><h2>Receiver operating characteristics (ROC) analysis.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/calibration-plot.png" data-gallery="true"><img alt="Cross-validated calibration plot." loading="lazy" width="555" height="414" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/calibration-plot.thumb.png"/></a><h2>Cross-validated calibration plot.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/pca-evaluation.png" data-gallery="true"><img alt="Data preprocessing embedded within a learning algorithm." loading="lazy" width="555" height="374" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/pca-evaluation.thumb.png"/></a><h2>Data preprocessing embedded within a learning algorithm.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/rank.png" data-gallery="true"><img alt="Feature scoring for finding interesting data projections." loading="lazy" width="555" height="345" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/rank.thumb.png"/></a><h2>Feature scoring for finding interesting data projections.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/model-based-scoring.png" data-gallery="true"><img alt="Model-based feature scoring." loading="lazy" width="555" height="300" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/model-based-scoring.thumb.png"/></a><h2>Model-based feature scoring.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/evaluation.png" data-gallery="true"><img alt="Cross-validated calibration plot." loading="lazy" width="555" height="380" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/evaluation.thumb.png"/></a><h2>Cross-validated calibration plot.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/misclassifications.png" data-gallery="true"><img alt="Visualizing misclassifications." loading="lazy" width="555" height="386" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/misclassifications.thumb.png"/></a><h2>Visualizing misclassifications.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/venn-misclassified.png" data-gallery="true"><img alt="Finding common misclassifications of three predictive models." loading="lazy" width="555" height="343" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/venn-misclassified.thumb.png"/></a><h2>Finding common misclassifications of three predictive models.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/test-train.png" data-gallery="true"><img alt="Model testing and scoring on a separate test data set." loading="lazy" width="555" height="327" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/test-train.thumb.png"/></a><h2>Model testing and scoring on a separate test data set.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/silhouette.png" data-gallery="true"><img alt="Intersection of misclassified data and data with low silhouette score." loading="lazy" width="555" height="313" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/silhouette.thumb.png"/></a><h2>Intersection of misclassified data and data with low silhouette score.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/cn2.png" data-gallery="true"><img alt="CN2 rule induction." loading="lazy" width="555" height="345" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/cn2.thumb.png"/></a><h2>CN2 rule induction.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/polynomial-regression.png" data-gallery="true"><img alt="Showcase for approximation by regression tree." loading="lazy" width="555" height="346" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/polynomial-regression.thumb.png"/></a><h2>Showcase for approximation by regression tree.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/gradient-descent.png" data-gallery="true"><img alt="Interactive gradient descent." loading="lazy" width="555" height="493" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/gradient-descent.thumb.png"/></a><h2>Interactive gradient descent.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/text-predictions.png" data-gallery="true"><img alt="Predicting text categories." loading="lazy" width="555" height="322" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/text-predictions.thumb.png"/></a><h2>Predicting text categories.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/text-topics.png" data-gallery="true"><img alt="Topic modelling of recent tweets." loading="lazy" width="555" height="358" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/text-topics.thumb.png"/></a><h2>Topic modelling of recent tweets.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/image-analytics.png" data-gallery="true"><img alt="Image analytics with deep-network embedding." loading="lazy" width="555" height="334" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/image-analytics.thumb.png"/></a><h2>Image analytics with deep-network embedding.</h2></li></ul></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a 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src="/screenshots/paint-data.thumb.png"/></a><h2>Paint a two-dimensional data set.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/explorative-analysis.png" data-gallery="true"><img alt="Data selection in Scatter Plot is visualised in a Box Plot." loading="lazy" width="555" height="311" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/explorative-analysis.thumb.png"/></a><h2>Data selection in Scatter Plot is visualised in a Box Plot.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/suggestion.png" data-gallery="true"><img alt="Orange can suggest which widget to add to the workflow." loading="lazy" width="555" height="410" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/suggestion.thumb.png"/></a><h2>Orange can suggest which widget to add to the workflow.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/concatenate.png" data-gallery="true"><img alt="Join two data sets." loading="lazy" width="555" height="327" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/concatenate.thumb.png"/></a><h2>Join two data sets.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/box-plot.png" data-gallery="true"><img alt="Box plot displays basic statistics of attributes." loading="lazy" width="555" height="291" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/box-plot.thumb.png"/></a><h2>Box plot displays basic statistics of attributes.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/sieve-diagram.png" data-gallery="true"><img alt="Sieve diagram on Titanic data set." loading="lazy" width="555" height="477" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/sieve-diagram.thumb.png"/></a><h2>Sieve diagram on Titanic data set.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/heat-map.png" data-gallery="true"><img alt="Heatmap visualisation." loading="lazy" width="555" height="387" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/heat-map.thumb.png"/></a><h2>Heatmap visualisation.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/tree-explorative.png" data-gallery="true"><img alt="Explorative analysis with classification trees." loading="lazy" width="555" height="355" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/tree-explorative.thumb.png"/></a><h2>Explorative analysis with classification trees.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/image-viewer.png" data-gallery="true"><img alt="Data can contain references to images." loading="lazy" width="555" height="330" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/image-viewer.thumb.png"/></a><h2>Data can contain references to images.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/hierarchical-clustering.png" data-gallery="true"><img alt="Hierarchial clustering supports interactive cluster selection." loading="lazy" width="555" height="455" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/hierarchical-clustering.thumb.png"/></a><h2>Hierarchial clustering supports interactive cluster selection.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/k-means.png" data-gallery="true"><img alt="Playing with Paint Data and an automatic selection of clusters in k-Means." loading="lazy" width="555" height="404" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/k-means.thumb.png"/></a><h2>Playing with Paint Data and an automatic selection of clusters in k-Means.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/mds.png" data-gallery="true"><img alt="Multidimensional scaling of Zoo data set reveals phylogeny groups." loading="lazy" width="555" height="322" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/mds.thumb.png"/></a><h2>Multidimensional scaling of Zoo data set reveals phylogeny groups.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/pca.png" data-gallery="true"><img alt="Principal component analysis with scree diagram." loading="lazy" width="555" height="448" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/pca.thumb.png"/></a><h2>Principal component analysis with scree diagram.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/roc.png" data-gallery="true"><img alt="Receiver operating characteristics (ROC) analysis." loading="lazy" width="555" height="404" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/roc.thumb.png"/></a><h2>Receiver operating characteristics (ROC) analysis.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/calibration-plot.png" data-gallery="true"><img alt="Cross-validated calibration plot." loading="lazy" width="555" height="414" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/calibration-plot.thumb.png"/></a><h2>Cross-validated calibration plot.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/pca-evaluation.png" data-gallery="true"><img alt="Data preprocessing embedded within a learning algorithm." loading="lazy" width="555" height="374" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/pca-evaluation.thumb.png"/></a><h2>Data preprocessing embedded within a learning algorithm.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/rank.png" data-gallery="true"><img alt="Feature scoring for finding interesting data projections." loading="lazy" width="555" height="345" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/rank.thumb.png"/></a><h2>Feature scoring for finding interesting data projections.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/model-based-scoring.png" data-gallery="true"><img alt="Model-based feature scoring." loading="lazy" width="555" height="300" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/model-based-scoring.thumb.png"/></a><h2>Model-based feature scoring.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/evaluation.png" data-gallery="true"><img alt="Cross-validated calibration plot." loading="lazy" width="555" height="380" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/evaluation.thumb.png"/></a><h2>Cross-validated calibration plot.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/misclassifications.png" data-gallery="true"><img alt="Visualizing misclassifications." loading="lazy" width="555" height="386" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/misclassifications.thumb.png"/></a><h2>Visualizing misclassifications.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/venn-misclassified.png" data-gallery="true"><img alt="Finding common misclassifications of three predictive models." loading="lazy" width="555" height="343" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/venn-misclassified.thumb.png"/></a><h2>Finding common misclassifications of three predictive models.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/test-train.png" data-gallery="true"><img alt="Model testing and scoring on a separate test data set." loading="lazy" width="555" height="327" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/test-train.thumb.png"/></a><h2>Model testing and scoring on a separate test data set.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/silhouette.png" data-gallery="true"><img alt="Intersection of misclassified data and data with low silhouette score." loading="lazy" width="555" height="313" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/silhouette.thumb.png"/></a><h2>Intersection of misclassified data and data with low silhouette score.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/cn2.png" data-gallery="true"><img alt="CN2 rule induction." loading="lazy" width="555" height="345" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/cn2.thumb.png"/></a><h2>CN2 rule induction.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/polynomial-regression.png" data-gallery="true"><img alt="Showcase for approximation by regression tree." loading="lazy" width="555" height="346" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/polynomial-regression.thumb.png"/></a><h2>Showcase for approximation by regression tree.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/gradient-descent.png" data-gallery="true"><img alt="Interactive gradient descent." loading="lazy" width="555" height="493" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/gradient-descent.thumb.png"/></a><h2>Interactive gradient descent.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/text-predictions.png" data-gallery="true"><img alt="Predicting text categories." loading="lazy" width="555" height="322" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/text-predictions.thumb.png"/></a><h2>Predicting text categories.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/text-topics.png" data-gallery="true"><img alt="Topic modelling of recent tweets." loading="lazy" width="555" height="358" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/text-topics.thumb.png"/></a><h2>Topic modelling of recent tweets.</h2></li><li class="sc-9737fb3c-1 fChJAg"><a href="/screenshots/image-analytics.png" data-gallery="true"><img alt="Image analytics with deep-network embedding." loading="lazy" width="555" height="334" decoding="async" data-nimg="1" style="color:transparent" src="/screenshots/image-analytics.thumb.png"/></a><h2>Image analytics with deep-network embedding.</h2></li></ul></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" 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 <p>Induction of association rules.</p>
 <p><strong>Inputs</strong></p>
@@ -209,4 +209,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/associate/images/__optimized-images__/association-rules-example1.png" data-gallery="true"><img alt="" loading="lazy" width="1313" height="934" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/associate/images/__optimized-images__/association-rules-example1.png"/></a>
 <h2>References and further reading</h2>
 <p>[1]: J. Han, J. Pei, Y. Yin, R. Mao. (2004) <a href="https://www.cs.sfu.ca/~jpei/publications/dami03_fpgrowth.pdf">Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach</a>.</p>
-<p>[2]: R. Agrawal, C. Aggarwal, V. Prasad. (2000) <a href="http://www.cs.tau.ac.il/~fiat/dmsem03/Depth%20First%20Generation%20of%20Long%20Patterns%20-%202000.pdf">Depth first generation of long patterns</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Association Rules\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Induction of association rules.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matching Data: Data instances matching the criteria.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget implements FP-growth \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Association_rule_learning\",\n        children: \"frequent pattern mining\"\n      }), \" algorithm [1] with bucketing optimization [2] for conditional databases of few items. 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+<p>[2]: R. Agrawal, C. Aggarwal, V. Prasad. (2000) <a href="http://www.cs.tau.ac.il/~fiat/dmsem03/Depth%20First%20Generation%20of%20Long%20Patterns%20-%202000.pdf">Depth first generation of long patterns</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Association Rules\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Induction of association rules.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matching Data: Data instances matching the criteria.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget implements FP-growth \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Association_rule_learning\",\n        children: \"frequent pattern mining\"\n      }), \" algorithm [1] with bucketing optimization [2] for conditional databases of few items. 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 <p>Finds frequent itemsets in the data.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@
 </ol>
 <h2>Example</h2>
 <p>Frequent Itemsets can be used directly with the <a href="/widget-catalog/associate/../data/file">File</a> widget.</p>
-<a href="/widget-catalog/associate/images/__optimized-images__/frequent-itemsets-example1.png" data-gallery="true"><img alt="" loading="lazy" width="1201" height="797" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/associate/images/__optimized-images__/frequent-itemsets-example1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Frequent Itemsets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finds frequent itemsets in the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matching Data: Data instances matching the criteria.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget finds \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Association_rule_learning\",\n        children: \"frequent items\"\n      }), \" in a data set based on a measure of\\nsupport for the rule.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/associate/images/__optimized-images__/frequent-itemsets-stamped.png\",\n      alt: \"\",\n      width: \"647\",\n      height: \"435\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the data set. 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 <p>The widget provides an option to annotate cells with cell types based on
 marker genes.</p>
@@ -253,4 +253,4 @@ <h2>Examples</h2>
 them in the reference space. The Annotator window shows the mapping of
 secondary data (colored points) to clusters generated on the reference
 data.</p>
-<a href="/widget-catalog/bioinformatics/images/annotate_projection/annotator-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1138" height="1162" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/annotate_projection/annotator-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Annotator\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget provides an option to annotate cells with cell types based on\\nmarker genes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reference Data: Data set with gene expression values.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Secondary Data: Subset of instances (optional).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Genes: Marker genes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: Instances selected from the plot.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Data with additional columns with annotations, clusters,\\nand projection\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget receives gene expression data together with mapping to a\\ntwo-dimensional space and marker genes. 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+<a href="/widget-catalog/bioinformatics/images/annotate_projection/annotator-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1138" height="1162" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/annotate_projection/annotator-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Annotator\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget provides an option to annotate cells with cell types based on\\nmarker genes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reference Data: Data set with gene expression values.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Secondary Data: Subset of instances (optional).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Genes: Marker genes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: Instances selected from the plot.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Data with additional columns with annotations, clusters,\\nand projection\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget receives gene expression data together with mapping to a\\ntwo-dimensional space and marker genes. 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@@ -184,4 +184,4 @@
 <h2>Example</h2>
 <p>From the <strong>GEO Data Sets</strong> widget we select the <a href="https://pubmed.ncbi.nlm.nih.gov/15718313/">GDS360</a> data set for gene expression in breast cancer core biopsies taken from patients found to be resistant (14 samples) or sensitive (10 samples) to docetaxel treatment. Next we feed the data to the <strong>Cluster Analysis</strong> widget and set our parameters and gene sets to compare. We set the thresholds to filter the results and connect the widget to the <strong>t-SNE</strong> widget to visualize the results and to the <strong>Data Table</strong> widget to inspect the results.</p>
 <a href="/widget-catalog/bioinformatics/images/cluster_analysis/Cluster-Analysis-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1808" height="965" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/cluster_analysis/Cluster-Analysis-Example.png"/></a>
-<p>This workflow can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_cluster_analysis.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a 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fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Cluster Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Display differentially expressed genes that characterize the cluster.\"\n    }), \"\\n\", _jsx(_components.p, {\n      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+<p>This workflow can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_cluster_analysis.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Databases Update</h1>
 <p>Updates local systems biology databases, like gene ontologies,
 annotations, gene names, protein interaction networks, and similar.</p>
@@ -172,4 +172,4 @@
 <li>Add a data set from the local machine.</li>
 </ol>
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-<p>To add a new file to the database, select the domain and the organism of the data. Give the data set a name and, optionally, tag it with appropriate tags. Finally, use the <em>Select File</em> button to load the local file. Press OK to complete the process. The data will be stored in a cached folder <strong>locally</strong>. To see the full path to the data, hover on the data set name.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Databases Update\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Updates local systems biology databases, like gene ontologies,\\nannotations, gene names, protein interaction networks, and similar.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With the bioinformatics add-on you can access several databases directly from Orange. 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+<p>To add a new file to the database, select the domain and the organism of the data. Give the data set a name and, optionally, tag it with appropriate tags. Finally, use the <em>Select File</em> button to load the local file. Press OK to complete the process. The data will be stored in a cached folder <strong>locally</strong>. To see the full path to the data, hover on the data set name.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Databases Update\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Updates local systems biology databases, like gene ontologies,\\nannotations, gene names, protein interaction networks, and similar.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"With the bioinformatics add-on you can access several databases directly from Orange. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>dictyExpress</h1>
 <p>Gives access to <a href="https://dictyexpress.research.bcm.edu">dictyExpress</a>
 databases.</p>
@@ -173,4 +173,4 @@
 </ol>
 <h2>Example</h2>
 <p><strong>dictyExpress</strong> widget can be used to retrieve data from a database, just like <strong>GEO Data Sets</strong> and similar to the <strong>File</strong> widget. We have retrieved the <em>D. discoideum vs. D. purpureum</em> data and sent it to the output by pressing <em>Commit</em>. We have observed the data in a <strong>Data Table</strong> and in a <strong>Heat Map</strong>, where we used <em>Merge by k-means</em> and clustering by rows to find similar genes.</p>
-<a href="/widget-catalog/bioinformatics/images/dicty_express/dictyExpress-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2656" height="1856" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/dicty_express/dictyExpress-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"dictyExpress\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gives access to \", _jsx(_components.a, {\n        href: \"https://dictyexpress.research.bcm.edu\",\n        children: \"dictyExpress\"\n      }), \"\\ndatabases.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Selected experiment (time-course gene expression data).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"dictyExpress\"\n      }), \" widget gives a direct access to the \", _jsx(_components.a, {\n        href: \"https://dictyexpress.research.bcm.edu\",\n        children: \"dictyExpress\"\n      }), \" database. 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 <p>Plots differential gene expression for selected experiments.</p>
 <p><strong>Inputs</strong></p>
@@ -215,4 +215,4 @@
 <h2>Example</h2>
 <p>From the GEO Data Sets widget, we selected <em>Breast cancer and docetaxel treatment</em> (GDS360) with 14 treatment resistant and 10 treatment sensitive tumors. Then we used the <strong>Differential Expression</strong> widget to select the most interesting genes. We left the upper and lower threshold at default (1 and -1) and output the data. Then we observed the selected data subset in a <strong>Data Table</strong>. The table shows selected genes with an additional gene score label.</p>
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-<p>A workflow that implements this widget can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_homologs.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Differential Expression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots differential gene expression for selected experiments.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: Differentially expressed genes.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Remaining Data Subset: Genes that were not differentially expressed.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Genes: Genes from the select data with scores appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget plots a \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/books/NBK10061/\",\n        children: \"differential gene\\nexpression\"\n      }), \" graph for a\\nsample target. 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+<p>A workflow that implements this widget can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_homologs.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Differential Expression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots differential gene expression for selected experiments.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: Differentially expressed genes.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Remaining Data Subset: Genes that were not differentially expressed.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Genes: Genes from the select data with scores appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget plots a \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/books/NBK10061/\",\n        children: \"differential gene\\nexpression\"\n      }), \" graph for a\\nsample target. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Gene Set Enrichment</h1>
 <p>Enrich gene sets.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@
 </ol>
 <h2>Example</h2>
 <p>From <strong>GEO Data Sets</strong> widget we select the <a href="https://pubmed.ncbi.nlm.nih.gov/21410935/">GDS3900</a> data set <em>Fear conditioning effect on hybrid mouse diversity panel</em>. The analysis focuses on the whole hippocampus and striatum from Hybrid Mouse Diversity Panel (HMDP) males exposed to a fear conditioning procedure. Next, we feed this data into the <strong>Gene Set Enrichment</strong> widget, where we specify <em>Mus musculus</em> as the <em>Organism</em> and select the Gene Ontology gene sets. After filtering the results, we proceed to connect the widget to the <strong>Data Table</strong> to inspect the <em>Enrichment report</em>. This workflow can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_gsea.ows">here</a>.</p>
-<a href="/widget-catalog/bioinformatics/images/gene_set_enrichment/Gene-Set-Enrichment-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1883" height="890" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/gene_set_enrichment/Gene-Set-Enrichment-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gene Set Enrichment\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Enrich gene sets.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Custom Gene Sets: Genes to compare.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference Genes: Genes used as reference.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matched Genes: Genes that match.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Gene_set_enrichment_analysis\",\n        children: \"Gene Set Enrichment Analysis\"\n      }), \" (GSEA) is a method to find gene sets overrepresented in a large group of genes, possibly associated with different phenotypes. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Gene Sets</h1>
 <p>Lists of genes associated with specific biological function.</p>
 <p><strong>Inputs</strong></p>
@@ -177,4 +177,4 @@ <h2>Example</h2>
 <p>To create a custom gene set, we start by importing an Excel table that includes 284 human homologs of DNA &quot;damage-up&quot; proteins (DDPs) identified in <em>E.coli</em>. These DDPs are categorized into three gene sets: All DDPs, DDPs with known cancer drivers excluded, and validated DDPs recognized as genuine DNA damage instigators in human cells <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6344048/">(Xia et al.)</a>. We accomplish this by utilizing the <strong>File</strong> widget. Next, we pass this data to the <strong>Genes</strong> widget for gene annotation, and then to the <strong>Gene Sets</strong> widget for custom gene set creation. Within the <strong>Gene Sets</strong> widget, we can select our file from the drop-down menu in the <em>Custom Gene Sets</em> section to generate the desired gene set. This workflow can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_gene_sets.ows">here</a>.</p>
 <a href="/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-example.png" data-gallery="true"><img alt="" loading="lazy" width="1708" height="996" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-example.png"/></a>
 <p>Next, we load the <a href="https://pubmed.ncbi.nlm.nih.gov/20040092/"><em>GDS3592</em></a> data set from the <strong>GEO Data Sets</strong> widget. This is a comparison of gene expressions in 12 normal ovarian surface epithelia and 12 ovarian cancer epithelial cells. To annotate the genes, we utilize the <strong>Genes</strong> widget. Once the genes are annotated, we connect both the <strong>Genes</strong> and <strong>Gene Sets</strong> (with our custom gene set selected) widgets to the <strong>Single Sample Scoring</strong> widget. Finally, we use <strong>Data Table</strong> widget to observe the enrichment scores associated with the selected gene sets.</p>
-<a href="/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1873" height="932" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gene Sets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Lists of genes associated with specific biological function.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Custom Gene Sets: Genes to compare.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Instances with gene metadata within the selected gene sets.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Gene Sets: Selected gene sets for utilization in other widgets (e.g., Single Sample Scoring).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gene sets are lists of genes associated with a specific biological function. The \", _jsx(_components.strong, {\n        children: \"Gene Sets\"\n      }), \" widget allows you to select gene sets from multiple databases, including the \", _jsx(_components.a, {\n        href: \"http://software.broadinstitute.org/gsea/msigdb\",\n        children: \"MSigDB\"\n      }), \", \", _jsx(_components.a, {\n        href: \"http://geneontology.org/\",\n        children: \"Gene Ontology\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.genome.jp/kegg/\",\n        children: \"KEGG\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"https://reactome.org/\",\n        children: \"Reactome\"\n      }), \" databases, or to create your own custom gene sets. You can also select gene sets and feed it to other widgets.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Data is preprocessed in Orange readable format and it is hosted \", _jsx(_components.a, {\n        href: \"http://download.biolab.si/datasets/bioinformatics/gene_sets/\",\n        children: \"here.\"\n      })]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-stamped.png\",\n      alt: \"\",\n      width: \"1054\",\n      height: \"573\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select the \", _jsx(_components.em, {\n          children: \"Organism\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Gene Sets\"\n        }), \": A list of available gene sets. Select multiple gene sets by ticking the checkbox next to the gene set name. 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+<a href="/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1873" height="932" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/gene-sets/Gene-Sets-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gene Sets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Lists of genes associated with specific biological function.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Custom Gene Sets: Genes to compare.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Instances with gene metadata within the selected gene sets.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Gene Sets: Selected gene sets for utilization in other widgets (e.g., Single Sample Scoring).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Gene sets are lists of genes associated with a specific biological function. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Genes</h1>
 <p>Match input gene ID&#x27;s with corresponding Entrez ID&#x27;s.</p>
 <p><strong>Inputs</strong></p>
@@ -180,4 +180,4 @@
 <h2>Example</h2>
 <p>First we load <em>brown-selected.tab</em> (from <em>Browse documentation data sets</em>) with the <strong>File</strong> widget and feed our data to the Genes widget. Orange recognized the organism correctly, but we have to tell it where our gene labels are. To do this, we tick off <em>Stored as feature (column) name</em> and select <em>gene</em> attribute from the list. Then we can observe gene info provided from the NCBI Gene database. In the <strong>Data Table</strong> we can see the Entrez ID column included as a meta attribute. The data is also properly annotated (see <em>Data Attributes</em> section in <strong>Data Info</strong> widget).</p>
 <a href="/widget-catalog/bioinformatics/images/genes/genes-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2948" height="1530" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/genes/genes-Example.png"/></a>
-<p>A workflow that implements this widget can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_gene_sets.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Genes\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Match input gene ID's with corresponding Entrez ID's.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Instances with meta data that the user has manually selected in the widget.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Genes: All genes from the input with included gene info summary and matcher result.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To work with widgets in the bioinformatics add-on data sets must be\\nproperly annotated. 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+<p>A workflow that implements this widget can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_gene_sets.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Genes\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Match input gene ID's with corresponding Entrez ID's.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Instances with meta data that the user has manually selected in the widget.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Genes: All genes from the input with included gene info summary and matcher result.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To work with widgets in the bioinformatics add-on data sets must be\\nproperly annotated. 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 <p>Provides access to data sets from gene expression omnibus <a href="http://www.ncbi.nlm.nih.gov/gds">GEO
 DataSets</a>.</p>
@@ -193,4 +193,4 @@
 </ol>
 <h2>Example</h2>
 <p><strong>GEO Data Sets</strong> is similar to the <strong>File</strong> widget, since it is used to load the data. In the example below we selected <em>Caffeine effect: time course and dose response</em> dataset from the GEO data base. Do not forget to press <em>Commit</em> to output the data. We can inspect the data in <em>Data Table</em>.</p>
-<a href="/widget-catalog/bioinformatics/images/geo_data_sets/GEO-Data-Sets-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2982" height="1548" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/geo_data_sets/GEO-Data-Sets-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"GEO Data Sets\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Provides access to data sets from gene expression omnibus \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/gds\",\n        children: \"GEO\\nDataSets\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Expression data: Data set selected in the widget with genes or samples in rows.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/gds\",\n        children: \"GEO DataSets\"\n      }), \" is a database of gene\\nexpression curated profiles maintained by\\n\", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/\",\n        children: \"NCBI\"\n      }), \" and included in the \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/geo/info/datasets.html\",\n        children: \"Gene\\nExpression Omnibus\"\n      }), \".\\nThis Orange widget provides access to all its data sets and outputs a\\ndata set selected for further processing. 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+<a href="/widget-catalog/bioinformatics/images/geo_data_sets/GEO-Data-Sets-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2982" height="1548" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/geo_data_sets/GEO-Data-Sets-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"GEO Data Sets\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Provides access to data sets from gene expression omnibus \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/gds\",\n        children: \"GEO\\nDataSets\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Expression data: Data set selected in the widget with genes or samples in rows.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/gds\",\n        children: \"GEO DataSets\"\n      }), \" is a database of gene\\nexpression curated profiles maintained by\\n\", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/\",\n        children: \"NCBI\"\n      }), \" and included in the \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/geo/info/datasets.html\",\n        children: \"Gene\\nExpression Omnibus\"\n      }), \".\\nThis Orange widget provides access to all its data sets and outputs a\\ndata set selected for further processing. 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 <p>Provides access to Gene Ontology database.</p>
 <p><strong>Inputs</strong></p>
@@ -274,4 +274,4 @@ <h2>Example</h2>
 this term has a high enrichment rate. To learn more about which genes
 are annotated to this GO term, select it in the view and observe the results in a <strong>Data Table</strong>, where we
 see all the genes participating in this process listed. The other output of <strong>GO Browser</strong> widget is enrichment report, which we observe in the second <strong>Data Table</strong>.</p>
-<a href="/widget-catalog/bioinformatics/images/go_browser/GO-Browser-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3208" height="1580" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/go_browser/GO-Browser-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"GO Browser\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Provides access to Gene Ontology database.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Cluster Data: Data on clustered genes.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference Data: Data with genes for the reference set (optional).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data on Selected Genes: Data on genes from the selected GO node.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Enrichment Report: Data on GO enrichment analysis.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"GO Browser\"\n      }), \" widget provides access to \", _jsx(_components.a, {\n        href: \"http://geneontology.org/\",\n        children: \"Gene Ontology database\"\n      }), \". 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Differential\\nanalysis allows us to select genes with the highest statistical\\nrelevance (we used ANOVA scoring and agent label) and feed them to \", _jsx(_components.strong, {\n        children: \"GO Browser\"\n      }), \". This\\nwidget lists four biological processes for our selected genes. Say we\\nare interested in finding out more about \", _jsx(_components.em, {\n        children: \"monosaccharide transmembrane transport\"\n      }), \" as\\nthis term has a high enrichment rate. To learn more about which genes\\nare annotated to this GO term, select it in the view and observe the results in a \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \", where we\\nsee all the genes participating in this process listed. The other output of \", _jsx(_components.strong, {\n        children: \"GO Browser\"\n      }), \" widget is enrichment report, which we observe in the second \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/bioinformatics/images/go_browser/GO-Browser-Example.png\",\n      alt: \"\",\n      width: \"3208\",\n      height: \"1580\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File 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You can either have the \", _jsx(_components.em, {\n            children: \"entire genome\"\n          }), \" as\\nreference or a \", _jsx(_components.em, {\n            children: \"reference set\"\n          }), \" from the input.\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Select the ontology where you want to calculate the enrichment.\\nThere are three \", _jsx(_components.em, {\n            children: \"Aspect\"\n          }), \" options:\"]\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.a, {\n              href: \"http://geneontology.org/page/biological-process-ontology-guidelines\",\n              children: \"Biological\\nprocess\"\n            })\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"http://geneontology.org/page/cellular-component-ontology-guidelines\",\n              children: \"Cellular\\ncomponent\"\n            }), \")\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.a, {\n              href: \"http://geneontology.org/page/molecular-function-ontology-guidelines\",\n              children: \"Molecular\\nfunction\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"A ranked tree (upper pane) and list (lower pane) of GO terms for the\\nselected aspect:\"\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.strong, {\n              children: \"GO term\"\n            })\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Cluster\"\n            }), \": number of genes from the input that are also\\nannotated to a particular GO term (and its proportion in all the\\ngenes from that term).\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Reference\"\n            }), \": number of genes that are annotated to a\\nparticular GO term (and its proportion in the entire genome).\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"P-value\"\n            }), \": probability of seeing as many or more genes at\\nrandom. The closer the p-value is to zero, the more significant\\na particular GO term is. Value is written in \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Scientific_notation#E_notation\",\n              children: \"e\\nnotation\"\n            }), \").\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"FDR\"\n            }), \": \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/False_discovery_rate\",\n              children: \"false discovery\\nrate\"\n            }), \" - a\\nmultiple testing correction that means a proportion of false\\ndiscoveries among all discoveries up to that FDR value.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Genes\"\n            }), \": genes in a biological process.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: 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[_jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Binomial_distribution\",\n              children: \"Binomial\"\n            }), \":\\nuse a binomial distribution\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Hypergeometric_distribution\",\n              children: \"Hypergeometric\"\n            }), \":\\nuse a hypergeometric distribution\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.a, {\n            href: \"http://geneontology.org/page/guide-go-evidence-codes\",\n            children: \"Evidence codes in\\nannotation\"\n          }), \"\\nshow how the annotation to a particular term is supported.\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"SELECT tab\"\n      })\n    }), \"\\n\", _jsxs(_components.ol, {\n      start: \"4\",\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.em, {\n            children: \"Annotated genes\"\n          }), \" outputs genes that are:\"]\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Directly or Indirectly\"\n            }), \" annotated (direct and inherited\\nannotations)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Directly\"\n            }), \" annotated (inherited annotations won't be in the\\noutput)\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.em, {\n            children: \"Output\"\n          }), \":\"]\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"All selected genes\"\n            }), \": outputs genes annotated to all selected\\nGO terms\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Term-specific genes\"\n            }), \": outputs genes that appear in only one\\nof selected GO terms\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Common term genes\"\n            }), \": outputs genes common to all selected GO\\nterms\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Add GO Term as class\"\n            }), \": adds GO terms as class attribute\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the example below we have used \", _jsx(_components.strong, {\n        children: \"GEO Data Sets\"\n      }), \" widget, in which we\\nhave selected \", _jsx(_components.em, {\n        children: \"Caffeine effects: time course and dose response\"\n      }), \" data set, and connected it to a \", _jsx(_components.strong, {\n        children: \"Differential Expression\"\n      }), \". Differential\\nanalysis allows us to select genes with the highest statistical\\nrelevance (we used ANOVA scoring and agent label) and feed them to \", _jsx(_components.strong, {\n        children: \"GO Browser\"\n      }), \". This\\nwidget lists four biological processes for our selected genes. Say we\\nare interested in finding out more about \", _jsx(_components.em, {\n        children: \"monosaccharide transmembrane transport\"\n      }), \" as\\nthis term has a high enrichment rate. To learn more about which genes\\nare annotated to this GO term, select it in the view and observe the results in a \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \", where we\\nsee all the genes participating in this process listed. The other output of \", _jsx(_components.strong, {\n        children: \"GO Browser\"\n      }), \" widget is enrichment report, which we observe in the second \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/bioinformatics/images/go_browser/GO-Browser-Example.png\",\n      alt: \"\",\n      width: \"3208\",\n      height: \"1580\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Homologs</h1>
 <p>Finds homologs to genes in the input data set.</p>
 <p><strong>Inputs</strong></p>
@@ -171,4 +171,4 @@
 </ol>
 <h2>Example</h2>
 <p>From the <strong>GEO Data Sets</strong> widget we select the <a href="https://pubmed.ncbi.nlm.nih.gov/17975176/">GDS3132</a> gene expression data set for neonatal lung response to cigarette smoke profiling in mice (<em>Mus musculus</em>). Next, we annotate the genes using the <strong>Genes</strong> widget. We then connect the <strong>Genes</strong> and <strong>Differential Expression</strong> widgets to inspect the differentially expressed genes. We select the top 100 genes and feed the data to <strong>Homologs</strong> widget and select the organism of interest, in our case <em>Homo Sapiens</em>. The output is a list of homologs to the genes in the input data set. Next, we connect the <strong>Homologs</strong> widget to the <strong>Data Table</strong> widget. This allows us to efficiently inspect and work with information specifically related to homologous genes. This workflow can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_homologs.ows">here</a>.</p>
-<a href="/widget-catalog/bioinformatics/images/homologs/Homologs-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1836" height="944" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/homologs/Homologs-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Homologs\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finds homologs to genes in the input data set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Genes: List of homologs.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Homologs\"\n      }), \" widget finds homologs to genes in the input data set for the selected organism. \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Sequence_homology\",\n        children: \"Homologs\"\n      }), \" are genes or proteins that share a common ancestry, often found in different species, and may have similar functions or sequences. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>KEGG Pathways</h1>
 <p>Diagrams of molecular interactions, reactions, and relations.</p>
 <p><strong>Inputs</strong></p>
@@ -189,4 +189,4 @@
 </ol>
 <h2>Example</h2>
 <p>This simple example shows how to visualize interactions with <strong>KEGG Pathways</strong>. We have loaded the <em>Caffeine effect: time courses and dose response</em> (GDS2914) data with the <strong>GEO Data Sets</strong> widget. Then we have observed the pathways in <strong>KEGG Pathways</strong>. We have used reference from signal and selected <em>AGE-RAGE signaling pathway in diabetic complications</em>.</p>
-<a href="/widget-catalog/bioinformatics/images/kegg_pathways/KEGG-Pathways-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2964" height="1442" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/kegg_pathways/KEGG-Pathways-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"KEGG Pathways\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Diagrams of molecular interactions, reactions, and relations.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference: Referential data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: Data subset.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Unselected Data: Remaining data.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"KEGG Pathways\"\n      }), \" widget displays diagrams of molecular interactions,\\nreactions and relations from the \", _jsx(_components.a, {\n        href: \"http://www.genome.jp/kegg/pathway.html\",\n        children: \"KEGG Pathways\\nDatabase\"\n      }), \". 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Untick the box if\\nyou wish to explore the pathways.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"To clear all locally cached KEGG data, press \", _jsx(_components.em, {\n          children: \"Clear cache\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"When \", _jsx(_components.em, {\n          children: \"Auto commit is on\"\n        }), \", the widget will automatically apply the\\nchanges. Alternatively press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"A list of pathways either as processes or in full orthology. Click\\non the process to display the pathway. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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@@ -186,4 +186,4 @@
 <h2>Example</h2>
 <p>We can use the <strong>Marker Genes</strong> in a combination with many other widgets. For example, we can use it to annotate cell types in the <strong>Annotator</strong> widget. In order to do that, we first need to load the data. From <strong>GEO Data Sets</strong> widget we select the <a href="https://pubmed.ncbi.nlm.nih.gov/21410935/">GDS3900</a> data set <em>Fear conditioning effect on hybrid mouse diversity panel</em> and feed the data to the <strong>t-SNE</strong> widget. We add the <strong>Marker Genes</strong> widget to annotate the cell types, select <em>Mouse</em> as <em>Organism</em> and select all marker genes. Finally, we connect both widgets to the <strong>Annotator</strong> widget to visualize the results.</p>
 <a href="/widget-catalog/bioinformatics/images/marker_genes/Marker-Genes-example.png" data-gallery="true"><img alt="" loading="lazy" width="1789" height="991" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/marker_genes/Marker-Genes-example.png"/></a>
-<p>A workflow that implements this widget can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_annotator.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a 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fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Marker Genes\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Widget provides access to a public database of marker genes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: 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[\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"CellMarker\"\n        }), \"\\n\", _jsxs(_components.p, {\n          children: [_jsx(_components.a, {\n            href: \"https://academic.oup.com/nar/article/47/D1/D721/5115823\",\n            children: \"CellMarker: a manually curated resource of cell markers in human and mouse.\"\n          }), \" Nucleic Acids Research. 2018.\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Data is preprocessed in Orange readable format and it is hosted \", _jsx(_components.a, {\n        href: \"http://download.biolab.si/datasets/bioinformatics/marker_genes/\",\n        children: \"here.\"\n      }), \" One can use \", _jsx(_components.a, {\n        href: \"/widget-catalog/bioinformatics/databases_update\",\n        children: \"Databases update\"\n      }), \"\\nwidget to download (or update) files locally.\"]\n    }), \"\\n\", 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You can see the genes in each group by expanding it.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select marker genes by selecting \", _jsx(_components.em, {\n          children: \"Available markers\"\n        }), \" and moving them to \", _jsx(_components.em, {\n          children: \"Selected markers\"\n        }), \" by clicking the arrow in between.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"For more details about a particular marker gene, simply click on it to view information in the \", _jsx(_components.em, {\n          children: \"Description\"\n        }), \" panel.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"When \", _jsx(_components.em, {\n          children: \"Commit automatically\"\n        }), \" is ticked, the widget will automatically apply the changes. 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 <p>Scoring gene sets by single sample.</p>
 <p><strong>Inputs</strong></p>
@@ -172,4 +172,4 @@
 <h2>Example</h2>
 <p>From the GEO Data Sets widget, we selected <em>Breast cancer and docetaxel treatment</em> (<a href="https://pubmed.ncbi.nlm.nih.gov/15718313/">GDS360</a>) data set with 14 treatment resistant and 10 treatment sensitive tumors. We annotated the genes using the <strong>Genes</strong> widget and selected the relevant gene sets using the <strong>Gene Sets</strong> widget. Finally, we used the <strong>Single Sample Scoring</strong> widget and the <strong>Data Table</strong> widget to observe the enrichment scores associated with the selected gene sets.</p>
 <a href="/widget-catalog/bioinformatics/images/single_sample_scoring/Single-Sample-Scoring-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1629" height="855" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/single_sample_scoring/Single-Sample-Scoring-Example.png"/></a>
-<p>A workflow that implements this widget can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_gene_sets.ows">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Single Sample Scoring\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scoring gene sets by single sample.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Custom Gene Sets: Gene sets to be used for scoring.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set with enrichment scores corresponding to the selected gene sets.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"single-sample\"\n      }), \" extension of Gene Set Enrichment Analysis (\", _jsx(_components.a, {\n        href: \"https://www.nature.com/articles/nature08460\",\n        children: \"ssGSEA\"\n      }), \") is a widely used method that assigns scores to gene sets based on individual sample gene overexpression. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/databases_update/">Databases Update</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/geo_data_sets/">GEO Data Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/dicty_express/">dictyExpress</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/genes/">Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/differential_expression/">Differential Expression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/go_browser/">GO Browser</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/kegg_pathways/">KEGG Pathways</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_set_enrichment/">Gene Set Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/gene_sets/">Gene Sets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/cluster_analysis/">Cluster Analysis</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/bioinformatics/volcano_plot/">Volcano Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/homologs/">Homologs</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/annotate_projection/">Annotator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/bioinformatics/single_sample_scoring/">Single sample scoring</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Volcano Plot</h1>
 <p>Plots significance versus fold-change for gene expression rates.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@
 </ol>
 <h2>Example</h2>
 <p>From the GEO Data Sets widget, we select <em>Breast cancer and docetaxel treatment</em> (GDS360) with 14 treatment resistant and 10 treatment sensitive tumors. We select <em>Genes in rows</em> as output. Then we use the <strong>Volcano plot</strong> widget to select the most interesting genes. We observe the selected data subset in a <strong>Data Table</strong>. The table shows selected genes with the additional log2 (ratio) and -log10 (P_value) columns. To visualize the difference in expression of each selected gene, we first transpose the data using <strong>Transpose</strong> widget and then use the <strong>Box Plot</strong> widget. This workflow can be accessed <a href="https://download.biolab.si/download/files/workflows/orange/bioinformatics_volcano_plot.ows">here</a>.</p>
-<a href="/widget-catalog/bioinformatics/images/volcano_plot/Volcano-Plot-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1893" height="972" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/bioinformatics/images/volcano_plot/Volcano-Plot-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Volcano Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots significance versus fold-change for gene expression rates.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: Data subset.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Volcano_plot_(statistics)\",\n        children: _jsx(_components.strong, {\n          children: \"Volcano plot\"\n        })\n      }), \" is a graphical method for\\nvisualizing changes in replicate data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/file/">File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/csvfileimport/">CSV File Import</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datasets/">Datasets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/sqltable/">SQL Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datatable/">Data Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/paintdata/">Paint Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datainfo/">Data Info</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/rank/">Rank</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/editdomain/">Edit Domain</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/data/color/">Color</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/featurestatistics/">Feature Statistics</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/save/">Save Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Color</h1>
 <p>Set color legend for variables.</p>
 <p><strong>Inputs</strong></p>
@@ -193,4 +193,4 @@ <h2>Example</h2>
 <p>We chose to work with the <em>heart_disease</em> data set. We opened the color palette and selected two new colors for diameter narrowing variable. Then we opened the <a href="/widget-catalog/data/../visualize/scatterplot">Scatter Plot</a> widget and viewed the changes made to the scatter plot.</p>
 <a href="/widget-catalog/data/images/__optimized-images__/Color-Example-Discrete.png" data-gallery="true"><img alt="" loading="lazy" width="2886" height="1830" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/Color-Example-Discrete.png"/></a>
 <p>To see the effect of color palettes for numeric variables, we color the points in the scatter plot by cholesterol and change the palette for this attribute in the Color widget.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/Color-Example-Continuous.png" data-gallery="true"><img alt="" loading="lazy" width="2886" height="1830" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/Color-Example-Continuous.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Color\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Set color legend for variables.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: data set with a new color legend\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Color\"\n      }), \" widget sets the color legend for visualizations.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Color-stamped.png\",\n      alt: \"\",\n      width: \"741\",\n      height: \"562\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"A list of discrete variables. Set the color of each variable by double-clicking on it. The widget also enables renaming variables by clicking on their names.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"A list of continuous variables. Click on the color strip to choose a different palette. To use the same palette for all variables, change it for one variable and click \", _jsx(_components.em, {\n          children: \"Copy to all\"\n        }), \" that appears on the right. The widget also enables renaming variables by clicking on their names.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Apply changes. If \", _jsx(_components.em, {\n          children: \"Apply automatically\"\n        }), \" is ticked, changes will be communicated automatically. Alternatively, just click \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Color-Continuous_unindexed.png\",\n      alt: \"\",\n      width: \"306\",\n      height: \"320\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Palettes for numeric variables are grouped and tagged by their properties.\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Diverging palettes have two colors on its ends and a central color (white or black) in the middle. Such palettes are particularly useful when the the values can be positive or negative, as some widgets (for instance the Heat map) will put the 0 at the middle point in the palette.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Linear palettes are constructed so that human perception of the color change is linear with the change of the value.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Color blind palettes cover different types of color blindness, and can also be linear or diverging.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"In isoluminant palettes, all colors have equal brightness.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Rainbow palettes are particularly nice in widgets that bin numeric values in visualizations.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We chose to work with the \", _jsx(_components.em, {\n        children: \"heart_disease\"\n      }), \" data set. We opened the color palette and selected two new colors for diameter narrowing variable. Then we opened the \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/../visualize/scatterplot\",\n        children: \"Scatter Plot\"\n      }), \" widget and viewed the changes made to the scatter plot.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Color-Example-Discrete.png\",\n      alt: \"\",\n      width: \"2886\",\n      height: \"1830\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To see the effect of color palettes for numeric variables, we color the points in the scatter plot by cholesterol and change the palette for this attribute in the Color widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Color-Example-Continuous.png\",\n      alt: \"\",\n      width: \"2886\",\n      height: \"1830\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), 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 <p>Import a data table from a CSV formatted file.</p>
 <p><strong>Outputs</strong></p>
@@ -212,4 +212,4 @@ <h3>Encoding</h3>
 <a href="/widget-catalog/data/images/__optimized-images__/CSVFileImport-encodings.png" data-gallery="true"><img alt="" loading="lazy" width="332" height="255" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/CSVFileImport-encodings.png"/></a>
 <h2>Example</h2>
 <p><strong>CSV File Import</strong> works almost exactly like the <a href="../data/file.md">File</a> widget, with the added options for importing different types of .csv files. In this workflow, the widget read the data from the file and sends it to the <a href="/widget-catalog/data/../data/datatable">Data Table</a> for inspection.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/CSVFileImport-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2540" height="1632" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/CSVFileImport-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    a: \"a\",\n    code: \"code\",\n    h3: \"h3\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"CSV File Import\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Import a data table from a CSV formatted file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset from the .csv file\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Frame: pandas DataFrame object\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"CSV File Import\"\n      }), \" widget reads comma-separated files and sends the dataset to its output channel. 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 <p>Displays information on a selected dataset.</p>
 <p><strong>Inputs</strong></p>
@@ -171,4 +171,4 @@
 </ol>
 <h2>Example</h2>
 <p>Below, we compare the basic statistics of two <strong>Data Info</strong> widgets - one with information on the entire dataset and the other with information on the (manually) selected subset from the <a href="/widget-catalog/data/../visualize/scatterplot">Scatter Plot</a> widget. We used the <em>Iris</em> dataset.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/DataInfo-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1169" height="766" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/DataInfo-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Data Info\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays information on a selected dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A simple widget that presents information on dataset size, features,\\ntargets, meta attributes, and location.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/data-info-stamped.png\",\n      alt: \"\",\n      width: \"211\",\n      height: \"344\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on dataset size\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on discrete and continuous features\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on targets\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on meta attributes\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on where the data is stored\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Below, we compare the basic statistics of two \", _jsx(_components.strong, {\n        children: \"Data Info\"\n      }), \" widgets - one with information on the entire dataset and the other with information on the (manually) selected subset from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/../visualize/scatterplot\",\n        children: \"Scatter Plot\"\n      }), \" widget. 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 <p>Load a dataset from an online repository.</p>
 <p><strong>Outputs</strong></p>
@@ -168,4 +168,4 @@
 </ol>
 <h2>Example</h2>
 <p>Orange workflows can start with <strong>Datasets</strong> widget instead of <strong>File</strong> widget. In the example below, the widget retrieves a dataset from an online repository (Kickstarter data), which is subsequently sent to both the <a href="../data/datatable">Data Table</a> and the <a href="/widget-catalog/data/../visualize/distributions">Distributions</a>.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/Datasets-Workflow.png" data-gallery="true"><img alt="" loading="lazy" width="2892" height="1968" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/Datasets-Workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Datasets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Load a dataset from an online repository.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: output dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget retrieves selected dataset from the server and sends it to the output. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/file/">File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/csvfileimport/">CSV File Import</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datasets/">Datasets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/sqltable/">SQL Table</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/data/datatable/">Data Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/paintdata/">Paint Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datainfo/">Data Info</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/rank/">Rank</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/editdomain/">Edit Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/color/">Color</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/featurestatistics/">Feature Statistics</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/save/">Save Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Data Table</h1>
 <p>Displays attribute-value data in a spreadsheet.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@ <h2>Example</h2>
 <p>We used two <a href="/widget-catalog/data/../data/file">File</a> widgets to read the <em>Iris</em> and <em>Glass</em> dataset (provided in Orange distribution), and send them to the <strong>Data Table</strong> widget.</p>
 <a href="/widget-catalog/data/images/__optimized-images__/DataTable-Schema.png" data-gallery="true"><img alt="" loading="lazy" width="516" height="212" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/DataTable-Schema.png"/></a>
 <p>Selected data instances in the first <strong>Data Table</strong> are passed to the second <strong>Data Table</strong>. Notice that we can select which dataset to view (iris or glass). Changing from one dataset to another alters the communicated selection of data instances if <em>Commit on any change</em> is selected.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/DataTable-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1449" height="936" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/DataTable-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Data Table\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays attribute-value data in a spreadsheet.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the table\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \" widget receives one or more datasets in its input and presents them as a spreadsheet. 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 <p>Rename features and their values.</p>
 <p><strong>Inputs</strong></p>
@@ -190,4 +190,4 @@
 </ul>
 <h2>Example</h2>
 <p>Below, we demonstrate how to simply edit an existing domain. We selected the <em>heart_disease.tab</em> dataset and edited the <em>gender</em> attribute. Where in the original we had the values <em>female</em> and <em>male</em>, we changed it into <em>F</em> for female and <em>M</em> for male. Then we used the down key to switch the order of the variables. Finally, we added a label to mark that the attribute is binary. We can observe the edited data in the <a href="/widget-catalog/data/../data/datatable">Data Table</a> widget.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/EditDomain-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2264" height="1284" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/EditDomain-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Edit Domain\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Rename features and their values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with edited domain\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget can be used to edit/change a dataset's domain - rename features, rename or merge values of categorical features, add a categorical value, and assign labels.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/EditDomain-stamped.png\",\n      alt: \"\",\n      width: \"638\",\n      height: \"600\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"All features (including meta attributes) from the input dataset are listed in the \", _jsx(_components.em, {\n          children: \"Variables\"\n        }), \" list. 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+<a href="/widget-catalog/data/images/__optimized-images__/EditDomain-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2264" height="1284" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/EditDomain-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Edit Domain\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Rename features and their values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with edited domain\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget can be used to edit/change a dataset's domain - rename features, rename or merge values of categorical features, add a categorical value, and assign labels.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/EditDomain-stamped.png\",\n      alt: \"\",\n      width: \"638\",\n      height: \"600\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"All features (including meta attributes) from the input dataset are listed in the \", _jsx(_components.em, {\n          children: \"Variables\"\n        }), \" list. 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 <p>Show basic statistics for data features.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@ <h2>Example</h2>
 <p>Once we have found a subset of potentially interesting features, or we have found features that we would like to exclude, we can simply select the features we want to keep. The widget outputs a new data set with only these features.</p>
 <a href="/widget-catalog/data/images/__optimized-images__/feature_statistics_example1.png" data-gallery="true"><img alt="" loading="lazy" width="1655" height="872" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/feature_statistics_example1.png"/></a>
 <p>Alternatively, if we want to store feature statistics, we can use the <em>Statistics</em> output and manipulate those values as needed. In this example, we display the statistics in a table.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/feature_statistics_example2.png" data-gallery="true"><img alt="" loading="lazy" width="1569" height="888" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/feature_statistics_example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    em: \"em\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Feature Statistics\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Show basic statistics for data features.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reduced data: table containing only selected features\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Statistics: table containing statistics of the selected features\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Feature Statistics\"\n      }), \" widget provides a quick way to inspect and find interesting features in a given data set.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/feature_statistics-stamped.png\",\n      alt: \"\",\n      width: \"1044\",\n      height: \"529\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The Feature Statistics widget on the \", _jsx(_components.em, {\n        children: \"heart-disease\"\n      }), \" data set.\"]\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"The feature type - can be categorical, numeric, time and string.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"The name of the feature.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"The histogram showing the distribution of feature's values. 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@@ -192,4 +192,4 @@ <h3>Other Formats</h3>
 <li>text/corpus: Corpus or Import Documents from Text add-on</li>
 <li>single cell data: Load Data from Single Cell add-on</li>
 <li>several spectroscopy files: Multifile from Spectroscopy add-on</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h3: \"h3\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"File\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Reads attribute-value data from an input file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset from the file\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/visual-programming/loading-your-data/index.html\",\n        children: \"reads the input data file\"\n      }), \" (data table with data instances) and sends the dataset to its output channel. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/file/">File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/csvfileimport/">CSV File Import</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datasets/">Datasets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/sqltable/">SQL Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datatable/">Data Table</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/data/paintdata/">Paint Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datainfo/">Data Info</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/rank/">Rank</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/editdomain/">Edit Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/color/">Color</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/featurestatistics/">Feature Statistics</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/save/">Save Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Paint Data</h1>
 <p>Paints data on a 2D plane. You can place individual data points or use a brush to paint larger datasets.</p>
 <p><strong>Outputs</strong></p>
@@ -170,4 +170,4 @@
 </ol>
 <h2>Example</h2>
 <p>In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that <a href="../unsupervised/kmeans.md">k-Means</a>, overall, recognizes clusters better than <a href="/widget-catalog/data/../unsupervised/hierarchicalclustering">Hierarchical Clustering</a>. It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/PaintData-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1709" height="1187" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/PaintData-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Paint Data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Paints data on a 2D plane. You can place individual data points or use a brush to paint larger datasets.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset as painted in the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget supports the creation of a new dataset by visually placing data points on a two-dimension plane. Data points can be placed on the plane individually (\", _jsx(_components.em, {\n        children: \"Put\"\n      }), \") or in a larger number by brushing (\", _jsx(_components.em, {\n        children: \"Brush\"\n      }), \"). 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/file/">File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/csvfileimport/">CSV File Import</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datasets/">Datasets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/sqltable/">SQL Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datatable/">Data Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/paintdata/">Paint Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datainfo/">Data Info</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/data/rank/">Rank</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/editdomain/">Edit Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/color/">Color</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/featurestatistics/">Feature Statistics</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/save/">Save Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Rank</h1>
 <p>Ranking of attributes in classification or regression datasets.</p>
 <p><strong>Inputs</strong></p>
@@ -202,4 +202,4 @@ <h2>Example: Attribute Ranking and Selection</h2>
 <h2>Example: Feature Subset Selection for Machine Learning</h2>
 <p>What follows is a bit more complicated example. In the workflow below, we first split the data into a training set and a test set. In the upper branch, the training data passes through the <strong>Rank</strong> widget to select the most informative attributes, while in the lower branch there is no feature selection. Both feature selected and original datasets are passed to their own <a href="/widget-catalog/data/../evaluate/testandscore">Test &amp; Score</a> widgets, which develop a <em>Naive Bayes</em> classifier and score it on a test set.</p>
 <a href="/widget-catalog/data/images/__optimized-images__/Rank-and-Test.png" data-gallery="true"><img alt="" loading="lazy" width="1352" height="820" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/Rank-and-Test.png"/></a>
-<p>For datasets with many features, a naive Bayesian classifier feature selection, as shown above, would often yield a better predictive accuracy.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Rank\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ranking of attributes in classification or regression datasets.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scorer: models for feature scoring\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reduced Data: dataset with selected attributes\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: data table with feature scores\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: list of attributes\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Rank\"\n      }), \" widget scores variables according to their correlation with discrete or numeric target variable, based on applicable internal scorers (like information gain, chi-square and linear regression) and any connected external models that supports scoring, such as linear regression, logistic regression, random forest, SGD, etc. The widget can also handle unsupervised data, but only by external scorers, such as PCA.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Rank-stamped.png\",\n      alt: \"\",\n      width: \"820\",\n      height: \"502\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select scoring methods. See the options for classification, regression and unsupervised data in the \", _jsx(_components.strong, {\n          children: \"Scoring methods\"\n        }), \" section.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select attributes to output. \", _jsx(_components.em, {\n          children: \"None\"\n        }), \" won't output any attributes, while \", _jsx(_components.em, {\n          children: \"All\"\n        }), \" will output all of them. With manual selection, select the attributes from the table on the right. \", _jsx(_components.em, {\n          children: \"Best ranked\"\n        }), \" will output n best ranked attributes.\\nIf \", _jsx(_components.em, {\n          children: \"Send Automatically\"\n        }), \" is ticked, the widget automatically communicates changes to other widgets.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Status bar. Produce a report by clicking on the file icon. 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On the right, warnings and errors are shown.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Scoring methods (classification)\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information Gain: the expected amount of information (reduction of entropy)\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Information_gain_ratio\",\n          children: \"Gain Ratio\"\n        }), \": a ratio of the information gain and the attribute's intrinsic information, which reduces the bias towards multivalued features that occurs in information gain\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Gini_coefficient\",\n          children: \"Gini\"\n        }), \": the inequality among values of a frequency distribution\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\",\n          children: \"ANOVA\"\n        }), \": the difference between average values of the feature in different classes\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Chi-squared_distribution\",\n          children: \"Chi2\"\n        }), \": dependence between the feature and the class as measured by the chi-square statistic\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Relief_(feature_selection)\",\n          children: \"ReliefF\"\n        }), \": the ability of an attribute to distinguish between classes on similar data instances\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://www.aaai.org/Papers/ICML/2003/ICML03-111.pdf\",\n          children: \"FCBF (Fast Correlation Based Filter)\"\n        }), \": entropy-based measure, which also identifies redundancy due to pairwise correlations between features\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Additionally, you can connect certain learners that enable scoring the features according to how important they are in models that the learners build (e.g. \", _jsx(_components.a, {\n        href: \"../model/logisticregression.md\",\n        children: \"Logistic Regression\"\n      }), \", \", _jsx(_components.a, {\n        href: \"../model/randomforest.md\",\n        children: \"Random Forest\"\n      }), \", \", _jsx(_components.a, {\n        href: \"/widget-catalog/data/../model/stochasticgradient\",\n        children: \"SGD\"\n      }), \"). 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+<p>For datasets with many features, a naive Bayesian classifier feature selection, as shown above, would often yield a better predictive accuracy.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Rank\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Ranking of attributes in classification or regression datasets.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scorer: models for feature scoring\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reduced Data: dataset with selected attributes\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: data table with feature scores\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: list of attributes\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Rank\"\n      }), \" widget scores variables according to their correlation with discrete or numeric target variable, based on applicable internal scorers (like information gain, chi-square and linear regression) and any connected external models that supports scoring, such as linear regression, logistic regression, random forest, SGD, etc. The widget can also handle unsupervised data, but only by external scorers, such as PCA.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Rank-stamped.png\",\n      alt: \"\",\n      width: \"820\",\n      height: \"502\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select scoring methods. 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 <p>Saves data to a file.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@
 </ul>
 <h2>Example</h2>
 <p>In the workflow below, we used the <em>Zoo</em> dataset. We loaded the data into the <a href="/widget-catalog/data/../visualize/scatterplot">Scatter Plot</a> widget, with which we selected a subset of data instances and pushed them to the <strong>Save Data</strong> widget to store them in a file.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/Save-Workflow.png" data-gallery="true"><img alt="" loading="lazy" width="860" height="935" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/Save-Workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Save Data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Saves data to a file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Save Data\"\n      }), \" widget considers a dataset provided in the input channel and saves it to a data file with a specified name. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/file/">File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/csvfileimport/">CSV File Import</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datasets/">Datasets</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/data/sqltable/">SQL Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datatable/">Data Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/paintdata/">Paint Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/datainfo/">Data Info</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/rank/">Rank</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/editdomain/">Edit Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/color/">Color</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/featurestatistics/">Feature Statistics</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/data/save/">Save Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>SQL Table</h1>
 <p>Reads data from an SQL database.</p>
 <p><strong>Outputs</strong></p>
@@ -189,4 +189,4 @@ <h3>MSSQL</h3>
 <h2>Example</h2>
 <p>Here is a simple example on how to use the <strong>SQL Table</strong> widget. Place the widget on the canvas, enter your database credentials and connect to your database. Then select the table you wish to analyse.</p>
 <p>Connect <strong>SQL Table</strong> to <a href="../data/datatable.md">Data Table</a> widget to inspect the output. If the table is populated, your data has transferred correctly. Now, you can use the <strong>SQL Table</strong> widget in the same way as the <a href="/widget-catalog/data/../data/file">File</a> widget.</p>
-<a href="/widget-catalog/data/images/__optimized-images__/SQLTable-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1672" height="1460" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/data/images/__optimized-images__/SQLTable-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h3: \"h3\",\n    pre: \"pre\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"SQL Table\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Reads data from an SQL database.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset from the database\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"SQL\"\n      }), \" widget accesses data stored in an SQL database. 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 <p>Import data from EnKlikAnketa (1ka.si) public URL.</p>
 <p><strong>Outputs</strong></p>
@@ -173,4 +173,4 @@ <h2>Example</h2>
 <p><strong>EnKlik Anketa</strong> widget is great for observing results from online surveys. We have created a sample survey and imported it into the widget. We have 41 responses and we have asked 8 questions, 7 of which were recognized as features and 1 as a meta attribute.</p>
 <p>The widget sets questions from the survey as feature names. This, however, might be slightly impractical for analytical purposes, as we can see in the <strong>Data Table</strong>. We will shorten the names with <strong>Edit Domain</strong> widget.</p>
 <p><strong>Edit Domain</strong> enables us to change attribute names and even rename attribute values for discrete attributes. Now our attribute names are much easier to work with, as we can see in <strong>Data Table (1)</strong>.</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/EnKlik-Anketa-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3302" height="1858" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/EnKlik-Anketa-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"EnKlik Anketa\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Import data from EnKlikAnketa (1ka.si) public URL.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: survey results\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"EnKlik Anketa\"\n      }), \" widget retrieves survey results obtained from the \", _jsx(_components.a, {\n        href: \"http://english.1ka.si/\",\n        children: \"EnKlikAnketa\"\n      }), \" service. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/educational/google-sheets/">Google Sheets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/enklik-anketa/">EnKlik Anketa</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/interactive-kmeans/">Interactive k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/gradient-descent/">Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-regression/">Polynomial Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-classification/">Polynomial Classification</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/pie-chart/">Pie Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/random-data/">Random Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Google Sheets</h1>
 <p>Read data from a Google Sheets spreadsheet.</p>
 <p><strong>Outputs</strong></p>
@@ -168,4 +168,4 @@ <h2>Description</h2>
 </ol>
 <h2>Example</h2>
 <p>This widget is used for loading the data. We have used the link from the Google Sheets: <a href="https://goo.gl/jChYki">https://goo.gl/jChYki</a>. This is a fictional data on hamsters and rabbits, of which some have the disease and some don&#x27;t. Use the <strong>Data Table</strong> to observe the loaded data in a spreadsheet.</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/Google-Sheets-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2684" height="1156" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/Google-Sheets-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Google Sheets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Read data from a Google Sheets spreadsheet.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: data set from the Google Sheets service.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Description\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget reads data from the \", _jsx(_components.a, {\n        href: \"https://docs.google.com/spreadsheets\",\n        children: \"Google Sheets service\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/google-sheets/">Google Sheets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/enklik-anketa/">EnKlik Anketa</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/interactive-kmeans/">Interactive k-Means</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/educational/gradient-descent/">Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-regression/">Polynomial Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-classification/">Polynomial Classification</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/pie-chart/">Pie Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/random-data/">Random Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Gradient Descent</h1>
 <p>Educational widget that shows the gradient descent algorithm on a logistic or linear regression.</p>
 <p><strong>Inputs</strong></p>
@@ -196,4 +196,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/educational/images/__optimized-images__/gradient-descent4.png" data-gallery="true"><img alt="" loading="lazy" width="763" height="589" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/gradient-descent4.png"/></a>
 <p>If we want to demonstrate the <em>linear regression</em> we can change the data set to <em>Housing</em>. This data set has a continuous class variable. When using linear regression we can select only one feature which means that our function is linear. Another parameter that is plotted in the graph is <a href="https://en.wikipedia.org/wiki/Y-intercept">intercept</a> of a <a href="https://en.wikipedia.org/wiki/Linear_function">linear function</a>.</p>
 <p>This time we selected <em>INDUS</em> as an <a href="https://en.wikipedia.org/wiki/Dependent_and_independent_variables">independent variable</a>. In the widget we can make the same actions as before. In the end we can also check the predictions for each point with the <em>Predictions</em> widget. And check coefficients of linear regression in a <em>Data Table</em>.</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/gradient-descent-housing.png" data-gallery="true"><img alt="" loading="lazy" width="963" height="686" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/gradient-descent-housing.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gradient Descent\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Educational widget that shows the gradient descent algorithm on a logistic or linear regression.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: data with columns selected in the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Classifier: model produced at the current step of the algorithm.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: coefficients at the current step of the algorithm.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Description\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget incrementally shows steps of \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Gradient_descent\",\n        children: \"gradient descent\"\n      }), \" for a logistic or linear regression. 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+<a href="/widget-catalog/educational/images/__optimized-images__/gradient-descent-housing.png" data-gallery="true"><img alt="" loading="lazy" width="963" height="686" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/gradient-descent-housing.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gradient Descent\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Educational widget that shows the gradient descent algorithm on a logistic or linear regression.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: data with columns selected in the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Classifier: model produced at the current step of the algorithm.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: coefficients at the current step of the algorithm.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Description\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget incrementally shows steps of \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Gradient_descent\",\n        children: \"gradient descent\"\n      }), \" for a logistic or linear regression. 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@@ -194,4 +194,4 @@ <h2>Example</h2>
 <p>Now we can repeat running the algorithm until it converges again, but before that we will move the new centroid to change the behavior of the algorithm. We grabbed the orange centroid and moved it to the desired position.</p>
 <a href="/widget-catalog/educational/images/__optimized-images__/interactive-kmeans-step6.png" data-gallery="true"><img alt="" loading="lazy" width="830" height="630" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/interactive-kmeans-step6.png"/></a>
 <p>Then we press <em>Run</em> and observe the centroids while the algorithm converges again.</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/interactive-kmeans-step7.png" data-gallery="true"><img alt="" loading="lazy" width="830" height="630" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/interactive-kmeans-step7.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Interactive k-Means\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Educational widget that shows the working of a k-means clustering.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: data set with cluster annotation\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Centroids: centroids position\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Description\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The aim of this widget is to show the working of a \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-means_clustering\",\n        children: \"k-means clustering algorithm\"\n      }), \" on two attributes from a data set. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/google-sheets/">Google Sheets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/enklik-anketa/">EnKlik Anketa</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/interactive-kmeans/">Interactive k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/gradient-descent/">Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-regression/">Polynomial Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-classification/">Polynomial Classification</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/educational/pie-chart/">Pie Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/random-data/">Random Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Pie Chart</h1>
 <p>The widget for visualizing discrete attributes in the pie chart.</p>
 <p><strong>Inputs</strong></p>
@@ -169,4 +169,4 @@
 </ol>
 <h2>Example</h2>
 <p>We load the Titanic dataset in File widget and connected the data to Pie Chart. Here we show the distribution of gender data and split pies by survived attributes. We notice that in the group of passengers that did not survive there are mainly male while there is a higher proportion of women in the group of people that survived. While the pie chart can shed some light of data we still suggest using more informative visualizations, e.g. Box Plot.</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/piechart-example.png" data-gallery="true"><img alt="" loading="lazy" width="840" height="522" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/piechart-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pie Chart\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget for visualizing discrete attributes in the pie chart.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The aim of this widget is to demonstrate that pie charts are a terrible visualization. Please don't use it for any other purpose.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/educational/images/__optimized-images__/piechart-stamped.png\",\n      alt: \"\",\n      width: \"839\",\n      height: \"341\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the attribute you want to visualize.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the attribute which is used to split data in more charts.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Check if you want pies to be exploded (parts of the pie will have space in between).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"You will see your data visualized here.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"With those buttons, you can either get help, save the plot, or include plots in the report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We load the Titanic dataset in File widget and connected the data to Pie Chart. 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+<a href="/widget-catalog/educational/images/__optimized-images__/piechart-example.png" data-gallery="true"><img alt="" loading="lazy" width="840" height="522" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/piechart-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pie Chart\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget for visualizing discrete attributes in the pie chart.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The aim of this widget is to demonstrate that pie charts are a terrible visualization. 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@@ -188,4 +188,4 @@ <h2>Example</h2>
 <p>We added another class, chose C2 as the target, increased the polynomial expansion to 4 and weaken the regularization (in Logistic regression widget).</p>
 <p>Outlines still represent the original classes. Instances of the target classes are colored red, the other two classes are gray.</p>
 <p>Hovering at any point shows us the probability for red (e.g. 0.264).</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/polyclassification-3-classes.png" data-gallery="true"><img alt="" loading="lazy" width="1611" height="1060" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/polyclassification-3-classes.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Polynomial Classification\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Educational widget that visually demonstrates classification in two-dimensional space.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor (optional): data preprocessors\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner (optional): classification algorithm used in the widget (default:Logistic Regression)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: classification algorithm used in the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Classifier: trained classifier\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: classifier coefficients if it has them\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Description\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget interactively shows classification probabilities using contours and color gradient for any classifier. 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Instances of the target classes are colored red, the other two classes are gray.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hovering at any point shows us the probability for red (e.g. 0.264).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/educational/images/__optimized-images__/polyclassification-3-classes.png\",\n      alt: \"\",\n      width: \"1611\",\n      height: \"1060\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File 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+<a href="/widget-catalog/educational/images/__optimized-images__/polyclassification-3-classes.png" data-gallery="true"><img alt="" loading="lazy" width="1611" height="1060" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/polyclassification-3-classes.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Polynomial Classification\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Educational widget that visually demonstrates classification in two-dimensional space.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor (optional): data preprocessors\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner (optional): classification algorithm used in the widget (default:Logistic Regression)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: classification algorithm used in the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Classifier: trained classifier\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: classifier coefficients if it has them\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Description\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget interactively shows classification probabilities using contours and color gradient for any classifier. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/google-sheets/">Google Sheets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/enklik-anketa/">EnKlik Anketa</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/interactive-kmeans/">Interactive k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/gradient-descent/">Gradient Descent</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/educational/polynomial-regression/">Polynomial Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/polynomial-classification/">Polynomial Classification</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/pie-chart/">Pie Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/educational/random-data/">Random Data</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Polynomial Regression</h1>
 <p>Educational widget that interactively shows regression line for different regressors.</p>
 <p><strong>Inputs</strong></p>
@@ -186,4 +186,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/educational/images/__optimized-images__/polynomial-regression-exp3.png" data-gallery="true"><img alt="" loading="lazy" width="852" height="531" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/polynomial-regression-exp3.png"/></a>
 <p>To observe different results, change <strong>Linear Regression</strong> to any other regression learner from Orange. Example below is done with the <strong>Tree</strong> learner.</p>
 <a href="/widget-catalog/educational/images/__optimized-images__/polyregressiontree1.png" data-gallery="true"><img alt="" loading="lazy" width="377" height="237" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/polyregressiontree1.png"/></a>
-<a href="/widget-catalog/educational/images/__optimized-images__/polynomial-regression-tree-exp1.png" data-gallery="true"><img alt="" loading="lazy" width="852" height="531" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/polynomial-regression-tree-exp1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Polynomial Regression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Educational widget that interactively shows regression line for different regressors.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set. 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 <p>Generate random data sample.</p>
 <p><strong>Inputs</strong></p>
@@ -187,4 +187,4 @@
 <h2>Example</h2>
 <p>We normaly wouldn&#x27;t create a data set with so many different distributions but rather, for instance, a set of normally distributed variables and perhaps a binary variable, which we will use as the target variable. In this example, we use the default settings, which generate 10 normally distributed variables and a single binomial variable.</p>
 <p>We observe the generated data in a <strong>Data Table</strong> and in <strong>Distributions</strong>.</p>
-<a href="/widget-catalog/educational/images/__optimized-images__/Random-Data-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2426" height="1928" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/educational/images/__optimized-images__/Random-Data-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Random Data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generate random data sample.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: randomly generated data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Random Data\"\n      }), \" allows creating random data sets, where variables correspond to the selected distributions. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/testandscore/">Test and Score</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/predictions/">Predictions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/confusionmatrix/">Confusion Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/rocanalysis/">ROC Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/performancecurve/">Performance Curve</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/evaluate/calibrationplot/">Calibration Plot</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Calibration Plot</h1>
 <p>Shows the match between classifiers&#x27; probability predictions and actual class probabilities.</p>
 <p><strong>Inputs</strong></p>
@@ -196,4 +196,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1838" height="1434" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot-Example1.png"/></a>
 <p>In the second example, we show how to use the widget to output a calibrated model. We use <a href="../data/datasampler.md">Data Sampler</a> to split the data into training and test subsets. We pass both the training and test subsets to <strong>Test and Score</strong> and train a <a href="/widget-catalog/evaluate/../model/logisticregression">Logistic Regression</a> model, which we pass to <strong>Calibration Plot</strong>. Note that only a single calibrated model can be on the output, hence the user must select a single model from the classifier list.</p>
 <p>Once the model is calibrated, we can pass it to <a href="/widget-catalog/evaluate/../evaluate/predictions">Predictions</a> and use it on training data.</p>
-<a href="/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2872" height="1550" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Calibration Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows the match between classifiers' probability predictions and actual class probabilities.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Calibrated Model: a model with calibrated probabilities or a model with the same probabilities but different classification threshold\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Calibration_curve\",\n        children: \"Calibration Plot\"\n      }), \" plots probabilities predicted by the classifier(s) against actual class probabilities. We would use this widget to see whether a classifier is overly optimistic (gives predominantly positive results) or pessimistic (gives predominantly negative results). The widget can also output a calibrated model, where the user sets his/her own probability threshold.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot.png\",\n      alt: \"\",\n      width: \"794\",\n      height: \"541\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select the desired target class from the drop down menu.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show rug\"\n            }), \": If enabled, ticks are displayed at the bottom and the top of the graph, which represent negative and positive examples respectively. Their position corresponds to the classifier's probability prediction. Different colors represent different classifiers. At the bottom of the graph, the points to the left are those which are (correctly) assigned a low probability of the target class, and those to the right are incorrectly assigned high probabilities. At the top of the graph, the instances to the right are correctly assigned high probabilities and vice versa.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Curves for individual folds: a curve is displayed for each fold from the \", _jsx(_components.a, {\n              href: \"/widget-catalog/evaluate/testandscore\",\n              children: \"Test and Score\"\n            }), \" widget.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Choose which classifiers to plot. Colors in the list of classifiers correspond to colors used in the plot. 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The options for smoothing functions are \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Sigmoid_function\",\n              children: \"Sigmoid\"\n            }), \" or \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Isotonic_regression\",\n              children: \"Isotonic\"\n            }), \" function.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"classification accuracy\"\n            }), \": displays classification accurracy at changing probability thresholds. Threshold can be set by dragging the vertical line left or right.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"F1\"\n            }), \": displays F1 score at changing probability thresholds. 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Alternatively, click \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When the widget shows the calibration curve, it outputs a calibrated model, whose predicted probabilities are tuned to better match the actual probabilities.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When showing other curves (such as F1), the widget outputs a model that gives the same probabilities, but class predictions are made at thresholds different from 0.5. For instance, if we drag the vertical line shown in the graph to 0.3, the widget outputs a model that predicts a positive class when its probability exceeds 30%.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget cannot output a model if the input data contains models obtained from multiple runs (for instance from cross validation or repeated sampling). 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+<a href="/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2872" height="1550" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Calibration Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows the match between classifiers' probability predictions and actual class probabilities.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Calibrated Model: a model with calibrated probabilities or a model with the same probabilities but different classification threshold\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Calibration_curve\",\n        children: \"Calibration Plot\"\n      }), \" plots probabilities predicted by the classifier(s) against actual class probabilities. We would use this widget to see whether a classifier is overly optimistic (gives predominantly positive results) or pessimistic (gives predominantly negative results). The widget can also output a calibrated model, where the user sets his/her own probability threshold.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/Calibration-Plot.png\",\n      alt: \"\",\n      width: \"794\",\n      height: \"541\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select the desired target class from the drop down menu.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show rug\"\n            }), \": If enabled, ticks are displayed at the bottom and the top of the graph, which represent negative and positive examples respectively. Their position corresponds to the classifier's probability prediction. Different colors represent different classifiers. At the bottom of the graph, the points to the left are those which are (correctly) assigned a low probability of the target class, and those to the right are incorrectly assigned high probabilities. At the top of the graph, the instances to the right are correctly assigned high probabilities and vice versa.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Curves for individual folds: a curve is displayed for each fold from the \", _jsx(_components.a, {\n              href: \"/widget-catalog/evaluate/testandscore\",\n              children: \"Test and Score\"\n            }), \" widget.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Choose which classifiers to plot. Colors in the list of classifiers correspond to colors used in the plot. 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Threshold can be set by dragging the vertical line left or right.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"sensitivity and specificity\"\n            }), \": displays the relationship between \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Sensitivity_and_specificity\",\n              children: \"sensitivity and specificity\"\n            }), \" at changing probability thresholds.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"precision and recall\"\n            }), \": displays the relationship between \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Precision_and_recall\",\n              children: \"precision and recall\"\n            }), \" at changing probability thresholds.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"pos and neg predictive value\"\n            }), \": displays the relationship between \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values\",\n              children: \"positive and negative predictive values\"\n            }), \" at changing probability thresholds.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"true and false positive rate\"\n            }), \": displays the relationship between \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/False_positives_and_false_negatives\",\n              children: \"TP and FP rate\"\n            }), \" at changing probability thresholds.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Apply Automatically\"\n        }), \" is ticked, changes are communicated automatically. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/testandscore/">Test and Score</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/predictions/">Predictions</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/evaluate/confusionmatrix/">Confusion Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/rocanalysis/">ROC Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/performancecurve/">Performance Curve</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/calibrationplot/">Calibration Plot</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Confusion Matrix</h1>
 <p>Shows proportions between the predicted and actual class.</p>
 <p><strong>Inputs</strong></p>
@@ -195,4 +195,4 @@ <h2>Example</h2>
 <p><a href="/widget-catalog/evaluate/../evaluate/testandscore">Test &amp; Score</a> gets the data from <a href="/widget-catalog/evaluate/../data/file">File</a> and two learning algorithms from <a href="/widget-catalog/evaluate/../model/naivebayes">Naive Bayes</a> and <a href="/widget-catalog/evaluate/../model/tree">Tree</a>. It performs cross-validation or some other train-and-test procedures to get class predictions by both algorithms for all (or some) data instances. The test results are fed into the <strong>Confusion Matrix</strong>, where we can observe how many instances were misclassified and in which way.</p>
 <p>In the output, we used <a href="/widget-catalog/evaluate/../data/datatable">Data Table</a> to show the instances we selected in the confusion matrix. If we, for instance, click <em>Misclassified</em>, the table will contain all instances which were misclassified by the selected method.</p>
 <p>The <a href="../visualize/scatterplot.md">Scatter Plot</a> gets two sets of data. From the <a href="/widget-catalog/evaluate/../data/file">File</a> widget it gets the complete data, while the confusion matrix sends only the selected data, misclassifications for instance. The scatter plot will show all the data, with bold symbols representing the selected data.</p>
-<a href="/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1536" height="978" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Confusion Matrix\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows proportions between the predicted and actual class.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: data subset selected from confusion matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with the additional information on whether a data instance was selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Confusion_matrix\",\n        children: \"Confusion Matrix\"\n      }), \" gives the number/proportion of instances between the predicted and actual class. 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This way, one can observe which specific instances were misclassified and how.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget usually gets the evaluation results from \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \"; an example of the schema is shown below.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-stamped.png\",\n      alt: \"\",\n      width: \"650\",\n      height: \"518\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"When evaluation results contain data on multiple learning algorithms, we have to choose one in the \", _jsx(_components.em, {\n          children: \"Learners\"\n        }), \" box.\\nThe snapshot shows the confusion matrix for \", _jsx(_components.a, {\n          href: \"/widget-catalog/evaluate/../model/tree\",\n          children: \"Tree\"\n        }), \" and \", _jsx(_components.a, {\n          href: \"/widget-catalog/evaluate/../model/naivebayes\",\n          children: \"Naive Bayesian\"\n        }), \" models trained and tested on the \", _jsx(_components.em, {\n          children: \"iris\"\n        }), \" data. 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The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"In \", _jsx(_components.em, {\n          children: \"Show\"\n        }), \", we select what data we would like to see in the matrix.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Number of instances\"\n            }), \" shows correctly and incorrectly classified instances numerically.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Proportions of predicted\"\n            }), \" shows how many instances classified as, say, \", _jsx(_components.em, {\n              children: \"Iris-versicolor\"\n            }), \" are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Proportions of actual\"\n            }), \" shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae.\\n\", _jsx(_components.img, {\n              src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-propTrue.png\",\n              alt: \"\",\n              width: \"424\",\n              height: \"194\"\n            })]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"In \", _jsx(_components.em, {\n          children: \"Select\"\n        }), \", you can choose the desired output.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Correct\"\n            }), \" sends all correctly classified instances to the output by selecting the diagonal of the matrix.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Misclassified\"\n            }), \" selects the misclassified instances.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"None\"\n            }), \" annuls the selection.\\nAs mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options \", _jsx(_components.em, {\n          children: \"Predictions\"\n        }), \" and/or \", _jsx(_components.em, {\n          children: \"Probabilities\"\n        }), \" are checked.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The widget outputs every change if \", _jsx(_components.em, {\n          children: \"Send Automatically\"\n        }), \" is ticked. If not, the user will need to click \", _jsx(_components.em, {\n          children: \"Send Selected\"\n        }), \" to commit the changes.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The following workflow demonstrates what this widget can be used for.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-Schema.png\",\n      alt: \"\",\n      width: \"643\",\n      height: \"626\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \" gets the data from \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/file\",\n        children: \"File\"\n      }), \" and two learning algorithms from \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../model/naivebayes\",\n        children: \"Naive Bayes\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../model/tree\",\n        children: \"Tree\"\n      }), \". It performs cross-validation or some other train-and-test procedures to get class predictions by both algorithms for all (or some) data instances. The test results are fed into the \", _jsx(_components.strong, {\n        children: \"Confusion Matrix\"\n      }), \", where we can observe how many instances were misclassified and in which way.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the output, we used \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/datatable\",\n        children: \"Data Table\"\n      }), \" to show the instances we selected in the confusion matrix. If we, for instance, click \", _jsx(_components.em, {\n        children: \"Misclassified\"\n      }), \", the table will contain all instances which were misclassified by the selected method.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"../visualize/scatterplot.md\",\n        children: \"Scatter Plot\"\n      }), \" gets two sets of data. From the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/file\",\n        children: \"File\"\n      }), \" widget it gets the complete data, while the confusion matrix sends only the selected data, misclassifications for instance. The scatter plot will show all the data, with bold symbols representing the selected data.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-Example.png\",\n      alt: \"\",\n      width: \"1536\",\n      height: \"978\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV 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Columns","category":"Transform","keywords":["select columns","filter","attributes","target","variable"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Columns.png","url":"selectcolumns"},{"title":"Select Rows","category":"Transform","keywords":["select rows","filter"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Rows.png","url":"selectrows"},{"title":"Transpose","category":"Transform","keywords":["transpose"],"background":"#FF9D5E","icon":"widget-icons/Transform-Transpose.png","url":"transpose"},{"title":"Merge Data","category":"Transform","keywords":["merge data","join"],"background":"#FF9D5E","icon":"widget-icons/Transform-Merge Data.png","url":"mergedata"},{"title":"Concatenate","category":"Transform","keywords":["concatenate","append","join","extend"],"background":"#FF9D5E","icon":"widget-icons/Transform-Concatenate.png","url":"concatenate"},{"title":"Select by Data 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Plot","category":"Visualize","keywords":["scatter","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Scatter Plot.png","url":"scatterplot"},{"title":"Line Plot","category":"Visualize","keywords":["line","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Line Plot.png","url":"lineplot"},{"title":"Bar Plot","category":"Visualize","keywords":["bar plot","chart"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Bar Plot.png","url":"barplot"},{"title":"Sieve Diagram","category":"Visualize","keywords":["sieve","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Sieve Diagram.png","url":"sievediagram"},{"title":"Mosaic Display","category":"Visualize","keywords":["mosaic","display"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Mosaic Display.png","url":"mosaicdisplay"},{"title":"FreeViz","category":"Visualize","keywords":["freeviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-FreeViz.png","url":"freeviz"},{"title":"Linear Projection","category":"Visualize","keywords":["linear","projection"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Linear Projection.png","url":"linearprojection"},{"title":"Radviz","category":"Visualize","keywords":["radviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Radviz.png","url":"radviz"},{"title":"Heat Map","category":"Visualize","keywords":["heat","map"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Heat Map.png","url":"heatmap"},{"title":"Venn Diagram","category":"Visualize","keywords":["venn","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Venn Diagram.png","url":"venndiagram"},{"title":"Silhouette Plot","category":"Visualize","keywords":["silhouette","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Silhouette Plot.png","url":"silhouetteplot"},{"title":"Pythagorean Tree","category":"Visualize","keywords":["pythagorean tree","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean 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The rightmost column gives the number of instances from each class (there are 50 irises of each of the three classes) and the bottom row gives the number of instances classified into each class (e.g., 48 instances were classified into virginica).\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"In \", _jsx(_components.em, {\n          children: \"Show\"\n        }), \", we select what data we would like to see in the matrix.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Number of instances\"\n            }), \" shows correctly and incorrectly classified instances numerically.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Proportions of predicted\"\n            }), \" shows how many instances classified as, say, \", _jsx(_components.em, {\n              children: \"Iris-versicolor\"\n            }), \" are in which true class; in the table we can read the 0% of them are actually setosae, 88.5% of those classified as versicolor are versicolors, and 7.7% are virginicae.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Proportions of actual\"\n            }), \" shows the opposite relation: of all true versicolors, 92% were classified as versicolors and 8% as virginicae.\\n\", _jsx(_components.img, {\n              src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-propTrue.png\",\n              alt: \"\",\n              width: \"424\",\n              height: \"194\"\n            })]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"In \", _jsx(_components.em, {\n          children: \"Select\"\n        }), \", you can choose the desired output.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Correct\"\n            }), \" sends all correctly classified instances to the output by selecting the diagonal of the matrix.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Misclassified\"\n            }), \" selects the misclassified instances.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"None\"\n            }), \" annuls the selection.\\nAs mentioned before, one can also select individual cells of the table to select specific kinds of misclassified instances (e.g. the versicolors classified as virginicae).\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"When sending selected instances, the widget can add new attributes, such as predicted classes or their probabilities, if the corresponding options \", _jsx(_components.em, {\n          children: \"Predictions\"\n        }), \" and/or \", _jsx(_components.em, {\n          children: \"Probabilities\"\n        }), \" are checked.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The widget outputs every change if \", _jsx(_components.em, {\n          children: \"Send Automatically\"\n        }), \" is ticked. If not, the user will need to click \", _jsx(_components.em, {\n          children: \"Send Selected\"\n        }), \" to commit the changes.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The following workflow demonstrates what this widget can be used for.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-Schema.png\",\n      alt: \"\",\n      width: \"643\",\n      height: \"626\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \" gets the data from \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/file\",\n        children: \"File\"\n      }), \" and two learning algorithms from \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../model/naivebayes\",\n        children: \"Naive Bayes\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../model/tree\",\n        children: \"Tree\"\n      }), \". It performs cross-validation or some other train-and-test procedures to get class predictions by both algorithms for all (or some) data instances. The test results are fed into the \", _jsx(_components.strong, {\n        children: \"Confusion Matrix\"\n      }), \", where we can observe how many instances were misclassified and in which way.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the output, we used \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/datatable\",\n        children: \"Data Table\"\n      }), \" to show the instances we selected in the confusion matrix. If we, for instance, click \", _jsx(_components.em, {\n        children: \"Misclassified\"\n      }), \", the table will contain all instances which were misclassified by the selected method.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"../visualize/scatterplot.md\",\n        children: \"Scatter Plot\"\n      }), \" gets two sets of data. From the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/file\",\n        children: \"File\"\n      }), \" widget it gets the complete data, while the confusion matrix sends only the selected data, misclassifications for instance. The scatter plot will show all the data, with bold symbols representing the selected data.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/ConfusionMatrix-Example.png\",\n      alt: \"\",\n      width: \"1536\",\n      height: \"978\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV 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Columns","category":"Transform","keywords":["select columns","filter","attributes","target","variable"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Columns.png","url":"selectcolumns"},{"title":"Select Rows","category":"Transform","keywords":["select rows","filter"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Rows.png","url":"selectrows"},{"title":"Transpose","category":"Transform","keywords":["transpose"],"background":"#FF9D5E","icon":"widget-icons/Transform-Transpose.png","url":"transpose"},{"title":"Merge Data","category":"Transform","keywords":["merge data","join"],"background":"#FF9D5E","icon":"widget-icons/Transform-Merge Data.png","url":"mergedata"},{"title":"Concatenate","category":"Transform","keywords":["concatenate","append","join","extend"],"background":"#FF9D5E","icon":"widget-icons/Transform-Concatenate.png","url":"concatenate"},{"title":"Select by Data 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Plot","category":"Visualize","keywords":["scatter","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Scatter Plot.png","url":"scatterplot"},{"title":"Line Plot","category":"Visualize","keywords":["line","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Line Plot.png","url":"lineplot"},{"title":"Bar Plot","category":"Visualize","keywords":["bar plot","chart"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Bar Plot.png","url":"barplot"},{"title":"Sieve Diagram","category":"Visualize","keywords":["sieve","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Sieve Diagram.png","url":"sievediagram"},{"title":"Mosaic Display","category":"Visualize","keywords":["mosaic","display"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Mosaic Display.png","url":"mosaicdisplay"},{"title":"FreeViz","category":"Visualize","keywords":["freeviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-FreeViz.png","url":"freeviz"},{"title":"Linear Projection","category":"Visualize","keywords":["linear","projection"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Linear Projection.png","url":"linearprojection"},{"title":"Radviz","category":"Visualize","keywords":["radviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Radviz.png","url":"radviz"},{"title":"Heat Map","category":"Visualize","keywords":["heat","map"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Heat Map.png","url":"heatmap"},{"title":"Venn Diagram","category":"Visualize","keywords":["venn","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Venn Diagram.png","url":"venndiagram"},{"title":"Silhouette Plot","category":"Visualize","keywords":["silhouette","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Silhouette Plot.png","url":"silhouetteplot"},{"title":"Pythagorean Tree","category":"Visualize","keywords":["pythagorean tree","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/testandscore/">Test and Score</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/predictions/">Predictions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/confusionmatrix/">Confusion Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/rocanalysis/">ROC Analysis</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/evaluate/performancecurve/">Performance Curve</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/calibrationplot/">Calibration Plot</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Performance Curve</h1>
 <p>Construct and display a performance curve from the evaluation of classifiers.</p>
 <p><strong>Inputs</strong></p>
@@ -181,4 +181,4 @@ <h2>Examples</h2>
 <p>In the second example, we show how to calibrate a model in the <strong>Performance Curve</strong> widget. We are using the <em>heart-disease</em> data. First, the widget requires a single model on the input. This means cross-validation from Test and Score won&#x27;t work, but there are as many models as there are folds. To pass a single model, use the <em>Test on test data</em> option.</p>
 <p>In Performance Curve, we then observe the curve for the positive (1) class. The model has the optimal balance between precision and recall at the probability threshold of 0.475. We select this threshold and the model with the given threshold is sent to the output.</p>
 <p>We can use this model in <a href="/widget-catalog/evaluate/../evaluate/predictions">Predictions</a> to predict on new data with the calibrated model. See also <a href="/widget-catalog/evaluate/../model/calibratedlearner">Calibrated Learner</a> for more calibration options.</p>
-<a href="/widget-catalog/evaluate/images/__optimized-images__/PerformanceCurve-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="3096" height="1816" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/PerformanceCurve-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Performance Curve\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Construct and display a performance curve from the evaluation of classifiers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Calibrated Model: trained model using the calibrated learner\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Performance Curve\"\n      }), \" shows the curves for analysing the proportion of true positive data instances in relation to the classifier's threshold or the number of instances that we classify as positive. 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+<a href="/widget-catalog/evaluate/images/__optimized-images__/PerformanceCurve-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="3096" height="1816" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/PerformanceCurve-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Performance Curve\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Construct and display a performance curve from the evaluation of classifiers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Calibrated Model: trained model using the calibrated learner\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Performance Curve\"\n      }), \" shows the curves for analysing the proportion of true positive data instances in relation to the classifier's threshold or the number of instances that we classify as positive. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/testandscore/">Test and Score</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/evaluate/predictions/">Predictions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/confusionmatrix/">Confusion Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/rocanalysis/">ROC Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/performancecurve/">Performance Curve</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/calibrationplot/">Calibration Plot</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Predictions</h1>
 <p>Shows models&#x27; predictions on the data.</p>
 <p><strong>Inputs</strong></p>
@@ -185,4 +185,4 @@ <h2>Examples</h2>
 <p>Then we will send the <em>Data Sample</em> into <a href="../data/preprocess.md">Preprocess</a>. We will use <em>Impute Missing Values</em>, but you can try any combination of preprocessors on your data. We will send preprocessed data to <a href="/widget-catalog/evaluate/../model/logisticregression">Logistic Regression</a> and the constructed model to <strong>Predictions</strong>.</p>
 <p>Finally, <strong>Predictions</strong> also needs the data to predict on. We will use the output of <a href="/widget-catalog/evaluate/../data/datasampler">Data Sampler</a> for prediction, but this time not the <em>Data Sample</em>, but the <em>Remaining Data</em>, this is the data that wasn&#x27;t used for training the model.</p>
 <p>Notice how we send the remaining data directly to <strong>Predictions</strong> without applying any preprocessing. This is because Orange handles preprocessing on new data internally to prevent any errors in the model construction. The exact same preprocessor that was used on the training data will be used for predictions. The same process applies to <a href="/widget-catalog/evaluate/../evaluate/testandscore">Test &amp; Score</a>.</p>
-<a href="/widget-catalog/evaluate/images/__optimized-images__/Predictions-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2616" height="1850" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/Predictions-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Predictions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows models' predictions on the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Predictors: predictors to be used on the data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Predictions: data with added predictions\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget receives a dataset and one or more predictors (predictive models, not learning algorithms - see the example below). It outputs the data and the predictions.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/Predictions-stamped.png\",\n      alt: \"\",\n      width: \"1612\",\n      height: \"1070\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Information on the input, namely the number of instances to predict, the number of predictors and the task (classification or regression). If you have sorted the data table by attribute and you wish to see the original view, press \", _jsx(_components.em, {\n          children: \"Restore Original Order\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"You can select the options for classification. If \", _jsx(_components.em, {\n          children: \"Predicted class\"\n        }), \" is ticked, the view provides information on predicted class. If \", _jsx(_components.em, {\n          children: \"Predicted probabilities for\"\n        }), \" is ticked, the view provides information on probabilities predicted by the classifier(s). You can also select the predicted class displayed in the view. The option \", _jsx(_components.em, {\n          children: \"Draw distribution bars\"\n        }), \" provides a visualization of probabilities.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"By ticking the \", _jsx(_components.em, {\n          children: \"Show full dataset\"\n        }), \", you can view the entire data table (otherwise only class variable will be shown).\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the desired output.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Predictions.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget show the probabilities and final decisions of \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Predictive_modelling\",\n        children: \"predictive models\"\n      }), \". The output of the widget is another dataset, where predictions are appended as new meta attributes. You can select which features you wish to output (original data, predictions, probabilities). The result can be observed in a \", _jsx(_components.a, {\n        href: \"../data/datatable.md\",\n        children: \"Data Table\"\n      }), \". If the predicted data includes true class values, the result of prediction can also be observed in a \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/confusionmatrix\",\n        children: \"Confusion Matrix\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example, we will use \", _jsx(_components.em, {\n        children: \"Attrition - Train\"\n      }), \" data from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/datasets\",\n        children: \"Datasets\"\n      }), \" widget. This is a data on attrition of employees. In other words, we wish to know whether a certain employee will resign from the job or not. We will construct a predictive model with the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../model/tree\",\n        children: \"Tree\"\n      }), \" widget and observe probabilities in \", _jsx(_components.strong, {\n        children: \"Predictions\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For predictions we need both the training data, which we have loaded in the first \", _jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget and the data to predict, which we will load in another \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/datasets\",\n        children: \"Datasets\"\n      }), \" widget. We will use \", _jsx(_components.em, {\n        children: \"Attrition - Predict\"\n      }), \" data this time. Connect the second data set to \", _jsx(_components.strong, {\n        children: \"Predictions\"\n      }), \". Now we can see predictions for the three data instances from the second data set.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../model/tree\",\n        children: \"Tree\"\n      }), \" model predicts none of the employees will leave the company. You can try other model and see if predictions change. Or test the predictive scores first in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/Predictions-Example1.png\",\n      alt: \"\",\n      width: \"2782\",\n      height: \"1348\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we will see how to properly use \", _jsx(_components.a, {\n        href: \"../data/preprocess.md\",\n        children: \"Preprocess\"\n      }), \" with \", _jsx(_components.strong, {\n        children: \"Predictions\"\n      }), \" or \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This time we are using the \", _jsx(_components.em, {\n        children: \"heart disease.tab\"\n      }), \" data from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/file\",\n        children: \"File\"\n      }), \" widget. You can access the data through the dropdown menu. This is a dataset with 303 patients that came to the doctor suffering from a chest pain. After the tests were done, some patients were found to have diameter narrowing and others did not (this is our class variable).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The heart disease data have some missing values and we wish to account for that. First, we will split the data set into train and test data with \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/datasampler\",\n        children: \"Data Sampler\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we will send the \", _jsx(_components.em, {\n        children: \"Data Sample\"\n      }), \" into \", _jsx(_components.a, {\n        href: \"../data/preprocess.md\",\n        children: \"Preprocess\"\n      }), \". 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+<a href="/widget-catalog/evaluate/images/__optimized-images__/Predictions-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2616" height="1850" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/Predictions-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Predictions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows models' predictions on the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Predictors: predictors to be used on the data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Predictions: data with added predictions\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget receives a dataset and one or more predictors (predictive models, not learning algorithms - see the example below). It outputs the data and the predictions.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/Predictions-stamped.png\",\n      alt: \"\",\n      width: \"1612\",\n      height: \"1070\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Information on the input, namely the number of instances to predict, the number of predictors and the task (classification or regression). If you have sorted the data table by attribute and you wish to see the original view, press \", _jsx(_components.em, {\n          children: \"Restore Original Order\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"You can select the options for classification. If \", _jsx(_components.em, {\n          children: \"Predicted class\"\n        }), \" is ticked, the view provides information on predicted class. If \", _jsx(_components.em, {\n          children: \"Predicted probabilities for\"\n        }), \" is ticked, the view provides information on probabilities predicted by the classifier(s). You can also select the predicted class displayed in the view. 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You can select which features you wish to output (original data, predictions, probabilities). The result can be observed in a \", _jsx(_components.a, {\n        href: \"../data/datatable.md\",\n        children: \"Data Table\"\n      }), \". If the predicted data includes true class values, the result of prediction can also be observed in a \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/confusionmatrix\",\n        children: \"Confusion Matrix\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example, we will use \", _jsx(_components.em, {\n        children: \"Attrition - Train\"\n      }), \" data from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/datasets\",\n        children: \"Datasets\"\n      }), \" widget. This is a data on attrition of employees. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/testandscore/">Test and Score</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/predictions/">Predictions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/confusionmatrix/">Confusion Matrix</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/evaluate/rocanalysis/">ROC Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/performancecurve/">Performance Curve</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/calibrationplot/">Calibration Plot</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>ROC Analysis</h1>
 <p>Plots a true positive rate against a false positive rate of a test.</p>
 <p><strong>Inputs</strong></p>
@@ -206,4 +206,4 @@
 </ol>
 <h2>Example</h2>
 <p>At the moment, the only widget which gives the right type of signal needed by the <strong>ROC Analysis</strong> is <a href="../evaluate/testandscore.md">Test &amp; Score</a>. Below, we compare two classifiers, namely <a href="../model/tree.md">Tree</a> and <a href="../model/naivebayes.md">Naive Bayes</a>, in <strong>Test&amp;Score</strong> and then compare their performance in <strong>ROC Analysis</strong>, <a href="../evaluate/performancecurve.md">Life Curve</a> and <a href="/widget-catalog/evaluate/../evaluate/calibrationplot">Calibration Plot</a>.</p>
-<a href="/widget-catalog/evaluate/images/__optimized-images__/ROCAnalysis-example.png" data-gallery="true"><img alt="" loading="lazy" width="1582" height="1060" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/ROCAnalysis-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"ROC Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots a true positive rate against a false positive rate of a test.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget shows ROC curves for the tested models and the corresponding convex hull. It serves as a mean of comparison between classification models. The curve plots a false positive rate on an x-axis (1-specificity; probability that target=1 when true value=0) against a true positive rate on a y-axis (sensitivity; probability that target=1 when true value=1). The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the classifier. 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+<a href="/widget-catalog/evaluate/images/__optimized-images__/ROCAnalysis-example.png" data-gallery="true"><img alt="" loading="lazy" width="1582" height="1060" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/ROCAnalysis-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"ROC Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots a true positive rate against a false positive rate of a test.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget shows ROC curves for the tested models and the corresponding convex hull. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/evaluate/testandscore/">Test and Score</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/predictions/">Predictions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/confusionmatrix/">Confusion Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/rocanalysis/">ROC Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/performancecurve/">Performance Curve</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/evaluate/calibrationplot/">Calibration Plot</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Test and Score</h1>
 <p>Tests learning algorithms on data.</p>
 <p><strong>Inputs</strong></p>
@@ -232,4 +232,4 @@ <h2>Example</h2>
 <p>In a typical use of the widget, we give it a dataset and a few learning algorithms and we observe their performance in the table inside the <strong>Test &amp; Score</strong> widget and in the <a href="../evaluate/rocanalysis.md">ROC</a>. The data is often preprocessed before testing; in this case we did some manual feature selection (<a href="/widget-catalog/evaluate/../data/selectcolumns">Select Columns</a> widget) on <em>Titanic</em> dataset, where we want to know only the sex and status of the survived and omit the age.</p>
 <p>In the bottom table, we have a pairwise comparison of models. We selected that comparison is based on the <em>area under ROC curve</em> statistic. The number in the table gives the probability that the model corresponding to the row is better than the model corresponding to the column. We can, for example, see that probability for the tree to be better than SVM is almost one, and the probability that tree is better than Naive Bayes is 0.001. Smaller numbers in the table are probabilities that the difference between the pair is negligible based on the negligible threshold 0.1.</p>
 <a href="/widget-catalog/evaluate/images/__optimized-images__/TestAndScore-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1348" height="1009" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/evaluate/images/__optimized-images__/TestAndScore-Example.png"/></a>
-<p>Another example of using this widget is presented in the documentation for the <a href="/widget-catalog/evaluate/../evaluate/confusionmatrix">Confusion Matrix</a> widget.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Test and Score\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Tests learning algorithms on data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Test Data: separate data for testing\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner: learning algorithm(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget tests learning algorithms. Different sampling schemes are available, including using separate test data. The widget does two things. First, it shows a table with different classifier performance measures, such as \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Accuracy_and_precision\",\n        children: \"classification accuracy\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve\",\n        children: \"area under the curve\"\n      }), \". Second, it outputs evaluation results, which can be used by other widgets for analyzing the performance of classifiers, such as \", _jsx(_components.a, {\n        href: \"../evaluate/rocanalysis.md\",\n        children: \"ROC Analysis\"\n      }), \" or \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/confusionmatrix\",\n        children: \"Confusion Matrix\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.em, {\n        children: \"Learner\"\n      }), \" signal has an uncommon property: it can be connected to more than one widget to test multiple learners with the same procedures.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/TestAndScore-stamped.png\",\n      alt: \"\",\n      width: \"719\",\n      height: \"577\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"The widget supports various sampling methods.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Cross-validation_(statistics)\",\n              children: \"Cross-validation\"\n            }), \" splits the data into a given number of folds (usually 5 or 10). 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To see others, right-click on the header and select the desired statistic.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [\"Classification\\n\", _jsx(_components.img, {\n              src: \"/widget-catalog/evaluate/images/__optimized-images__/TestAndScore-Classification.png\",\n              alt: \"\",\n              width: \"719\",\n              height: \"577\"\n            }), \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"http://gim.unmc.edu/dxtests/roc3.htm\",\n                  children: \"Area under ROC\"\n                }), \" is the area under the receiver-operating curve.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Accuracy_and_precision\",\n                  children: \"Classification accuracy\"\n                }), \" is the proportion of correctly classified examples.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/F1_score\",\n                  children: \"F-1\"\n                }), \" is a weighted harmonic mean of precision and recall (see below).\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Precision_and_recall\",\n                  children: \"Precision\"\n                }), \" is the proportion of true positives among instances classified as positive, e.g. the proportion of \", _jsx(_components.em, {\n                  children: \"Iris virginica\"\n                }), \" correctly identified as Iris virginica.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Precision_and_recall\",\n                  children: \"Recall\"\n                }), \" is the proportion of true positives among all positive instances in the data, e.g. the number of sick among all diagnosed as sick.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Sensitivity_and_specificity\",\n                  children: \"Specificity\"\n                }), \" is the proportion of true negatives among all negative instances, e.g. the number of non-sick among all diagnosed as non-sick.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Cross_entropy\",\n                  children: \"LogLoss\"\n                }), \" or cross-entropy loss takes into account the uncertainty of your prediction based on how much it varies from the actual label.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Phi_coefficient\",\n                  children: \"Matthews correlation coefficient\"\n                }), \" takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes.\"]\n              }), \"\\n\", _jsx(_components.li, {\n                children: \"Train time - 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The number in the table gives the probability that the model corresponding to the row has a higher score than the model corresponding to the column. What the higher score means depends on the metric: a higher score can either mean a model is better (for example, CA or AUC) or the opposite (for example, RMSE). If negligible difference is enabled, the smaller number below shows the probability that the difference between the pair is negligible. The test is based on the \", _jsx(_components.a, {\n          href: \"https://link.springer.com/article/10.1007/s10994-015-5486-z\",\n          children: \"Bayesian interpretation of the t-test\"\n        }), \" (\", _jsx(_components.a, {\n          href: \"https://baycomp.readthedocs.io/en/latest/introduction.html\",\n          children: \"shorter introduction\"\n        }), \").\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Get help and produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing for predictive modeling\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When building predictive models, one has to be careful about how to preprocess the data. 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Then the learner's preprocessors will be applied to the preprocessed subset.\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/data/images/__optimized-images__/Preprocess-Models2.png\",\n          alt: \"\",\n          width: \"516\",\n          height: \"226\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, there's a wrong way to do it. Connecting \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" directly to the original data and outputting preprocessed data set will likely overfit the model. Don't do it.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Preprocess-Models3.png\",\n      alt: \"\",\n      width: \"458\",\n      height: \"246\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In a typical use of the widget, we give it a dataset and a few learning algorithms and we observe their performance in the table inside the \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" widget and in the \", _jsx(_components.a, {\n        href: \"../evaluate/rocanalysis.md\",\n        children: \"ROC\"\n      }), \". The data is often preprocessed before testing; in this case we did some manual feature selection (\", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/selectcolumns\",\n        children: \"Select Columns\"\n      }), \" widget) on \", _jsx(_components.em, {\n        children: \"Titanic\"\n      }), \" dataset, where we want to know only the sex and status of the survived and omit the age.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the bottom table, we have a pairwise comparison of models. We selected that comparison is based on the \", _jsx(_components.em, {\n        children: \"area under ROC curve\"\n      }), \" statistic. The number in the table gives the probability that the model corresponding to the row is better than the model corresponding to the column. We can, for example, see that probability for the tree to be better than SVM is almost one, and the probability that tree is better than Naive Bayes is 0.001. Smaller numbers in the table are probabilities that the difference between the pair is negligible based on the negligible threshold 0.1.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/TestAndScore-Example.png\",\n      alt: \"\",\n      width: \"1348\",\n      height: \"1009\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Another example of using this widget is presented in the documentation for the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/confusionmatrix\",\n        children: \"Confusion Matrix\"\n      }), \" widget.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: 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+<p>Another example of using this widget is presented in the documentation for the <a href="/widget-catalog/evaluate/../evaluate/confusionmatrix">Confusion Matrix</a> widget.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Test and Score\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Tests learning algorithms on data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Test Data: separate data for testing\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner: learning algorithm(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Evaluation Results: results of testing classification algorithms\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget tests learning algorithms. 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The number in the table gives the probability that the model corresponding to the row has a higher score than the model corresponding to the column. What the higher score means depends on the metric: a higher score can either mean a model is better (for example, CA or AUC) or the opposite (for example, RMSE). If negligible difference is enabled, the smaller number below shows the probability that the difference between the pair is negligible. The test is based on the \", _jsx(_components.a, {\n          href: \"https://link.springer.com/article/10.1007/s10994-015-5486-z\",\n          children: \"Bayesian interpretation of the t-test\"\n        }), \" (\", _jsx(_components.a, {\n          href: \"https://baycomp.readthedocs.io/en/latest/introduction.html\",\n          children: \"shorter introduction\"\n        }), \").\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Get help and produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing for predictive modeling\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When building predictive models, one has to be careful about how to preprocess the data. There are two possible ways to do it in Orange, each slightly different:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Connect \", _jsx(_components.a, {\n            href: \"/widget-catalog/evaluate/../data/preprocess\",\n            children: \"Preprocess\"\n          }), \" to the learner. This will override the default preprocessing pipeline for the learner and apply only custom preprocessing pipeline (default preprocessing steps are described in each learner's documentation). The procedure might lead to errors within the learner.\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/data/images/__optimized-images__/Preprocess-Models1.png\",\n          alt: \"\",\n          width: \"514\",\n          height: \"200\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Connect \", _jsx(_components.strong, {\n            children: \"Preprocess\"\n          }), \" to Test and Score. This will apply the preprocessors to each batch within cross-validation. Then the learner's preprocessors will be applied to the preprocessed subset.\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/data/images/__optimized-images__/Preprocess-Models2.png\",\n          alt: \"\",\n          width: \"516\",\n          height: \"226\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, there's a wrong way to do it. Connecting \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" directly to the original data and outputting preprocessed data set will likely overfit the model. Don't do it.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/data/images/__optimized-images__/Preprocess-Models3.png\",\n      alt: \"\",\n      width: \"458\",\n      height: \"246\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In a typical use of the widget, we give it a dataset and a few learning algorithms and we observe their performance in the table inside the \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" widget and in the \", _jsx(_components.a, {\n        href: \"../evaluate/rocanalysis.md\",\n        children: \"ROC\"\n      }), \". The data is often preprocessed before testing; in this case we did some manual feature selection (\", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../data/selectcolumns\",\n        children: \"Select Columns\"\n      }), \" widget) on \", _jsx(_components.em, {\n        children: \"Titanic\"\n      }), \" dataset, where we want to know only the sex and status of the survived and omit the age.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the bottom table, we have a pairwise comparison of models. We selected that comparison is based on the \", _jsx(_components.em, {\n        children: \"area under ROC curve\"\n      }), \" statistic. The number in the table gives the probability that the model corresponding to the row is better than the model corresponding to the column. We can, for example, see that probability for the tree to be better than SVM is almost one, and the probability that tree is better than Naive Bayes is 0.001. Smaller numbers in the table are probabilities that the difference between the pair is negligible based on the negligible threshold 0.1.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/evaluate/images/__optimized-images__/TestAndScore-Example.png\",\n      alt: \"\",\n      width: \"1348\",\n      height: \"1009\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Another example of using this widget is presented in the documentation for the \", _jsx(_components.a, {\n        href: \"/widget-catalog/evaluate/../evaluate/confusionmatrix\",\n        children: \"Confusion Matrix\"\n      }), \" widget.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/permutation-importance/">Feature Importance</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/explain/explain-model/">Explain Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/explain-prediction/">Explain Prediction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/explain-predictions/">Explain Predictions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/ice/">ICE</a></li></ul></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Explain Model</h1>
 <p>Explains a classification or regression model. Explains which features contribute the most and how they contribute toward the prediction for a specific class.</p>
 <p><strong>Inputs</strong></p>
@@ -177,4 +177,4 @@
 <h2>Example</h2>
 <p>In the flowing example, we use the Explain Model widget to explain Logistic regression model. In the File widget, we open Hearth disease dataset. We connect it to Logistic regression widget, which trains the model. Explain Model widget accepts the model and data which are used to explain the model. For an explanation, we usually use the same data than for training, but it is also possible to explain the model on different data. In the Explain model widget, we set the target class on the class to 1 -- it means that we observe features that contribute the most to the prediction of a patient with diagnosed heart disease.</p>
 <p>Features in the plot are ordered by their relevance to the prediction. Major vessels coloured is the most important for the prediction in class 1. Instances with higher values of this feature (red colour) have higher SHAP values which mean they contribute toward the prediction of class 1. Lower values of this attribute (blue) contribute against the prediction of this class. The second most important attribute is chest pain (categorical attribute) with value asymptomatic. The presence of this category for the patient (red colour) contributes toward the prediction of class 1, while the absence of this category contributes against class 1.</p>
-<a href="/widget-catalog/explain/images/__optimized-images__/Explain-Model-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2190" height="1490" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/Explain-Model-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Explain Model\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Explains a classification or regression model. Explains which features contribute the most and how they contribute toward the prediction for a specific class.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset used to compute the explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: a model which widget explains\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected data: data instance that belong to selected points in the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: The score of each attribute. 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+<a href="/widget-catalog/explain/images/__optimized-images__/Explain-Model-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2190" height="1490" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/Explain-Model-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Explain Model\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Explains a classification or regression model. 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 <p>Explains which features contribute the most to the prediction for a single instance based on the model and how they contribute.</p>
 <p><strong>Inputs</strong></p>
@@ -178,4 +178,4 @@ <h2>Example</h2>
 <p>First, we open heart disease dataset in File widget. With the Data Sampler widget, we split the dataset on the training and test set. The training set is used to train the logistic regression model with the Logistic Regression widget. We compute predictions for the test part of the dataset (remaining data from Data Sampler) with the Predictions widget. In the Predictions widget (the left window) we select the data instance whose prediction we would like to explain -- we select the third row in the data which is predicted to belong to class 1 (diagnosed heart disease).</p>
 <p>Explain Prediction widget accept three inputs. First is the model from the Logistic Regression widget, background data from the Data Sampler (we usually use model&#x27;s training data as background data), and the data instance whose prediction we want to explain with the Explain Prediction widget. In the widget we select class 1 as a target class, it means we are explaining what features and how they affect the prediction probability for the selected class 1. Numbers in the gray boxes in the plot indicate that the prediction probability for the selected class is 0.6 (border between red and yellow tape) and the baseline probability is 0.45 (the average probability in the data).</p>
 <p>Features marked red on the tape push probabilities from the baseline probability toward probability 1.0 (prediction of the selected class), and blue features push against the prediction of the selected class. Numbers on the tape are SHAP values for each feature -- this is how much the feature (and its value) changes the probability toward or against the selected class. We can see that the highest impact on the prediction has the feature <em>major vessels coloured</em> with the value 2 and <em>ST by exercise</em> with the value 2.8. Two important features that push against the prediction of class 1 are <em>gender=male</em> with value 0 (which means that the patient is not male) and <em>gender=female</em> with the value 1 (patient is female - actually another feature with the same meaning that previous).<br/>
-<a href="/widget-catalog/explain/images/__optimized-images__/Explain-Prediction-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2328" height="1646" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/Explain-Prediction-Example.png"/></a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    code: \"code\",\n    h2: \"h2\",\n    em: \"em\",\n    br: \"br\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Explain Prediction\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Explains which features contribute the most to the prediction for a single instance based on the model and how they contribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model: a model whose predictions are explained by the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Background data: data needed to compute explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Single data instance whose prediction is explained by the widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Scores: The SHAP value of each features value. 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+<a href="/widget-catalog/explain/images/__optimized-images__/Explain-Prediction-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2328" height="1646" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/Explain-Prediction-Example.png"/></a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    code: \"code\",\n    h2: \"h2\",\n    em: \"em\",\n    br: \"br\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Explain Prediction\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Explains which features contribute the most to the prediction for a single instance based on the model and how they contribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model: a model whose predictions are explained by the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Background data: data needed to compute explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Single data instance whose prediction is explained by the widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Scores: The SHAP value of each features value. 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 <p>Explains which features contribute the most to the predictions for the selected instances based on the model and how they contribute.</p>
 <p><strong>Inputs</strong></p>
@@ -166,4 +166,4 @@
 <li>Data: original dataset with an additional column showing whether the instance is selected</li>
 <li>Scores: SHAP values for each feature. Features that contribute more to prediction have a higher score deviation from 0.</li>
 </ul>
-<p><strong>Explain Predictions</strong> widget explains classification or regression model&#x27;s predictions for the provided data instances.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Explain Predictions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Explains which features contribute the most to the predictions for the selected instances based on the model and how they contribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model: model whose predictions are explained by the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Background data: dataset needed to compute explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset whose predictions are explained by the widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: original dataset with an additional column showing whether the instance is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: SHAP values for each feature. 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+<p><strong>Explain Predictions</strong> widget explains classification or regression model&#x27;s predictions for the provided data instances.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Explain Predictions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Explains which features contribute the most to the predictions for the selected instances based on the model and how they contribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model: model whose predictions are explained by the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Background data: dataset needed to compute explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset whose predictions are explained by the widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: original dataset with an additional column showing whether the instance is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: SHAP values for each feature. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/permutation-importance/">Feature Importance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/explain-model/">Explain Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/explain-prediction/">Explain Prediction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/explain/explain-predictions/">Explain Predictions</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/explain/ice/">ICE</a></li></ul></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>ICE</h1>
 <p>Displays one line per instance that shows how the instance’s prediction changes when a feature changes.</p>
 <p><strong>Inputs</strong></p>
@@ -182,4 +182,4 @@
 <h2>Example</h2>
 <p>In the following example, we use the ICE widget to explain a <a href="https://orangedatamining.com/widget-catalog/model/randomforest/">Random Forest</a> model. In the <a href="https://orangedatamining.com/widget-catalog/data/file/">File</a> widget, we open the <em>housing</em> dataset. We connect it to the Random Forest widget, which trains the model. The ICE widget accepts the model and data which are used to explain the model.</p>
 <p>By selecting some arbitrary lines, the selected instances of the input dataset appear on the output of the ICE widget.</p>
-<a href="/widget-catalog/explain/images/__optimized-images__/ICE-example.png" data-gallery="true"><img alt="" loading="lazy" width="1211" height="790" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/ICE-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"ICE\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays one line per instance that shows how the instance’s prediction changes when a feature changes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model: model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"ICE\"\n      }), \" (Individual Conditional Expectation) widget visualizes the dependence of the prediction on a feature for each instance separately, resulting in one line per instance, compared to one line overall in partial dependence plots.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/explain/images/__optimized-images__/ICE.png\",\n      alt: \"\",\n      width: \"986\",\n      height: \"659\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select a target class.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select a feature.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Order features by importance (partial dependence averaged across all the samples).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Apply the color of a discrete feature.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.strong, {\n          children: \"Centered\"\n        }), \" is ticked, the plot lines will start at the origin of the y-axis.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.strong, {\n          children: \"Show mean\"\n        }), \" is ticked, the average across all the samples in the dataset is shown.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.strong, {\n          children: \"Send Automatically\"\n        }), \" is ticked, the output is sent automatically after any change.\\nAlternatively, click \", _jsx(_components.strong, {\n          children: \"Send\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Get help, save the plot, make the report, set plot properties, or observe the size of input and output data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Plot shows a line for each instance in the input dataset.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"An interactive plot. Each line represents an instance in the dataset. To select a line, click and drag a line over it.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the following example, we use the ICE widget to explain a \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/model/randomforest/\",\n        children: \"Random Forest\"\n      }), \" model. In the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/data/file/\",\n        children: \"File\"\n      }), \" widget, we open the \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" dataset. We connect it to the Random Forest widget, which trains the model. 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+<a href="/widget-catalog/explain/images/__optimized-images__/ICE-example.png" data-gallery="true"><img alt="" loading="lazy" width="1211" height="790" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/ICE-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"ICE\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays one line per instance that shows how 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Each line represents an instance in the dataset. To select a line, click and drag a line over it.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the following example, we use the ICE widget to explain a \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/model/randomforest/\",\n        children: \"Random Forest\"\n      }), \" model. In the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/data/file/\",\n        children: \"File\"\n      }), \" widget, we open the \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" dataset. We connect it to the Random Forest widget, which trains the model. The ICE widget accepts the model and data which are used to explain the model.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"By selecting some arbitrary lines, the selected instances of the input dataset appear on the output of the ICE widget.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/explain/images/__optimized-images__/ICE-example.png\",\n      alt: \"\",\n      width: \"1211\",\n      height: \"790\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: 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 <p>Inspect model using the Permutation Feature Importance technique.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@ <h2>Example</h2>
 <p>In the flowing example, we use the Feature Importance widget to explain features, used in Logistic regression model. In the File widget, we open Hearth disease dataset. We connect it to Logistic regression widget, which trains the model. Feature Importance widget accepts the model and data which are used to explain the features. For an explanation, we usually use the same data than for training, but it is also possible to explain the features on different data (e.g. reference data subset).</p>
 <p>The features in the plot are ordered by their relevance (e.g. Major vessels coloured is the most important feature).</p>
 <p>By selecting some arbitrary features, a filtered input dataset appears on the output of the Feature Importance widget.</p>
-<a href="/widget-catalog/explain/images/__optimized-images__/Permutation-Importance-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1326" height="812" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/Permutation-Importance-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Feature Importance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Inspect model using the Permutation Feature Importance technique.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset used to compute the explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: a model which widget explains\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected data: data instances that belong to selected features in the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: Mean and standard deviation of score for each feature.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Feature Importance\"\n      }), \" widget explains classification and regression models. 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+<a href="/widget-catalog/explain/images/__optimized-images__/Permutation-Importance-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1326" height="812" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/explain/images/__optimized-images__/Permutation-Importance-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Feature Importance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Inspect model using the Permutation Feature Importance technique.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset used to compute the explanations\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: a model which widget explains\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected data: data instances that belong to selected features in the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: Mean and standard deviation of score for each feature.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Feature Importance\"\n      }), \" widget explains classification and regression models. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/geo/geocoding/">Geocoding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/geo/geomap/">Geo Map</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/geo/choroplethmap/">Choropleth Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/geo/geotransform/">Geo Transform</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Choropleth Map</h1>
 <p>A thematic map in which areas are shaded in proportion to the measurement of the statistical variable being displayed.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/geo/images/__optimized-images__/Choropleth-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="976" height="545" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/geo/images/__optimized-images__/Choropleth-Example2.png"/></a>
 <p>Choropleth can provide a much better picture: we set the Detail to maximum, choose <em>Construction waste</em> and show its <em>mean</em>.</p>
 <a href="/widget-catalog/geo/images/__optimized-images__/Choropleth-Example3.png" data-gallery="true"><img alt="" loading="lazy" width="1024" height="632" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/geo/images/__optimized-images__/Choropleth-Example3.png"/></a>
-<p>Dumps with the largest proportion of construction waste (or the lowest proportion of other types?) can be found in central Slovenia.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Choropleth Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A thematic map in which areas are shaded in proportion to the measurement of the statistical variable being displayed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the map.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Choropleth\"\n      }), \" provides an easy way to visualize how a measurement varies across a geographic area or show the level of variability within a region. 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+<p>Dumps with the largest proportion of construction waste (or the lowest proportion of other types?) can be found in central Slovenia.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Choropleth Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A thematic map in which areas are shaded in proportion to the measurement of the statistical variable being displayed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the map.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Choropleth\"\n      }), \" provides an easy way to visualize how a measurement varies across a geographic area or show the level of variability within a region. 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 <p>Encode region names into geographical coordinates, or reverse-geocode latitude and longitude pairs into regions.</p>
 <p><strong>Inputs</strong></p>
@@ -186,4 +186,4 @@ <h2>Example</h2>
 <p>We will use <em>HDI</em> data from the <strong>Datasets</strong> widget. Open the widget, find <em>HDI</em> data, select it and press <em>Send</em>. First, let us observe the data in a <strong>Data Table</strong>. We have a meta attribute names <em>Country</em>, which contains country names. Now we would like to plot this on a map, but <strong>Geo Map</strong> widget requires latitude and longitude pairs. <strong>Geocoding</strong> will help us extract this information from country names.</p>
 <p>Connect <strong>Geocoding</strong> to <strong>Datasets</strong>. Region identifier in our case is the attribute <em>Country</em> and the identifier type is <em>Country name</em>. If our data contained major European cities, we would have to select this from the dropdown. On the right there is the <em>Unmatched identifier</em> editor, which shows those instances, for which <strong>Geocoding</strong> couldn&#x27;t find corresponding latitude/longitude pairs. We can help the widget by providing a custom replacement. Click on the field and start typing <em>Korea</em>. The widget will suggest two countries, Democratic Republic of Korea and South Korea. Select the one you wish to use here.</p>
 <p>Finally, we can observe the data in the second <strong>Data Table</strong>. We can see our data now has two additional attributes, one for the latitude and one for the longitude of the region of interest. Now, you can plot the data on the map!</p>
-<a href="/widget-catalog/geo/images/__optimized-images__/Geocoding-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3088" height="1432" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/geo/images/__optimized-images__/Geocoding-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Geocoding\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Encode region names into geographical coordinates, or reverse-geocode latitude and longitude pairs into regions.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: An input data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Coded Data: Data set with new meta attributes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Geocoding\"\n      }), \" widget extracts latitude/longitude pairs from region names or synthesizes latitude/longitude to return region name. 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+<a href="/widget-catalog/geo/images/__optimized-images__/Geocoding-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3088" height="1432" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/geo/images/__optimized-images__/Geocoding-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Geocoding\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Encode region names into geographical coordinates, or reverse-geocode latitude and longitude pairs into regions.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: An input data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Coded Data: Data set with new meta attributes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Geocoding\"\n      }), \" widget extracts latitude/longitude pairs from region names or synthesizes latitude/longitude to return region name. 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 <p>Show data points on a map.</p>
 <p><strong>Inputs</strong></p>
@@ -182,4 +182,4 @@
 </ol>
 <h2>Examples</h2>
 <p>In this simple example we visualize the <em>Philadelphia Crime</em> dataset that we can find in the <strong>Datasets</strong> widget. We connect the output of that widget to the <strong>Map</strong> widget. Latitude and longitude variables get automatically detected and we additionally select the crime type variable for color. We can observe how different crimes are more present in specific areas of the city.</p>
-<a href="/widget-catalog/geo/images/__optimized-images__/GeoMap-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1396" height="1141" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/geo/images/__optimized-images__/GeoMap-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Geo Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Show data points on a map.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Geo Map\"\n      }), \" widget visualizes geo-spatial data on a map. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/geo/geocoding/">Geocoding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/geo/geomap/">Geo Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/geo/choroplethmap/">Choropleth Map</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/geo/geotransform/">Geo Transform</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Geo Transform</h1>
 <p>Transform geographic coordinates from one system to another.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@ <h2>Example</h2>
 <p>Latitude and longitude are encoded in the <em>Xsugg</em> and <em>Ysugg</em> variables. But if we plot these variables, we cannot see anything in the <a href="/widget-catalog/geo/geomap">Geo Map</a>. The widget raises a warning stating the points are outside the specified range for the map.</p>
 <p>This is because the data is encoded in the WGS 84 / UTM zone 34N system (EPSG:32634). Geo Transform can convert the data from the original system to the standard WGS 84, which Geo Map is using. After we set the correct system for transformation, we press <em>Commit</em> to output the data.</p>
 <p>In the second Geo Map, we can see the data is now plotted correctly. All the found shards are placed on the Antikythera island.</p>
-<a href="/widget-catalog/geo/images/__optimized-images__/GeoTransform-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2940" height="1614" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/geo/images/__optimized-images__/GeoTransform-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Geo Transform\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Transform geographic coordinates from one system to another.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: data with transformed coordinates.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Geo Transform\"\n      }), \" widget converts latitude and longitude data from one geodesic system to another. It uses \", _jsx(_components.a, {\n        href: \"https://pyproj4.github.io/pyproj/stable/\",\n        children: \"pyproj\"\n      }), \" library for the conversion.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/geo/images/__optimized-images__/GeoTransform.png\",\n      alt: \"\",\n      width: \"450\",\n      height: \"434\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Set latitude and longitude attributes, if the widget didn't recognize them automatically.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Geodesic systems used for transformation. By default, the output system is set to the latest revision of the World Geodesic System, WGS 84.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \" to apply the transformation.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the example, we will use the Antikythera data, an archaeological dataset describing the shards found on the Greek island Antikythera. The dataset can be found \", _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\",\n        children: \"here\"\n      }), \". We will use the easy way and simply copy-paste the URL of the data into the URL line of the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/data/file/\",\n        children: \"File\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.a, {\n        href: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\",\n        children: \"https://archaeologydataservice.ac.uk/catalogue/adsdata/arch-1115-2/dissemination/csv/pottery/pottery.csv\u0026hs=true\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Latitude and longitude are encoded in the \", _jsx(_components.em, {\n        children: \"Xsugg\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Ysugg\"\n      }), \" variables. But if we plot these variables, we cannot see anything in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/geo/geomap\",\n        children: \"Geo Map\"\n      }), \". The widget raises a warning stating the points are outside the specified range for the map.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is because the data is encoded in the WGS 84 / UTM zone 34N system (EPSG:32634). Geo Transform can convert the data from the original system to the standard WGS 84, which Geo Map is using. After we set the correct system for transformation, we press \", _jsx(_components.em, {\n        children: \"Commit\"\n      }), \" to output the data.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the second Geo Map, we can see the data is now plotted correctly. All the found shards are placed on the Antikythera island.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/geo/images/__optimized-images__/GeoTransform-Example.png\",\n      alt: \"\",\n      width: \"2940\",\n      height: \"1614\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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@@ -210,4 +210,4 @@ <h2>Example</h2>
 <p>We will use the default embedder <em>SqueezeNet</em>. The widget will automatically start retrieving image vectors from the server.</p>
 <a href="/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1171" height="869" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example1.png"/></a>
 <p>Once the computation is done, you can observe the enhanced data in a <strong>Data Table</strong>. With the retrieved embeddings, you can continue with any machine learning method Orange offers. Below is an example for clustering.</p>
-<a href="/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2140" height="1658" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Image Embedding\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image embedding through deep neural networks.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Images: List of images.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Embeddings: Images represented with a vector of numbers.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Skipped Images: List of images where embeddings were not calculated.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \" reads images and uploads them to a remote server or evaluate them locally. Deep learning models are used to calculate a feature vector for each image. It returns an enhanced data table with additional columns (image descriptors).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Images can be imported with \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/importimages\",\n        children: \"Import Images\"\n      }), \" widget or as paths to images in a spreadsheet. In this case the column with images paths needs a three-row header with \", _jsx(_components.em, {\n        children: \"type=image\"\n      }), \" label in the third row.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/header-example.png\",\n      alt: \"\",\n      width: \"1794\",\n      height: \"752\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image Embedding offers several embedders, each trained for a specific task. 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Images sent to the server are not stored anywhere.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-stamped.png\",\n      alt: \"\",\n      width: \"339\",\n      height: \"340\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Information on the number of embedded images and images skipped.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Settings:\"\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [\"\\n\", _jsxs(_components.p, {\n              children: [_jsx(_components.em, {\n                children: \"Image attribute\"\n              }), \": attribute containing images you wish to embed\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"\\n\", _jsxs(_components.p, {\n              children: [_jsx(_components.em, {\n                children: \"Embedder\"\n              }), \":\"]\n            }), \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [\"SqueezeNet: \", _jsx(_components.a, {\n                  href: \"https://arxiv.org/abs/1602.07360\",\n                  children: \"Small and fast\"\n                }), \" model for image recognition trained on ImageNet.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [\"Inception v3: \", _jsx(_components.a, {\n                  href: \"https://arxiv.org/abs/1512.00567\",\n                  children: \"Google's Inception v3\"\n                }), \" model trained on ImageNet.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [\"VGG-16: \", _jsx(_components.a, {\n                  href: \"https://arxiv.org/abs/1409.1556\",\n                  children: \"16-layer image recognition model\"\n                }), \" trained on ImageNet.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [\"VGG-19: \", _jsx(_components.a, {\n                  href: \"https://arxiv.org/abs/1409.1556\",\n                  children: \"19-layer image recognition model\"\n                }), \" trained on ImageNet.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [\"Painters: A model trained to \", _jsx(_components.a, {\n                  href: \"http://blog.kaggle.com/2016/11/17/painter-by-numbers-competition-1st-place-winners-interview-nejc-ilenic/\",\n                  children: \"predict painters from artwork images\"\n                }), \".\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [\"DeepLoc: A model trained to analyze \", _jsx(_components.a, {\n                  href: \"https://www.ncbi.nlm.nih.gov/pubmed/29036616\",\n                  children: \"yeast cell images\"\n                }), \".\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Tick the box on the left to start the embedding automatically. Alternatively, click \", _jsx(_components.em, {\n            children: \"Apply\"\n          }), \". To cancel the embedding, click \", _jsx(_components.em, {\n            children: \"Cancel\"\n          }), \".\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Access help.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Embedders\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"InceptionV3\"\n      }), \" is Google’s deep neural network for image recognition. It is trained on the ImageNet data set. The model we are using is available \", _jsx(_components.a, {\n        href: \"http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz\",\n        children: \"here\"\n      }), \". For the embedding, we use the activations of the penultimate layer of the model, which represents images with vectors.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"SqueezeNet\"\n      }), \" is a deep model for image recognition that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. The model is trained on the ImageNet dataset. We re-implemented the SqueezeNet by using weights from the \", _jsx(_components.a, {\n        href: \"https://github.com/DeepScale/SqueezeNet\",\n        children: \"author’s pretrained model\"\n      }), \". We use activations from pre-softmax (\", _jsx(_components.code, {\n        children: \"flatten10\"\n      }), \") layer as an embedding.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"VGG16\"\n      }), \" and \", _jsx(_components.strong, {\n        children: \"VGG19\"\n      }), \" are deep neural networks for image recognition proposed by Visual Geometry Group from the University of Oxford. They are trained on the ImageNet data set. We use a \", _jsx(_components.a, {\n        href: \"https://github.com/machrisaa/tensorflow-vgg\",\n        children: \"community implementation\"\n      }), \" of networks with original weights. As an embedding, we use activations of the penultimate layer - \", _jsx(_components.code, {\n        children: \"fc7\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Image Embedding also includes \", _jsx(_components.a, {\n        href: \"https://github.com/inejc/painters\",\n        children: _jsx(_components.strong, {\n          children: \"Painters\"\n        })\n      }), \", an embedder that was trained on 79,433 images of paintings by 1,584 painters and won Kaggle’s Painter by Numbers competition. Activations of the penultimate layer of the network are used as an embedding.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"DeepLoc\"\n      }), \" is a convolutional network trained on 21,882 images of single cells that were manually assigned to one of 15 localization compartments. We use the pre-trained network proposed by \", _jsx(_components.a, {\n        href: \"https://github.com/okraus/DeepLoc\",\n        children: \"authors\"\n      }), \". The embeddings are activations of penultimate layer \", _jsx(_components.code, {\n        children: \"fc_2\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"An \", _jsx(_components.a, {\n        href: \"https://www.nature.com/articles/s41467-019-12397-x\",\n        children: \"article\"\n      }), \" by Godec et al. (2019) explains how the embeddings work and how to use it in Orange.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let us first import images from a folder with \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/importimages\",\n        children: \"Import Images\"\n      }), \". We have three images of an orange, a banana and a strawberry in a folder called Fruits. From \", _jsx(_components.strong, {\n        children: \"Import Images\"\n      }), \" we will send a data table containing a column with image paths to \", _jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use the default embedder \", _jsx(_components.em, {\n        children: \"SqueezeNet\"\n      }), \". The widget will automatically start retrieving image vectors from the server.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example1.png\",\n      alt: \"\",\n      width: \"1171\",\n      height: \"869\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Once the computation is done, you can observe the enhanced data in a \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \". With the retrieved embeddings, you can continue with any machine learning method Orange offers. Below is an example for clustering.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example2.png\",\n      alt: \"\",\n      width: \"2140\",\n      height: \"1658\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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Plot","category":"Evaluate","keywords":["calibration","plot"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Calibration Plot.png","url":"calibrationplot"}]],["Unsupervised",[{"title":"Distance File","category":"Unsupervised","keywords":["distance file","load","read","open"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance File.png","url":"distancefile"},{"title":"Distance Matrix","category":"Unsupervised","keywords":["distance","matrix"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Matrix.png","url":"distancematrix"},{"title":"t-SNE","category":"Unsupervised","keywords":["t-sne","tsne"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-t-SNE.png","url":"tsne"},{"title":"Correlations","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correlations.png","url":"correlations"},{"title":"Distance 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+<a href="/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2140" height="1658" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/ImageEmbedding-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Image Embedding\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Image embedding through deep neural networks.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Images: List of images.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Embeddings: Images represented with a vector of numbers.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Skipped Images: List of images where embeddings were not calculated.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Image Embedding\"\n      }), \" reads images and uploads them to a remote server or evaluate them locally. 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 <p>Displays images in a similarity grid.</p>
 <p><strong>Inputs</strong></p>
@@ -178,4 +178,4 @@ <h2>Example</h2>
 <p><strong>Image Grid</strong> can be used to visualize similarity of images in a 2D projection. We have used 5 images of fruits and vegetables, namely orange, banana, strawberry, broccoli and cauliflower.</p>
 <p>We loaded the images with <a href="importimages.md">Import Images</a> and embedded them with Inception v3 embedder in <a href="/widget-catalog/image-analytics/imageembedding">Image Embedding</a>.</p>
 <p>Finally, we visualized the images in <strong>Image Grid</strong>. It is obvious that broccoli and cauliflower and much more alike than strawberry and banana.</p>
-<a href="/widget-catalog/image-analytics/images/__optimized-images__/ImageGrid-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2030" height="1400" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/ImageGrid-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Image Grid\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays images in a similarity grid.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Embeddings: Image embeddings from Image Embedding widget.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: A subset of embeddings or images.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Images: Images from the dataset with an additional column specifying if the image is selected or the group, if there are several.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Images: Selected images with an additional column specifying the group.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Image Grid\"\n      }), \" widget can display images from a dataset in a similarity grid - images with similar content are placed closer to each other. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/image-analytics/importimages/">Import Images</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/image-analytics/imageviewer/">Image Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/image-analytics/imageembedding/">Image Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/image-analytics/imagegrid/">Image Grid</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/image-analytics/saveimages/">Save Images</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Image Viewer</h1>
 <p>Displays images that come with a data set.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@ <h2>Examples</h2>
 <p>A very simple way to use this widget is to connect the <strong>File</strong> widget with <strong>Image Viewer</strong> and see all the images that come with your data set. You can also visualize images from <a href="/widget-catalog/image-analytics/importimages">Import Images</a>.</p>
 <a href="/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example1.png" data-gallery="true"><img alt="" loading="lazy" width="1317" height="849" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example1.png"/></a>
 <p>Alternatively, you can visualize only selected instances, as shown in the example below.</p>
-<a href="/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1279" height="962" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Image Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays images that come with a data set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: A data set with images.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Images that come with the data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected images: Images selected in the widget.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Image Viewer\"\n      }), \" widget can display images from a data set, which are\\nstored locally or on the internet. The widget will look for an attribute with \", _jsx(_components.em, {\n        children: \"type=image\"\n      }), \" in the third header row. It can be used for image comparison, while looking for similarities or discrepancies between selected data instances (e.g. bacterial growth or bitmap representations of handwriting).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/ImageViewer-stamped.png\",\n      alt: \"\",\n      width: \"822\",\n      height: \"645\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the column with image data (links).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the column with image titles.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Zoom in or out.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Saves the visualization in a file.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Tick the box on the left to commit changes automatically.\\nAlternatively, click \", _jsx(_components.em, {\n          children: \"Send\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A very simple way to use this widget is to connect the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget with \", _jsx(_components.strong, {\n        children: \"Image Viewer\"\n      }), \" and see all the images that come with your data set. 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+<a href="/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1279" height="962" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Image Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays images that come with a data set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: A data set with images.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Images that come with the data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected images: Images selected in the widget.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Image Viewer\"\n      }), \" widget can display images from a data set, which are\\nstored locally or on the internet. The widget will look for an attribute with \", _jsx(_components.em, {\n        children: \"type=image\"\n      }), \" in the third header row. It can be used for image comparison, while looking for similarities or discrepancies between selected data instances (e.g. bacterial growth or bitmap representations of handwriting).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/ImageViewer-stamped.png\",\n      alt: \"\",\n      width: \"822\",\n      height: \"645\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the column with image data (links).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the column with image titles.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Zoom in or out.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Saves the visualization in a file.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Tick the box on the left to commit changes automatically.\\nAlternatively, click \", _jsx(_components.em, {\n          children: \"Send\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A very simple way to use this widget is to connect the \", _jsx(_components.strong, {\n        children: \"File\"\n      }), \" widget with \", _jsx(_components.strong, {\n        children: \"Image Viewer\"\n      }), \" and see all the images that come with your data set. You can also visualize images from \", _jsx(_components.a, {\n        href: \"/widget-catalog/image-analytics/importimages\",\n        children: \"Import Images\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example1.png\",\n      alt: \"\",\n      width: \"1317\",\n      height: \"849\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Alternatively, you can visualize only selected instances, as shown in the example below.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/image-viewer-example2.png\",\n      alt: \"\",\n      width: \"1279\",\n      height: \"962\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, 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Network","category":"Model","keywords":["neural network","mlp"],"background":"#FAC1D9","icon":"widget-icons/Model-Neural Network.png","url":"neuralnetwork"},{"title":"Stochastic Gradient Descent","category":"Model","keywords":["stochastic gradient descent","sgd"],"background":"#FAC1D9","icon":"widget-icons/Model-Stochastic Gradient Descent.png","url":"stochasticgradient"},{"title":"Stacking","category":"Model","keywords":[],"background":"#FAC1D9","icon":"widget-icons/Model-Stacking.png","url":"stacking"},{"title":"Save Model","category":"Model","keywords":["save model","save"],"background":"#FAC1D9","icon":"widget-icons/Model-Save Model.png","url":"savemodel"},{"title":"Load Model","category":"Model","keywords":["load model","file","open","model"],"background":"#FAC1D9","icon":"widget-icons/Model-Load Model.png","url":"loadmodel"}]],["Evaluate",[{"title":"Test and Score","category":"Evaluate","keywords":["test and score","cross 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Plot","category":"Evaluate","keywords":["calibration","plot"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Calibration Plot.png","url":"calibrationplot"}]],["Unsupervised",[{"title":"Distance File","category":"Unsupervised","keywords":["distance file","load","read","open"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance File.png","url":"distancefile"},{"title":"Distance Matrix","category":"Unsupervised","keywords":["distance","matrix"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Matrix.png","url":"distancematrix"},{"title":"t-SNE","category":"Unsupervised","keywords":["t-sne","tsne"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-t-SNE.png","url":"tsne"},{"title":"Correlations","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correlations.png","url":"correlations"},{"title":"Distance 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 <p>Import images from a directory(s).</p>
 <p><strong>Outputs</strong></p>
@@ -176,4 +176,4 @@ <h2>Example</h2>
 <p>Then we will use <strong>Test &amp; Score</strong> and <strong>Logistic Regression</strong>, to build a model for predicting the author of a painting. We get a perfect score? How come? It turns out, these were the images the <em>Painters</em> embedder was trained on, so a high accuracy is expected.</p>
 <a href="/widget-catalog/image-analytics/images/__optimized-images__/ImportImages-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2494" height="1924" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/ImportImages-Example.png"/></a>
 <h2>Note</h2>
-<p>If you have a list of images in a tabular format, add <em>type=image</em> to the third header row of the column containing the path to images.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Import Images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Import images from a directory(s).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Dataset describing one image in each row.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Import Images\"\n      }), \" walks through a directory and returs one row per located image. 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+<p>If you have a list of images in a tabular format, add <em>type=image</em> to the third header row of the column containing the path to images.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Import Images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Import images from a directory(s).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Dataset describing one image in each row.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Import Images\"\n      }), \" walks through a directory and returs one row per located image. 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 <p>Save images in the directory structure.</p>
 <p><strong>Inputs</strong></p>
@@ -190,4 +190,4 @@ <h2>Supported Formats</h2>
 </ul>
 <h2>Example</h2>
 <p>Here is a simple example how to use <strong>Save Images</strong>. We loaded 14 paintings from Picasso, sent them to <a href="imageembedding.md">Image Embedding</a> using <em>Painters</em> embedder, then to <a href="https://orange-visual-programming.readthedocs.io/widgets/unsupervised/distances.html">Distances</a> using cosine distance and finally to <a href="https://orange-visual-programming.readthedocs.io/widgets/unsupervised/hierarchicalclustering.html">Hierarchical Clustering</a> to construct a dendrogram. Then we selected a cluster from the plot and saved the images belonging to the selected cluster with <strong>Save Images</strong>.</p>
-<a href="/widget-catalog/image-analytics/images/__optimized-images__/SaveImages-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2244" height="1424" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/image-analytics/images/__optimized-images__/SaveImages-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Save Images\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Save images in the directory structure.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: images to save.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Save Images\"\n      }), \" is a simple widget that saves images sent to its input. 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When a class is present in the data, images will be saved in subdirectories based on the class variable.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/image-analytics/images/__optimized-images__/SaveImages.png\",\n      alt: \"\",\n      width: \"367\",\n      height: \"232\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Attribute containing the path to the image.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Scale images to\"\n        }), \" is ticked, images will be resized to the size used in the selected embedder:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Inception v3: 299x299\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"SqueezeNet: 227x227\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"VGG-16: 224x224\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"VGG-19: 224x224\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Painters: 256x256\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"DeepLoc: 64x64\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"openface: 256x256\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"File format to save images in. See the next section for information on supported formats.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Autosave when receiving new data or settings change\"\n        }), \" is on, images will be saved upon every change. \", _jsx(_components.em, {\n          children: \"Save\"\n        }), \" will save images, while \", _jsx(_components.em, {\n          children: \"Save as...\"\n        }), \" enables the user to set the name and the folder where to save the images.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Supported Formats\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Save Images\"\n      }), \" can save images in the following formats:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \".png\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".jpeg\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".gif\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".tiff\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".pdf\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".bmp\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".eps\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \".ico\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is a simple example how to use \", _jsx(_components.strong, {\n        children: \"Save Images\"\n      }), \". 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src="/widget-catalog/widget-icons/__optimized-images__/Transform-Concatenate.png"/><p>Concatenate</p></div></a><a href="/widget-catalog/transform/select-by-data-index/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Select by Data Index" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Select by Data Index.png"/><p>Select by Data Index</p></div></a><a href="/widget-catalog/transform/unique/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Unique" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Unique.png"/><p>Unique</p></div></a><a href="/widget-catalog/transform/aggregatecolumns/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Aggregate Columns" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Aggregate Columns.png"/><p>Aggregate Columns</p></div></a><a href="/widget-catalog/transform/groupby/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Group by" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Group by.png"/><p>Group by</p></div></a><a href="/widget-catalog/transform/pivot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pivot Table" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Pivot Table.png"/><p>Pivot Table</p></div></a><a href="/widget-catalog/transform/applydomain/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Apply Domain" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Apply Domain.png"/><p>Apply Domain</p></div></a><a href="/widget-catalog/transform/preprocess/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Preprocess" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Preprocess.png"/><p>Preprocess</p></div></a><a href="/widget-catalog/transform/impute/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Impute" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Impute.png"/><p>Impute</p></div></a><a href="/widget-catalog/transform/continuize/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Continuize" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Continuize.png"/><p>Continuize</p></div></a><a href="/widget-catalog/transform/discretize/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Discretize" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Discretize.png"/><p>Discretize</p></div></a><a href="/widget-catalog/transform/randomize/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Randomize" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Randomize.png"/><p>Randomize</p></div></a><a href="/widget-catalog/transform/purgedomain/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Purge Domain" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Purge Domain.png"/><p>Purge Domain</p></div></a><a href="/widget-catalog/transform/melt/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Melt" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Melt.png"/><p>Melt</p></div></a><a href="/widget-catalog/transform/formula/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Formula" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Formula.png"/><p>Formula</p></div></a><a href="/widget-catalog/transform/createclass/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Create Class" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Create Class.png"/><p>Create Class</p></div></a><a href="/widget-catalog/transform/createinstance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Create Instance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Create Instance.png"/><p>Create Instance</p></div></a><a href="/widget-catalog/transform/pythonscript/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Python Script" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Python Script.png"/><p>Python Script</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Visualize</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/visualize/treeviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tree Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Tree Viewer.png"/><p>Tree Viewer</p></div></a><a href="/widget-catalog/visualize/boxplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Box Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Box Plot.png"/><p>Box Plot</p></div></a><a href="/widget-catalog/visualize/violinplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Violin Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Violin Plot.png"/><p>Violin Plot</p></div></a><a href="/widget-catalog/visualize/distributions/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distributions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Distributions.png"/><p>Distributions</p></div></a><a href="/widget-catalog/visualize/scatterplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Scatter Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Scatter Plot.png"/><p>Scatter Plot</p></div></a><a href="/widget-catalog/visualize/lineplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Line Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Line Plot.png"/><p>Line Plot</p></div></a><a href="/widget-catalog/visualize/barplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Bar Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Bar Plot.png"/><p>Bar Plot</p></div></a><a href="/widget-catalog/visualize/sievediagram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Sieve Diagram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Sieve Diagram.png"/><p>Sieve Diagram</p></div></a><a href="/widget-catalog/visualize/mosaicdisplay/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Mosaic Display" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Mosaic Display.png"/><p>Mosaic Display</p></div></a><a href="/widget-catalog/visualize/freeviz/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="FreeViz" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-FreeViz.png"/><p>FreeViz</p></div></a><a href="/widget-catalog/visualize/linearprojection/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Linear Projection" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Linear Projection.png"/><p>Linear Projection</p></div></a><a href="/widget-catalog/visualize/radviz/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Radviz" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Radviz.png"/><p>Radviz</p></div></a><a href="/widget-catalog/visualize/heatmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Heat Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Heat Map.png"/><p>Heat Map</p></div></a><a href="/widget-catalog/visualize/venndiagram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Venn Diagram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Venn Diagram.png"/><p>Venn Diagram</p></div></a><a href="/widget-catalog/visualize/silhouetteplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Silhouette Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Silhouette Plot.png"/><p>Silhouette Plot</p></div></a><a href="/widget-catalog/visualize/pythagoreantree/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pythagorean Tree" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Pythagorean Tree.png"/><p>Pythagorean Tree</p></div></a><a href="/widget-catalog/visualize/pythagoreanforest/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pythagorean Forest" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Pythagorean Forest.png"/><p>Pythagorean Forest</p></div></a><a href="/widget-catalog/visualize/cn2ruleviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="CN2 Rule Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-CN2 Rule Viewer.png"/><p>CN2 Rule Viewer</p></div></a><a href="/widget-catalog/visualize/nomogram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Nomogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Nomogram.png"/><p>Nomogram</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Model</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/model/constant/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Constant" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Constant.png"/><p>Constant</p></div></a><a href="/widget-catalog/model/cn2ruleinduction/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="CN2 Rule Induction" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-CN2 Rule Induction.png"/><p>CN2 Rule Induction</p></div></a><a href="/widget-catalog/model/calibratedlearner/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Calibrated Learner" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Calibrated Learner.png"/><p>Calibrated Learner</p></div></a><a href="/widget-catalog/model/knn/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="kNN" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-kNN.png"/><p>kNN</p></div></a><a href="/widget-catalog/model/tree/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tree" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Tree.png"/><p>Tree</p></div></a><a href="/widget-catalog/model/randomforest/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Random Forest" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Random Forest.png"/><p>Random Forest</p></div></a><a href="/widget-catalog/model/gradientboosting/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gradient Boosting" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Gradient Boosting.png"/><p>Gradient Boosting</p></div></a><a href="/widget-catalog/model/svm/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="SVM" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-SVM.png"/><p>SVM</p></div></a><a href="/widget-catalog/model/linearregression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Linear Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Linear Regression.png"/><p>Linear Regression</p></div></a><a href="/widget-catalog/model/logisticregression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Logistic Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Logistic Regression.png"/><p>Logistic Regression</p></div></a><a href="/widget-catalog/model/naivebayes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Naive Bayes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Naive Bayes.png"/><p>Naive Bayes</p></div></a><a href="/widget-catalog/model/adaboost/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="AdaBoost" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-AdaBoost.png"/><p>AdaBoost</p></div></a><a href="/widget-catalog/model/curvefit/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Curve Fit" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Curve Fit.png"/><p>Curve Fit</p></div></a><a href="/widget-catalog/model/neuralnetwork/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Neural Network" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Neural Network.png"/><p>Neural Network</p></div></a><a href="/widget-catalog/model/stochasticgradient/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Stochastic Gradient Descent" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Stochastic Gradient Descent.png"/><p>Stochastic Gradient Descent</p></div></a><a href="/widget-catalog/model/stacking/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Stacking" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Stacking.png"/><p>Stacking</p></div></a><a href="/widget-catalog/model/savemodel/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Save Model.png"/><p>Save Model</p></div></a><a href="/widget-catalog/model/loadmodel/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Load Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Load Model.png"/><p>Load Model</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Evaluate</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/evaluate/testandscore/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Test and Score" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Test and Score.png"/><p>Test and Score</p></div></a><a href="/widget-catalog/evaluate/predictions/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Predictions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Predictions.png"/><p>Predictions</p></div></a><a href="/widget-catalog/evaluate/confusionmatrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Confusion Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Confusion Matrix.png"/><p>Confusion Matrix</p></div></a><a href="/widget-catalog/evaluate/rocanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="ROC Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-ROC Analysis.png"/><p>ROC Analysis</p></div></a><a href="/widget-catalog/evaluate/performancecurve/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Performance Curve" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Performance Curve.png"/><p>Performance Curve</p></div></a><a href="/widget-catalog/evaluate/calibrationplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Calibration Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Calibration Plot.png"/><p>Calibration Plot</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Unsupervised</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/unsupervised/distancefile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance File" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance File.png"/><p>Distance File</p></div></a><a href="/widget-catalog/unsupervised/distancematrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance Matrix.png"/><p>Distance Matrix</p></div></a><a href="/widget-catalog/unsupervised/tsne/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="t-SNE" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-t-SNE.png"/><p>t-SNE</p></div></a><a href="/widget-catalog/unsupervised/correlations/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Correlations" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Correlations.png"/><p>Correlations</p></div></a><a href="/widget-catalog/unsupervised/distancemap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance Map.png"/><p>Distance Map</p></div></a><a href="/widget-catalog/unsupervised/hierarchicalclustering/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Hierarchical Clustering" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Hierarchical Clustering.png"/><p>Hierarchical Clustering</p></div></a><a href="/widget-catalog/unsupervised/kmeans/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="k-Means" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-k-Means.png"/><p>k-Means</p></div></a><a href="/widget-catalog/unsupervised/louvainclustering/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Louvain Clustering" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Louvain Clustering.png"/><p>Louvain Clustering</p></div></a><a href="/widget-catalog/unsupervised/DBSCAN/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="DBSCAN" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-DBSCAN.png"/><p>DBSCAN</p></div></a><a href="/widget-catalog/unsupervised/manifoldlearning/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Manifold Learning" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Manifold Learning.png"/><p>Manifold Learning</p></div></a><a href="/widget-catalog/unsupervised/outliers/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Outliers" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Outliers.png"/><p>Outliers</p></div></a><a href="/widget-catalog/unsupervised/PCA/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="PCA" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-PCA.png"/><p>PCA</p></div></a><a href="/widget-catalog/unsupervised/neighbors/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Neighbors" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Neighbors.png"/><p>Neighbors</p></div></a><a href="/widget-catalog/unsupervised/correspondenceanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Correspondence Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Correspondence Analysis.png"/><p>Correspondence Analysis</p></div></a><a href="/widget-catalog/unsupervised/distances/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distances" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distances.png"/><p>Distances</p></div></a><a href="/widget-catalog/unsupervised/distancetransformation/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance Transformation" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance Transformation.png"/><p>Distance Transformation</p></div></a><a href="/widget-catalog/unsupervised/mds/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="MDS" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-MDS.png"/><p>MDS</p></div></a><a href="/widget-catalog/unsupervised/savedistancematrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Distance Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Save Distance Matrix.png"/><p>Save Distance Matrix</p></div></a><a href="/widget-catalog/unsupervised/selforganizingmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Self-Organizing Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Self-Organizing Map.png"/><p>Self-Organizing Map</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Spectroscopy</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/spectroscopy/spectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Spectra.png"/><p>Spectra</p></div></a><a href="/widget-catalog/spectroscopy/hyperspectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="HyperSpectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-HyperSpectra.png"/><p>HyperSpectra</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Spectral Series" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Spectral Series.png"/><p>Spectral Series</p></div></a><a href="/widget-catalog/spectroscopy/interpolate/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interpolate" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Interpolate.png"/><p>Interpolate</p></div></a><a href="/widget-catalog/spectroscopy/preprocess-spectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Preprocess Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Preprocess Spectra.png"/><p>Preprocess Spectra</p></div></a><a href="/widget-catalog/spectroscopy/integrate-spectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Integrate Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Integrate Spectra.png"/><p>Integrate Spectra</p></div></a><a href="/widget-catalog/spectroscopy/peakfit/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Peak Fit" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Peak Fit.png"/><p>Peak Fit</p></div></a><a href="/widget-catalog/spectroscopy/multifile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Multifile" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Multifile.png"/><p>Multifile</p></div></a><a href="/widget-catalog/spectroscopy/tilefile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tile File" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Tile File.png"/><p>Tile File</p></div></a><a href="/widget-catalog/spectroscopy/average/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Average Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Average Spectra.png"/><p>Average Spectra</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Bin" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Bin.png"/><p>Bin</p></div></a><a href="/widget-catalog/spectroscopy/interferogram-to-spectrum/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interferogram to Spectrum" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Interferogram to Spectrum.png"/><p>Interferogram to Spectrum</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="PLS" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-PLS.png"/><p>PLS</p></div></a><a href="/widget-catalog/spectroscopy/reshape-map/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Reshape Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Reshape Map.png"/><p>Reshape Map</p></div></a><a href="/widget-catalog/spectroscopy/snr/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="SNR" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-SNR.png"/><p>SNR</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Align Stack" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Align Stack.png"/><p>Align Stack</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Text Mining</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/text-mining/corpus-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Corpus" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Corpus.png"/><p>Corpus</p></div></a><a href="/widget-catalog/text-mining/importdocuments/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Import Documents" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Import Documents.png"/><p>Import Documents</p></div></a><a href="/widget-catalog/text-mining/createcorpus/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Create Corpus" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Create Corpus.png"/><p>Create Corpus</p></div></a><a href="/widget-catalog/text-mining/guardian-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="The Guardian" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-The Guardian.png"/><p>The Guardian</p></div></a><a href="/widget-catalog/text-mining/nytimes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="NY Times" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-NY Times.png"/><p>NY Times</p></div></a><a href="/widget-catalog/text-mining/pubmed/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pubmed" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Pubmed.png"/><p>Pubmed</p></div></a><a href="/widget-catalog/text-mining/twitter-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Twitter" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Twitter.png"/><p>Twitter</p></div></a><a href="/widget-catalog/text-mining/wikipedia-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Wikipedia" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Wikipedia.png"/><p>Wikipedia</p></div></a><a href="/widget-catalog/text-mining/preprocesstext/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Preprocess Text" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Preprocess Text.png"/><p>Preprocess Text</p></div></a><a href="/widget-catalog/text-mining/corpustonetwork/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Corpus to Network" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Corpus to Network.png"/><p>Corpus to Network</p></div></a><a href="/widget-catalog/text-mining/bagofwords-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Bag of Words" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Bag of Words.png"/><p>Bag of Words</p></div></a><a href="/widget-catalog/text-mining/documentembedding/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Document Embedding" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Document Embedding.png"/><p>Document Embedding</p></div></a><a href="/widget-catalog/text-mining/similarityhashing/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Similarity Hashing" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Similarity Hashing.png"/><p>Similarity Hashing</p></div></a><a href="/widget-catalog/text-mining/sentimentanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Sentiment Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Sentiment Analysis.png"/><p>Sentiment Analysis</p></div></a><a href="/widget-catalog/text-mining/tweetprofiler/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tweet Profiler" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Tweet Profiler.png"/><p>Tweet Profiler</p></div></a><a href="/widget-catalog/text-mining/topicmodelling-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Topic Modelling" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Topic Modelling.png"/><p>Topic Modelling</p></div></a><a href="/widget-catalog/text-mining/LDAvis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="LDAvis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-LDAvis.png"/><p>LDAvis</p></div></a><a href="/widget-catalog/text-mining/corpusviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Corpus Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Corpus Viewer.png"/><p>Corpus Viewer</p></div></a><a href="/widget-catalog/text-mining/score-documents/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Score Documents" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Score Documents.png"/><p>Score Documents</p></div></a><a href="/widget-catalog/text-mining/wordcloud/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Word Cloud" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Word Cloud.png"/><p>Word Cloud</p></div></a><a href="/widget-catalog/text-mining/concordance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Concordance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Concordance.png"/><p>Concordance</p></div></a><a href="/widget-catalog/text-mining/docmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Document Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Document Map.png"/><p>Document Map</p></div></a><a href="/widget-catalog/text-mining/wordenrichment/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Word Enrichment" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Word Enrichment.png"/><p>Word Enrichment</p></div></a><a href="/widget-catalog/text-mining/duplicatedetection/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Duplicate Detection" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Duplicate Detection.png"/><p>Duplicate Detection</p></div></a><a href="/widget-catalog/text-mining/wordlist/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Word List" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Word List.png"/><p>Word List</p></div></a><a href="/widget-catalog/text-mining/keywords/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Extract Keywords" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Extract Keywords.png"/><p>Extract Keywords</p></div></a><a href="/widget-catalog/text-mining/annotator/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Annotated Corpus Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Annotated Corpus Map.png"/><p>Annotated Corpus Map</p></div></a><a href="/widget-catalog/text-mining/ontology/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Ontology" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Ontology.png"/><p>Ontology</p></div></a><a href="/widget-catalog/text-mining/semanticviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Semantic Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Semantic Viewer.png"/><p>Semantic Viewer</p></div></a><a href="/widget-catalog/text-mining/collocations/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Collocations" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Collocations.png"/><p>Collocations</p></div></a><a href="/widget-catalog/text-mining/statistics/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Statistics" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Statistics.png"/><p>Statistics</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Survival Analysis</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/survival-analysis/as-survival-data/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="As Survival Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-As Survival Data.png"/><p>As Survival Data</p></div></a><a href="/widget-catalog/survival-analysis/kaplan-meier-plot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Kaplan-Meier Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Kaplan-Meier Plot.png"/><p>Kaplan-Meier Plot</p></div></a><a href="/widget-catalog/survival-analysis/cox-regression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Cox regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Cox regression.png"/><p>Cox regression</p></div></a><a href="/widget-catalog/survival-analysis/rank-survival-features/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Rank Survival Features" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Rank Survival Features.png"/><p>Rank Survival Features</p></div></a><a href="/widget-catalog/survival-analysis/stepwise-cox-regression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Stepwise Cox Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Stepwise Cox Regression.png"/><p>Stepwise Cox Regression</p></div></a><a href="/widget-catalog/survival-analysis/cohorts/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Cohorts" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Cohorts.png"/><p>Cohorts</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Bioinformatics</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/bioinformatics/databases_update/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Databases Update" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Databases Update.png"/><p>Databases Update</p></div></a><a href="/widget-catalog/bioinformatics/geo_data_sets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="GEO Data Sets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-GEO Data Sets.png"/><p>GEO Data Sets</p></div></a><a href="/widget-catalog/bioinformatics/dicty_express/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="dictyExpress" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-dictyExpress.png"/><p>dictyExpress</p></div></a><a href="/widget-catalog/bioinformatics/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Genialis Expressions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Genialis Expressions.png"/><p>Genialis Expressions</p></div></a><a href="/widget-catalog/bioinformatics/genes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Genes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Genes.png"/><p>Genes</p></div></a><a href="/widget-catalog/bioinformatics/differential_expression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Differential Expression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Differential Expression.png"/><p>Differential Expression</p></div></a><a href="/widget-catalog/bioinformatics/go_browser/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="GO Browser" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-GO Browser.png"/><p>GO Browser</p></div></a><a href="/widget-catalog/bioinformatics/kegg_pathways/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="KEGG Pathways" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-KEGG Pathways.png"/><p>KEGG Pathways</p></div></a><a href="/widget-catalog/bioinformatics/gene_set_enrichment/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gene Set Enrichment" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Gene Set Enrichment.png"/><p>Gene Set Enrichment</p></div></a><a href="/widget-catalog/bioinformatics/gene_sets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gene Sets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Gene Sets.png"/><p>Gene Sets</p></div></a><a href="/widget-catalog/bioinformatics/cluster_analysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Cluster Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Cluster Analysis.png"/><p>Cluster Analysis</p></div></a><a href="/widget-catalog/bioinformatics/volcano_plot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Volcano Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Volcano Plot.png"/><p>Volcano Plot</p></div></a><a href="/widget-catalog/bioinformatics/homologs/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Homologs" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Homologs.png"/><p>Homologs</p></div></a><a href="/widget-catalog/bioinformatics/marker_genes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Marker Genes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Marker Genes.png"/><p>Marker Genes</p></div></a><a href="/widget-catalog/bioinformatics/annotate_projection/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Annotator" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Annotator.png"/><p>Annotator</p></div></a><a href="/widget-catalog/bioinformatics/single_sample_scoring/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single sample scoring" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Single sample scoring.png"/><p>Single sample scoring</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Single Cell</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/single-cell/load_data/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Load Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Load Data.png"/><p>Load Data</p></div></a><a href="/widget-catalog/single-cell/single_cell_datasets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single Cell Datasets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Single Cell Datasets.png"/><p>Single Cell Datasets</p></div></a><a href="/widget-catalog/single-cell/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Dropout Gene Selection" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Dropout Gene Selection.png"/><p>Dropout Gene Selection</p></div></a><a href="/widget-catalog/single-cell/filter/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Filter" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Filter.png"/><p>Filter</p></div></a><a href="/widget-catalog/single-cell/single_cell_preprocess/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single Cell Preprocess" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Single Cell Preprocess.png"/><p>Single Cell Preprocess</p></div></a><a href="/widget-catalog/single-cell/batch_effect_removal/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Batch Effect Removal" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Batch Effect Removal.png"/><p>Batch Effect Removal</p></div></a><a href="/widget-catalog/single-cell/align_datasets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Align Datasets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Align Datasets.png"/><p>Align Datasets</p></div></a><a href="/widget-catalog/single-cell/score_genes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Score Genes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Score Genes.png"/><p>Score Genes</p></div></a><a href="/widget-catalog/single-cell/score_cells/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Score Cells" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Score Cells.png"/><p>Score Cells</p></div></a><a href="/widget-catalog/single-cell/dot_matrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Dot Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Dot Matrix.png"/><p>Dot Matrix</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Image Analytics</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/image-analytics/importimages/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Import Images" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Import Images.png"/><p>Import Images</p></div></a><a href="/widget-catalog/image-analytics/imageviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Image Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Image Viewer.png"/><p>Image Viewer</p></div></a><a href="/widget-catalog/image-analytics/imageembedding/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Image Embedding" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Image Embedding.png"/><p>Image Embedding</p></div></a><a href="/widget-catalog/image-analytics/imagegrid/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Image Grid" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Image Grid.png"/><p>Image Grid</p></div></a><a href="/widget-catalog/image-analytics/saveimages/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Images" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Save Images.png"/><p>Save Images</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Networks</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/networks/networkfile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network File" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network File.png"/><p>Network File</p></div></a><a href="/widget-catalog/networks/networkexplorer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Explorer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Explorer.png"/><p>Network Explorer</p></div></a><a href="/widget-catalog/networks/networkgenerator/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Generator" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Generator.png"/><p>Network Generator</p></div></a><a href="/widget-catalog/networks/networkanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Analysis.png"/><p>Network Analysis</p></div></a><a href="/widget-catalog/networks/networkclustering/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Clustering" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Clustering.png"/><p>Network Clustering</p></div></a><a href="/widget-catalog/networks/networkofgroups/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Of Groups" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Of Groups.png"/><p>Network Of Groups</p></div></a><a href="/widget-catalog/networks/networkfromdistances/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network From Distances" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network From Distances.png"/><p>Network From Distances</p></div></a><a href="/widget-catalog/networks/singlemode/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single Mode" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Single Mode.png"/><p>Single Mode</p></div></a><a href="/widget-catalog/networks/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Network" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Save Network.png"/><p>Save Network</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Geo</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/geo/geocoding/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Geocoding" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Geocoding.png"/><p>Geocoding</p></div></a><a href="/widget-catalog/geo/geomap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Geo Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Geo Map.png"/><p>Geo Map</p></div></a><a href="/widget-catalog/geo/choroplethmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Choropleth Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Choropleth Map.png"/><p>Choropleth Map</p></div></a><a href="/widget-catalog/geo/geotransform/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Geo Transform" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Geo Transform.png"/><p>Geo Transform</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Educational</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/educational/google-sheets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Google Sheets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Google Sheets.png"/><p>Google Sheets</p></div></a><a href="/widget-catalog/educational/enklik-anketa/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="EnKlik Anketa" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-EnKlik Anketa.png"/><p>EnKlik Anketa</p></div></a><a href="/widget-catalog/educational/interactive-kmeans/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interactive k-Means" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Interactive k-Means.png"/><p>Interactive k-Means</p></div></a><a href="/widget-catalog/educational/gradient-descent/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gradient Descent" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Gradient Descent.png"/><p>Gradient Descent</p></div></a><a href="/widget-catalog/educational/polynomial-regression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Polynomial Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Polynomial Regression.png"/><p>Polynomial Regression</p></div></a><a href="/widget-catalog/educational/polynomial-classification/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Polynomial Classification" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Polynomial Classification.png"/><p>Polynomial Classification</p></div></a><a href="/widget-catalog/educational/pie-chart/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pie Chart" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Pie Chart.png"/><p>Pie Chart</p></div></a><a href="/widget-catalog/educational/random-data/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Random Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Random Data.png"/><p>Random Data</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Time Series</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/time-series/yahoo_finance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Yahoo Finance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Yahoo Finance.png"/><p>Yahoo Finance</p></div></a><a href="/widget-catalog/time-series/as_timeseries/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="As Timeseries" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-As Timeseries.png"/><p>As Timeseries</p></div></a><a href="/widget-catalog/time-series/interpolate/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interpolate" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Interpolate.png"/><p>Interpolate</p></div></a><a href="/widget-catalog/time-series/moving_transform_w/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Moving Transform" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Moving Transform.png"/><p>Moving Transform</p></div></a><a href="/widget-catalog/time-series/line_chart/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Line Chart" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Line Chart.png"/><p>Line Chart</p></div></a><a href="/widget-catalog/time-series/periodogram_w/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Periodogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Periodogram.png"/><p>Periodogram</p></div></a><a href="/widget-catalog/time-series/correlogram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Correlogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Correlogram.png"/><p>Correlogram</p></div></a><a href="/widget-catalog/time-series/spiralogram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Spiralogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Spiralogram.png"/><p>Spiralogram</p></div></a><a href="/widget-catalog/time-series/granger_causality/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Granger Causality" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Granger Causality.png"/><p>Granger Causality</p></div></a><a href="/widget-catalog/time-series/arima/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="ARIMA Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-ARIMA Model.png"/><p>ARIMA Model</p></div></a><a href="/widget-catalog/time-series/var/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="VAR Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-VAR Model.png"/><p>VAR Model</p></div></a><a href="/widget-catalog/time-series/model_evaluation_w/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Model Evaluation" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Model Evaluation.png"/><p>Model Evaluation</p></div></a><a href="/widget-catalog/time-series/time_slice/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Time Slice" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Time Slice.png"/><p>Time Slice</p></div></a><a href="/widget-catalog/time-series/difference/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Difference" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Difference.png"/><p>Difference</p></div></a><a href="/widget-catalog/time-series/seasonal_adjustment/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Seasonal Adjustment" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Seasonal Adjustment.png"/><p>Seasonal Adjustment</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Associate</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/associate/frequentitemsets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Frequent Itemsets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Associate-Frequent Itemsets.png"/><p>Frequent Itemsets</p></div></a><a href="/widget-catalog/associate/associationrules/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Association Rules" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Associate-Association Rules.png"/><p>Association Rules</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Explain</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/explain/permutation-importance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Feature Importance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Feature Importance.png"/><p>Feature Importance</p></div></a><a href="/widget-catalog/explain/explain-model/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Explain Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Explain Model.png"/><p>Explain Model</p></div></a><a href="/widget-catalog/explain/explain-prediction/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Explain Prediction" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Explain Prediction.png"/><p>Explain Prediction</p></div></a><a href="/widget-catalog/explain/explain-predictions/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Explain Predictions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Explain Predictions.png"/><p>Explain Predictions</p></div></a><a href="/widget-catalog/explain/ice/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="ICE" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-ICE.png"/><p>ICE</p></div></a></div></div></div></div></main><footer 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Domain" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Data-Edit Domain.png"/><p>Edit Domain</p></div></a><a href="/widget-catalog/data/color/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Color" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Data-Color.png"/><p>Color</p></div></a><a href="/widget-catalog/data/featurestatistics/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Feature Statistics" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Data-Feature Statistics.png"/><p>Feature Statistics</p></div></a><a href="/widget-catalog/data/save/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Data-Save Data.png"/><p>Save Data</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Transform</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/transform/datasampler/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Data Sampler" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Data Sampler.png"/><p>Data Sampler</p></div></a><a href="/widget-catalog/transform/selectcolumns/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Select Columns" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Select Columns.png"/><p>Select Columns</p></div></a><a href="/widget-catalog/transform/selectrows/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Select Rows" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Select Rows.png"/><p>Select Rows</p></div></a><a href="/widget-catalog/transform/transpose/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Transpose" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Transpose.png"/><p>Transpose</p></div></a><a href="/widget-catalog/transform/mergedata/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Merge Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Merge Data.png"/><p>Merge Data</p></div></a><a href="/widget-catalog/transform/concatenate/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Concatenate" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Concatenate.png"/><p>Concatenate</p></div></a><a href="/widget-catalog/transform/select-by-data-index/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Select by Data Index" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Select by Data Index.png"/><p>Select by Data Index</p></div></a><a href="/widget-catalog/transform/unique/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Unique" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Unique.png"/><p>Unique</p></div></a><a href="/widget-catalog/transform/aggregatecolumns/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Aggregate Columns" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Aggregate Columns.png"/><p>Aggregate Columns</p></div></a><a href="/widget-catalog/transform/groupby/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Group by" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Group by.png"/><p>Group by</p></div></a><a href="/widget-catalog/transform/pivot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pivot Table" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Pivot Table.png"/><p>Pivot Table</p></div></a><a href="/widget-catalog/transform/applydomain/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Apply Domain" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Apply Domain.png"/><p>Apply Domain</p></div></a><a href="/widget-catalog/transform/preprocess/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Preprocess" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Preprocess.png"/><p>Preprocess</p></div></a><a href="/widget-catalog/transform/impute/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Impute" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Impute.png"/><p>Impute</p></div></a><a href="/widget-catalog/transform/continuize/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Continuize" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Continuize.png"/><p>Continuize</p></div></a><a href="/widget-catalog/transform/discretize/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Discretize" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Discretize.png"/><p>Discretize</p></div></a><a href="/widget-catalog/transform/randomize/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Randomize" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Randomize.png"/><p>Randomize</p></div></a><a href="/widget-catalog/transform/purgedomain/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Purge Domain" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Purge Domain.png"/><p>Purge Domain</p></div></a><a href="/widget-catalog/transform/melt/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Melt" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Melt.png"/><p>Melt</p></div></a><a href="/widget-catalog/transform/formula/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Formula" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Formula.png"/><p>Formula</p></div></a><a href="/widget-catalog/transform/createclass/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Create Class" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Create Class.png"/><p>Create Class</p></div></a><a href="/widget-catalog/transform/createinstance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Create Instance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Create Instance.png"/><p>Create Instance</p></div></a><a href="/widget-catalog/transform/pythonscript/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Python Script" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Transform-Python Script.png"/><p>Python Script</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Visualize</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/visualize/treeviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tree Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Tree Viewer.png"/><p>Tree Viewer</p></div></a><a href="/widget-catalog/visualize/boxplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Box Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Box Plot.png"/><p>Box Plot</p></div></a><a href="/widget-catalog/visualize/violinplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Violin Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Violin Plot.png"/><p>Violin Plot</p></div></a><a href="/widget-catalog/visualize/distributions/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distributions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Distributions.png"/><p>Distributions</p></div></a><a href="/widget-catalog/visualize/scatterplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Scatter Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Scatter Plot.png"/><p>Scatter Plot</p></div></a><a href="/widget-catalog/visualize/lineplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Line Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Line Plot.png"/><p>Line Plot</p></div></a><a href="/widget-catalog/visualize/barplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Bar Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Bar Plot.png"/><p>Bar Plot</p></div></a><a href="/widget-catalog/visualize/sievediagram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Sieve Diagram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Sieve Diagram.png"/><p>Sieve Diagram</p></div></a><a href="/widget-catalog/visualize/mosaicdisplay/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Mosaic Display" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Mosaic Display.png"/><p>Mosaic Display</p></div></a><a href="/widget-catalog/visualize/freeviz/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="FreeViz" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-FreeViz.png"/><p>FreeViz</p></div></a><a href="/widget-catalog/visualize/linearprojection/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Linear Projection" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Linear Projection.png"/><p>Linear Projection</p></div></a><a href="/widget-catalog/visualize/radviz/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Radviz" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Radviz.png"/><p>Radviz</p></div></a><a href="/widget-catalog/visualize/heatmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Heat Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Heat Map.png"/><p>Heat Map</p></div></a><a href="/widget-catalog/visualize/venndiagram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Venn Diagram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Venn Diagram.png"/><p>Venn Diagram</p></div></a><a href="/widget-catalog/visualize/silhouetteplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Silhouette Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Silhouette Plot.png"/><p>Silhouette Plot</p></div></a><a href="/widget-catalog/visualize/pythagoreantree/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pythagorean Tree" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Pythagorean Tree.png"/><p>Pythagorean Tree</p></div></a><a href="/widget-catalog/visualize/pythagoreanforest/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pythagorean Forest" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Pythagorean Forest.png"/><p>Pythagorean Forest</p></div></a><a href="/widget-catalog/visualize/cn2ruleviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="CN2 Rule Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-CN2 Rule Viewer.png"/><p>CN2 Rule Viewer</p></div></a><a href="/widget-catalog/visualize/nomogram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Nomogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Visualize-Nomogram.png"/><p>Nomogram</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Model</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/model/constant/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Constant" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Constant.png"/><p>Constant</p></div></a><a href="/widget-catalog/model/cn2ruleinduction/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="CN2 Rule Induction" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-CN2 Rule Induction.png"/><p>CN2 Rule Induction</p></div></a><a href="/widget-catalog/model/calibratedlearner/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Calibrated Learner" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Calibrated Learner.png"/><p>Calibrated Learner</p></div></a><a href="/widget-catalog/model/knn/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="kNN" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-kNN.png"/><p>kNN</p></div></a><a href="/widget-catalog/model/tree/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tree" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Tree.png"/><p>Tree</p></div></a><a href="/widget-catalog/model/randomforest/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Random Forest" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Random Forest.png"/><p>Random Forest</p></div></a><a href="/widget-catalog/model/gradientboosting/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gradient Boosting" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Gradient Boosting.png"/><p>Gradient Boosting</p></div></a><a href="/widget-catalog/model/svm/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="SVM" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-SVM.png"/><p>SVM</p></div></a><a href="/widget-catalog/model/linearregression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Linear Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Linear Regression.png"/><p>Linear Regression</p></div></a><a href="/widget-catalog/model/logisticregression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Logistic Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Logistic Regression.png"/><p>Logistic Regression</p></div></a><a href="/widget-catalog/model/naivebayes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Naive Bayes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Naive Bayes.png"/><p>Naive Bayes</p></div></a><a href="/widget-catalog/model/adaboost/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="AdaBoost" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-AdaBoost.png"/><p>AdaBoost</p></div></a><a href="/widget-catalog/model/curvefit/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Curve Fit" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Curve Fit.png"/><p>Curve Fit</p></div></a><a href="/widget-catalog/model/neuralnetwork/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Neural Network" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Neural Network.png"/><p>Neural Network</p></div></a><a href="/widget-catalog/model/stochasticgradient/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Stochastic Gradient Descent" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Stochastic Gradient Descent.png"/><p>Stochastic Gradient Descent</p></div></a><a href="/widget-catalog/model/stacking/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Stacking" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Stacking.png"/><p>Stacking</p></div></a><a href="/widget-catalog/model/savemodel/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Save Model.png"/><p>Save Model</p></div></a><a href="/widget-catalog/model/loadmodel/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Load Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Model-Load Model.png"/><p>Load Model</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Evaluate</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/evaluate/testandscore/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Test and Score" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Test and Score.png"/><p>Test and Score</p></div></a><a href="/widget-catalog/evaluate/predictions/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Predictions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Predictions.png"/><p>Predictions</p></div></a><a href="/widget-catalog/evaluate/confusionmatrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Confusion Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Confusion Matrix.png"/><p>Confusion Matrix</p></div></a><a href="/widget-catalog/evaluate/rocanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="ROC Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-ROC Analysis.png"/><p>ROC Analysis</p></div></a><a href="/widget-catalog/evaluate/performancecurve/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Performance Curve" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Performance Curve.png"/><p>Performance Curve</p></div></a><a href="/widget-catalog/evaluate/calibrationplot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Calibration Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Evaluate-Calibration Plot.png"/><p>Calibration Plot</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Unsupervised</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/unsupervised/distancefile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance File" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance File.png"/><p>Distance File</p></div></a><a href="/widget-catalog/unsupervised/distancematrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance Matrix.png"/><p>Distance Matrix</p></div></a><a href="/widget-catalog/unsupervised/tsne/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="t-SNE" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-t-SNE.png"/><p>t-SNE</p></div></a><a href="/widget-catalog/unsupervised/correlations/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Correlations" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Correlations.png"/><p>Correlations</p></div></a><a href="/widget-catalog/unsupervised/distancemap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance Map.png"/><p>Distance Map</p></div></a><a href="/widget-catalog/unsupervised/hierarchicalclustering/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Hierarchical Clustering" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Hierarchical Clustering.png"/><p>Hierarchical Clustering</p></div></a><a href="/widget-catalog/unsupervised/kmeans/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="k-Means" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-k-Means.png"/><p>k-Means</p></div></a><a href="/widget-catalog/unsupervised/louvainclustering/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Louvain Clustering" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Louvain Clustering.png"/><p>Louvain Clustering</p></div></a><a href="/widget-catalog/unsupervised/DBSCAN/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="DBSCAN" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-DBSCAN.png"/><p>DBSCAN</p></div></a><a href="/widget-catalog/unsupervised/manifoldlearning/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Manifold Learning" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Manifold Learning.png"/><p>Manifold Learning</p></div></a><a href="/widget-catalog/unsupervised/outliers/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Outliers" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Outliers.png"/><p>Outliers</p></div></a><a href="/widget-catalog/unsupervised/PCA/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="PCA" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-PCA.png"/><p>PCA</p></div></a><a href="/widget-catalog/unsupervised/neighbors/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Neighbors" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Neighbors.png"/><p>Neighbors</p></div></a><a href="/widget-catalog/unsupervised/correspondenceanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Correspondence Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Correspondence Analysis.png"/><p>Correspondence Analysis</p></div></a><a href="/widget-catalog/unsupervised/distances/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distances" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distances.png"/><p>Distances</p></div></a><a href="/widget-catalog/unsupervised/distancetransformation/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Distance Transformation" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Distance Transformation.png"/><p>Distance Transformation</p></div></a><a href="/widget-catalog/unsupervised/mds/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="MDS" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-MDS.png"/><p>MDS</p></div></a><a href="/widget-catalog/unsupervised/savedistancematrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Distance Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Save Distance Matrix.png"/><p>Save Distance Matrix</p></div></a><a href="/widget-catalog/unsupervised/selforganizingmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Self-Organizing Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Unsupervised-Self-Organizing Map.png"/><p>Self-Organizing Map</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Spectroscopy</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/spectroscopy/spectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Spectra.png"/><p>Spectra</p></div></a><a href="/widget-catalog/spectroscopy/hyperspectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="HyperSpectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-HyperSpectra.png"/><p>HyperSpectra</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Spectral Series" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Spectral Series.png"/><p>Spectral Series</p></div></a><a href="/widget-catalog/spectroscopy/interpolate/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interpolate" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Interpolate.png"/><p>Interpolate</p></div></a><a href="/widget-catalog/spectroscopy/preprocess-spectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Preprocess Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Preprocess Spectra.png"/><p>Preprocess Spectra</p></div></a><a href="/widget-catalog/spectroscopy/integrate-spectra/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Integrate Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Integrate Spectra.png"/><p>Integrate Spectra</p></div></a><a href="/widget-catalog/spectroscopy/peakfit/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Peak Fit" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Peak Fit.png"/><p>Peak Fit</p></div></a><a href="/widget-catalog/spectroscopy/multifile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Multifile" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Multifile.png"/><p>Multifile</p></div></a><a href="/widget-catalog/spectroscopy/tilefile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tile File" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Tile File.png"/><p>Tile File</p></div></a><a href="/widget-catalog/spectroscopy/average/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Average Spectra" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Average Spectra.png"/><p>Average Spectra</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Bin" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Bin.png"/><p>Bin</p></div></a><a href="/widget-catalog/spectroscopy/interferogram-to-spectrum/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interferogram to Spectrum" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Interferogram to Spectrum.png"/><p>Interferogram to Spectrum</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="PLS" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-PLS.png"/><p>PLS</p></div></a><a href="/widget-catalog/spectroscopy/reshape-map/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Reshape Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Reshape Map.png"/><p>Reshape Map</p></div></a><a href="/widget-catalog/spectroscopy/snr/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="SNR" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-SNR.png"/><p>SNR</p></div></a><a href="/widget-catalog/spectroscopy/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Align Stack" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Spectroscopy-Align Stack.png"/><p>Align Stack</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Text Mining</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/text-mining/corpus-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Corpus" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Corpus.png"/><p>Corpus</p></div></a><a href="/widget-catalog/text-mining/importdocuments/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Import Documents" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Import Documents.png"/><p>Import Documents</p></div></a><a href="/widget-catalog/text-mining/createcorpus/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Create Corpus" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Create Corpus.png"/><p>Create Corpus</p></div></a><a href="/widget-catalog/text-mining/guardian-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="The Guardian" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-The Guardian.png"/><p>The Guardian</p></div></a><a href="/widget-catalog/text-mining/nytimes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="NY Times" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-NY Times.png"/><p>NY Times</p></div></a><a href="/widget-catalog/text-mining/pubmed/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pubmed" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Pubmed.png"/><p>Pubmed</p></div></a><a href="/widget-catalog/text-mining/twitter-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Twitter" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Twitter.png"/><p>Twitter</p></div></a><a href="/widget-catalog/text-mining/wikipedia-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Wikipedia" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Wikipedia.png"/><p>Wikipedia</p></div></a><a href="/widget-catalog/text-mining/preprocesstext/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Preprocess Text" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Preprocess Text.png"/><p>Preprocess Text</p></div></a><a href="/widget-catalog/text-mining/corpustonetwork/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Corpus to Network" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Corpus to Network.png"/><p>Corpus to Network</p></div></a><a href="/widget-catalog/text-mining/bagofwords-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Bag of Words" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Bag of Words.png"/><p>Bag of Words</p></div></a><a href="/widget-catalog/text-mining/documentembedding/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Document Embedding" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Document Embedding.png"/><p>Document Embedding</p></div></a><a href="/widget-catalog/text-mining/similarityhashing/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Similarity Hashing" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Similarity Hashing.png"/><p>Similarity Hashing</p></div></a><a href="/widget-catalog/text-mining/sentimentanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Sentiment Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Sentiment Analysis.png"/><p>Sentiment Analysis</p></div></a><a href="/widget-catalog/text-mining/tweetprofiler/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Tweet Profiler" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Tweet Profiler.png"/><p>Tweet Profiler</p></div></a><a href="/widget-catalog/text-mining/topicmodelling-widget/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Topic Modelling" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Topic Modelling.png"/><p>Topic Modelling</p></div></a><a href="/widget-catalog/text-mining/LDAvis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="LDAvis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-LDAvis.png"/><p>LDAvis</p></div></a><a href="/widget-catalog/text-mining/corpusviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Corpus Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Corpus Viewer.png"/><p>Corpus Viewer</p></div></a><a href="/widget-catalog/text-mining/score-documents/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Score Documents" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Score Documents.png"/><p>Score Documents</p></div></a><a href="/widget-catalog/text-mining/wordcloud/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Word Cloud" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Word Cloud.png"/><p>Word Cloud</p></div></a><a href="/widget-catalog/text-mining/concordance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Concordance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Concordance.png"/><p>Concordance</p></div></a><a href="/widget-catalog/text-mining/docmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Document Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Document Map.png"/><p>Document Map</p></div></a><a href="/widget-catalog/text-mining/wordenrichment/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Word Enrichment" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Word Enrichment.png"/><p>Word Enrichment</p></div></a><a href="/widget-catalog/text-mining/duplicatedetection/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Duplicate Detection" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Duplicate Detection.png"/><p>Duplicate Detection</p></div></a><a href="/widget-catalog/text-mining/wordlist/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Word List" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Word List.png"/><p>Word List</p></div></a><a href="/widget-catalog/text-mining/keywords/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Extract Keywords" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Extract Keywords.png"/><p>Extract Keywords</p></div></a><a href="/widget-catalog/text-mining/annotator/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Annotated Corpus Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Annotated Corpus Map.png"/><p>Annotated Corpus Map</p></div></a><a href="/widget-catalog/text-mining/ontology/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Ontology" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Ontology.png"/><p>Ontology</p></div></a><a href="/widget-catalog/text-mining/semanticviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Semantic Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Semantic Viewer.png"/><p>Semantic Viewer</p></div></a><a href="/widget-catalog/text-mining/collocations/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Collocations" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Collocations.png"/><p>Collocations</p></div></a><a href="/widget-catalog/text-mining/statistics/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Statistics" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Text Mining-Statistics.png"/><p>Statistics</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Survival Analysis</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/survival-analysis/as-survival-data/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="As Survival Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-As Survival Data.png"/><p>As Survival Data</p></div></a><a href="/widget-catalog/survival-analysis/kaplan-meier-plot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Kaplan-Meier Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Kaplan-Meier Plot.png"/><p>Kaplan-Meier Plot</p></div></a><a href="/widget-catalog/survival-analysis/cox-regression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Cox regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Cox regression.png"/><p>Cox regression</p></div></a><a href="/widget-catalog/survival-analysis/rank-survival-features/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Rank Survival Features" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Rank Survival Features.png"/><p>Rank Survival Features</p></div></a><a href="/widget-catalog/survival-analysis/stepwise-cox-regression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Stepwise Cox Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Stepwise Cox Regression.png"/><p>Stepwise Cox Regression</p></div></a><a href="/widget-catalog/survival-analysis/cohorts/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Cohorts" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Survival Analysis-Cohorts.png"/><p>Cohorts</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Bioinformatics</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/bioinformatics/databases_update/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Databases Update" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Databases Update.png"/><p>Databases Update</p></div></a><a href="/widget-catalog/bioinformatics/geo_data_sets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="GEO Data Sets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-GEO Data Sets.png"/><p>GEO Data Sets</p></div></a><a href="/widget-catalog/bioinformatics/dicty_express/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="dictyExpress" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-dictyExpress.png"/><p>dictyExpress</p></div></a><a href="/widget-catalog/bioinformatics/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Genialis Expressions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Genialis Expressions.png"/><p>Genialis Expressions</p></div></a><a href="/widget-catalog/bioinformatics/genes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Genes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Genes.png"/><p>Genes</p></div></a><a href="/widget-catalog/bioinformatics/differential_expression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Differential Expression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Differential Expression.png"/><p>Differential Expression</p></div></a><a href="/widget-catalog/bioinformatics/go_browser/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="GO Browser" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-GO Browser.png"/><p>GO Browser</p></div></a><a href="/widget-catalog/bioinformatics/kegg_pathways/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="KEGG Pathways" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-KEGG Pathways.png"/><p>KEGG Pathways</p></div></a><a href="/widget-catalog/bioinformatics/gene_set_enrichment/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gene Set Enrichment" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Gene Set Enrichment.png"/><p>Gene Set Enrichment</p></div></a><a href="/widget-catalog/bioinformatics/gene_sets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gene Sets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Gene Sets.png"/><p>Gene Sets</p></div></a><a href="/widget-catalog/bioinformatics/cluster_analysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Cluster Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Cluster Analysis.png"/><p>Cluster Analysis</p></div></a><a href="/widget-catalog/bioinformatics/volcano_plot/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Volcano Plot" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Volcano Plot.png"/><p>Volcano Plot</p></div></a><a href="/widget-catalog/bioinformatics/homologs/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Homologs" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Homologs.png"/><p>Homologs</p></div></a><a href="/widget-catalog/bioinformatics/marker_genes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Marker Genes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Marker Genes.png"/><p>Marker Genes</p></div></a><a href="/widget-catalog/bioinformatics/annotate_projection/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Annotator" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Annotator.png"/><p>Annotator</p></div></a><a href="/widget-catalog/bioinformatics/single_sample_scoring/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single sample scoring" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Bioinformatics-Single sample scoring.png"/><p>Single sample scoring</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Single Cell</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/single-cell/load_data/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Load Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Load Data.png"/><p>Load Data</p></div></a><a href="/widget-catalog/single-cell/single_cell_datasets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single Cell Datasets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Single Cell Datasets.png"/><p>Single Cell Datasets</p></div></a><a href="/widget-catalog/single-cell/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Dropout Gene Selection" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Dropout Gene Selection.png"/><p>Dropout Gene Selection</p></div></a><a href="/widget-catalog/single-cell/filter/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Filter" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Filter.png"/><p>Filter</p></div></a><a href="/widget-catalog/single-cell/single_cell_preprocess/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single Cell Preprocess" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Single Cell Preprocess.png"/><p>Single Cell Preprocess</p></div></a><a href="/widget-catalog/single-cell/batch_effect_removal/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Batch Effect Removal" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Batch Effect Removal.png"/><p>Batch Effect Removal</p></div></a><a href="/widget-catalog/single-cell/align_datasets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Align Datasets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Align Datasets.png"/><p>Align Datasets</p></div></a><a href="/widget-catalog/single-cell/score_genes/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Score Genes" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Score Genes.png"/><p>Score Genes</p></div></a><a href="/widget-catalog/single-cell/score_cells/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Score Cells" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Score Cells.png"/><p>Score Cells</p></div></a><a href="/widget-catalog/single-cell/dot_matrix/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Dot Matrix" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Single Cell-Dot Matrix.png"/><p>Dot Matrix</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Image Analytics</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/image-analytics/importimages/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Import Images" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Import Images.png"/><p>Import Images</p></div></a><a href="/widget-catalog/image-analytics/imageviewer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Image Viewer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Image Viewer.png"/><p>Image Viewer</p></div></a><a href="/widget-catalog/image-analytics/imageembedding/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Image Embedding" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Image Embedding.png"/><p>Image Embedding</p></div></a><a href="/widget-catalog/image-analytics/imagegrid/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Image Grid" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Image Grid.png"/><p>Image Grid</p></div></a><a href="/widget-catalog/image-analytics/saveimages/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Images" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Image Analytics-Save Images.png"/><p>Save Images</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Networks</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/networks/networkfile/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network File" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network File.png"/><p>Network File</p></div></a><a href="/widget-catalog/networks/networkexplorer/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Explorer" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Explorer.png"/><p>Network Explorer</p></div></a><a href="/widget-catalog/networks/networkgenerator/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Generator" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Generator.png"/><p>Network Generator</p></div></a><a href="/widget-catalog/networks/networkanalysis/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Analysis" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Analysis.png"/><p>Network Analysis</p></div></a><a href="/widget-catalog/networks/networkclustering/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Clustering" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Clustering.png"/><p>Network Clustering</p></div></a><a href="/widget-catalog/networks/networkofgroups/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network Of Groups" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network Of Groups.png"/><p>Network Of Groups</p></div></a><a href="/widget-catalog/networks/networkfromdistances/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Network From Distances" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Network From Distances.png"/><p>Network From Distances</p></div></a><a href="/widget-catalog/networks/singlemode/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Single Mode" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Single Mode.png"/><p>Single Mode</p></div></a><a href="/widget-catalog/networks/undefined/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Save Network" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Networks-Save Network.png"/><p>Save Network</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Geo</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/geo/geocoding/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Geocoding" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Geocoding.png"/><p>Geocoding</p></div></a><a href="/widget-catalog/geo/geomap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Geo Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Geo Map.png"/><p>Geo Map</p></div></a><a href="/widget-catalog/geo/choroplethmap/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Choropleth Map" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Choropleth Map.png"/><p>Choropleth Map</p></div></a><a href="/widget-catalog/geo/geotransform/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Geo Transform" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Geo-Geo Transform.png"/><p>Geo Transform</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Educational</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/educational/google-sheets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Google Sheets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Google Sheets.png"/><p>Google Sheets</p></div></a><a href="/widget-catalog/educational/enklik-anketa/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="EnKlik Anketa" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-EnKlik Anketa.png"/><p>EnKlik Anketa</p></div></a><a href="/widget-catalog/educational/interactive-kmeans/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interactive k-Means" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Interactive k-Means.png"/><p>Interactive k-Means</p></div></a><a href="/widget-catalog/educational/gradient-descent/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Gradient Descent" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Gradient Descent.png"/><p>Gradient Descent</p></div></a><a href="/widget-catalog/educational/polynomial-regression/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Polynomial Regression" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Polynomial Regression.png"/><p>Polynomial Regression</p></div></a><a href="/widget-catalog/educational/polynomial-classification/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Polynomial Classification" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Polynomial Classification.png"/><p>Polynomial Classification</p></div></a><a href="/widget-catalog/educational/pie-chart/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Pie Chart" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Pie Chart.png"/><p>Pie Chart</p></div></a><a href="/widget-catalog/educational/random-data/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Random Data" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Educational-Random Data.png"/><p>Random Data</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Time Series</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/time-series/yahoo_finance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Yahoo Finance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Yahoo Finance.png"/><p>Yahoo Finance</p></div></a><a href="/widget-catalog/time-series/as_timeseries/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="As Timeseries" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-As Timeseries.png"/><p>As Timeseries</p></div></a><a href="/widget-catalog/time-series/interpolate/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Interpolate" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Interpolate.png"/><p>Interpolate</p></div></a><a href="/widget-catalog/time-series/moving_transform_w/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Moving Transform" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Moving Transform.png"/><p>Moving Transform</p></div></a><a href="/widget-catalog/time-series/line_chart/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Line Chart" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Line Chart.png"/><p>Line Chart</p></div></a><a href="/widget-catalog/time-series/periodogram_w/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Periodogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Periodogram.png"/><p>Periodogram</p></div></a><a href="/widget-catalog/time-series/correlogram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Correlogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Correlogram.png"/><p>Correlogram</p></div></a><a href="/widget-catalog/time-series/spiralogram/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Spiralogram" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Spiralogram.png"/><p>Spiralogram</p></div></a><a href="/widget-catalog/time-series/granger_causality/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Granger Causality" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Granger Causality.png"/><p>Granger Causality</p></div></a><a href="/widget-catalog/time-series/arima/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="ARIMA Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-ARIMA Model.png"/><p>ARIMA Model</p></div></a><a href="/widget-catalog/time-series/var/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="VAR Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-VAR Model.png"/><p>VAR Model</p></div></a><a href="/widget-catalog/time-series/model_evaluation_w/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Model Evaluation" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Model Evaluation.png"/><p>Model Evaluation</p></div></a><a href="/widget-catalog/time-series/time_slice/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Time Slice" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Time Slice.png"/><p>Time Slice</p></div></a><a href="/widget-catalog/time-series/difference/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Difference" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Difference.png"/><p>Difference</p></div></a><a href="/widget-catalog/time-series/seasonal_adjustment/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Seasonal Adjustment" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Time Series-Seasonal Adjustment.png"/><p>Seasonal Adjustment</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Associate</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/associate/frequentitemsets/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Frequent Itemsets" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Associate-Frequent Itemsets.png"/><p>Frequent Itemsets</p></div></a><a href="/widget-catalog/associate/associationrules/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Association Rules" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Associate-Association Rules.png"/><p>Association Rules</p></div></a></div></div><div class="sc-689d4fa0-0 eEGXGY"><h2>Explain</h2><div class="sc-689d4fa0-1 fhSsBP"><a href="/widget-catalog/explain/permutation-importance/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Feature Importance" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Feature Importance.png"/><p>Feature Importance</p></div></a><a href="/widget-catalog/explain/explain-model/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Explain Model" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Explain Model.png"/><p>Explain Model</p></div></a><a href="/widget-catalog/explain/explain-prediction/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Explain Prediction" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Explain Prediction.png"/><p>Explain Prediction</p></div></a><a href="/widget-catalog/explain/explain-predictions/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="Explain Predictions" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-Explain Predictions.png"/><p>Explain Predictions</p></div></a><a href="/widget-catalog/explain/ice/"><div class="sc-689d4fa0-4 gcjhmU"><img alt="ICE" loading="lazy" width="60" height="60" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/widget-icons/__optimized-images__/Explain-ICE.png"/><p>ICE</p></div></a></div></div></div></div></main><footer 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>AdaBoost</h1>
 <p>An ensemble meta-algorithm that combines weak learners and adapts to the &#x27;hardness&#x27; of each training sample.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Examples</h2>
 <p>For classification, we loaded the <em>iris</em> dataset. We used <em>AdaBoost</em>, <a href="../model/tree.md">Tree</a> and <a href="../model/logisticregression.md">Logistic Regression</a> and evaluated the models&#x27; performance in <a href="/widget-catalog/model/../evaluate/testandscore">Test &amp; Score</a>.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/AdaBoost-classification.png" data-gallery="true"><img alt="" loading="lazy" width="1924" height="1460" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/AdaBoost-classification.png"/></a>
 <p>For regression, we loaded the <em>housing</em> dataset, sent the data instances to two different models (<strong>AdaBoost</strong> and <a href="../model/tree.md">Tree</a>) and output them to the <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a> widget.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/AdaBoost-regression.png" data-gallery="true"><img alt="" loading="lazy" width="1750" height="1360" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/AdaBoost-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"AdaBoost\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"An ensemble meta-algorithm that combines weak learners and adapts to the 'hardness' of each training sample.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner: learning algorithm\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: AdaBoost learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/AdaBoost\",\n        children: \"AdaBoost\"\n      }), \" (short for \\\"Adaptive boosting\\\") widget is a machine-learning algorithm, formulated by \", _jsx(_components.a, {\n        href: \"https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf\",\n        children: \"Yoav Freund and Robert Schapire\"\n      }), \". 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\"AdaBoost\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/tree.md\",\n        children: \"Tree\"\n      }), \") and output them to the \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/AdaBoost-regression.png\",\n      alt: \"\",\n      width: \"1750\",\n      height: \"1360\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: 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+<a href="/widget-catalog/model/images/__optimized-images__/AdaBoost-regression.png" data-gallery="true"><img alt="" loading="lazy" width="1750" height="1360" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/AdaBoost-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"AdaBoost\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"An ensemble meta-algorithm that combines weak learners and adapts to the 'hardness' of each training sample.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner: learning algorithm\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: AdaBoost learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/AdaBoost\",\n        children: \"AdaBoost\"\n      }), \" (short for \\\"Adaptive boosting\\\") widget is a machine-learning algorithm, formulated by \", _jsx(_components.a, {\n        href: \"https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf\",\n        children: \"Yoav Freund and Robert Schapire\"\n      }), \". 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@@ -195,4 +195,4 @@ <h2>Example</h2>
 <p>A simple example with <strong>Calibrated Learner</strong>. We are using the <em>titanic</em> data set as the widget requires binary class values (in this case they are &#x27;survived&#x27; and &#x27;not survived&#x27;).</p>
 <p>We will use <a href="/widget-catalog/model/logisticregression">Logistic Regression</a> as the base learner which will we calibrate with the default settings, that is with sigmoid optimization of distribution values and by optimizing the CA.</p>
 <p>Comparing the results with the uncalibrated <strong>Logistic Regression</strong> model we see that the calibrated model performs better.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/Calibrated-Learner-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2148" height="1388" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Calibrated-Learner-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Calibrated Learner\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Wraps another learner with probability calibration and decision threshold optimization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Base Learner: learner to calibrate\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: calibrated learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model using the calibrated learner\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This learner produces a model that calibrates the distribution of class probabilities and optimizes decision threshold. 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+<a href="/widget-catalog/model/images/__optimized-images__/Calibrated-Learner-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2148" height="1388" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Calibrated-Learner-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Calibrated Learner\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Wraps another learner with probability calibration and decision threshold optimization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Base Learner: learner to calibrate\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: calibrated learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model using the calibrated learner\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This learner produces a model that calibrates the distribution of class probabilities and optimizes decision threshold. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>CN2 Rule Induction</h1>
 <p>Induce rules from data using CN2 algorithm.</p>
 <p><strong>Inputs</strong></p>
@@ -222,4 +222,4 @@ <h2>References</h2>
 <li>Clark, Peter and Tim Niblett. &quot;The CN2 Induction Algorithm&quot;, Machine Learning Journal, 3 (4), 261-283, 1989.</li>
 <li>Clark, Peter and Robin Boswell. &quot;Rule Induction with CN2: Some Recent Improvements&quot;, Machine Learning - Proceedings of the 5th European Conference (EWSL-91),151-163, 1991.</li>
 <li>Lavrač, Nada et al. &quot;Subgroup Discovery with CN2-SD&quot;,Journal of Machine Learning Research 5, 153-188, 2004</li>
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Alternatively, press ‘Apply‘ after configuration.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"CN2 Rule Induction uses default preprocessing when no other preprocessors are given. 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We can review and interpret the built model with \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../visualize/cn2ruleviewer\",\n        children: \"CN2 Rule Viewer\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/CN2-visualize.png\",\n      alt: \"\",\n      width: \"2158\",\n      height: \"1368\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The second workflow tests evaluates \", _jsx(_components.strong, {\n        children: \"CN2 Rule Induction\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/tree.md\",\n        children: \"Tree\"\n      }), \" in \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/CN2-classification.png\",\n      alt: \"\",\n      width: \"1834\",\n      height: \"1550\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Fürnkranz, Johannes. \\\"Separate-and-Conquer Rule Learning\\\", Artificial Intelligence Review 13, 3-54, 1999.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Clark, Peter and Tim Niblett. \\\"The CN2 Induction Algorithm\\\", Machine Learning Journal, 3 (4), 261-283, 1989.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Clark, Peter and Robin Boswell. \\\"Rule Induction with CN2: Some Recent Improvements\\\", Machine Learning - Proceedings of the 5th European Conference (EWSL-91),151-163, 1991.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Lavrač, Nada et al. \\\"Subgroup Discovery with CN2-SD\\\",Journal of Machine Learning Research 5, 153-188, 2004\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction 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+</ol></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"CN2 Rule Induction\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Induce rules from data using CN2 algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: CN2 learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"CN2 Rule Classifier: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The CN2 algorithm is a classification technique designed for the efficient induction of simple, comprehensible rules of form \\\"if \", _jsx(_components.em, {\n        children: \"cond\"\n      }), \" then predict \", _jsx(_components.em, {\n        children: \"class\"\n      }), \"\\\", even in domains where noise may be present.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"CN2 Rule Induction\"\n      }), \" works only for classification.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/CN2-stamped.png\",\n      alt: \"\",\n      width: \"654\",\n      height: \"1046\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Name under which the learner appears in other widgets. 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Alternatively, press ‘Apply‘ after configuration.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"CN2 Rule Induction uses default preprocessing when no other preprocessors are given. It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the example below, we have used \", _jsx(_components.em, {\n        children: \"zoo\"\n      }), \" dataset and passed it to \", _jsx(_components.strong, {\n        children: \"CN2 Rule Induction\"\n      }), \". We can review and interpret the built model with \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../visualize/cn2ruleviewer\",\n        children: \"CN2 Rule Viewer\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/CN2-visualize.png\",\n      alt: \"\",\n      width: \"2158\",\n      height: \"1368\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The second workflow tests evaluates \", _jsx(_components.strong, {\n        children: \"CN2 Rule Induction\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/tree.md\",\n        children: \"Tree\"\n      }), \" in \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/CN2-classification.png\",\n      alt: \"\",\n      width: \"1834\",\n      height: \"1550\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Fürnkranz, Johannes. \\\"Separate-and-Conquer Rule Learning\\\", Artificial Intelligence Review 13, 3-54, 1999.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Clark, Peter and Tim Niblett. \\\"The CN2 Induction Algorithm\\\", Machine Learning Journal, 3 (4), 261-283, 1989.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Clark, Peter and Robin Boswell. \\\"Rule Induction with CN2: Some Recent Improvements\\\", Machine Learning - Proceedings of the 5th European Conference (EWSL-91),151-163, 1991.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Lavrač, Nada et al. \\\"Subgroup Discovery with CN2-SD\\\",Journal of Machine Learning Research 5, 153-188, 2004\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction 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 <p>Predict the most frequent class or mean value from the training set.</p>
 <p><strong>Inputs</strong></p>
@@ -181,4 +181,4 @@ <h2>Examples</h2>
 <p>In a typical classification example, we would use this widget to compare the scores of other learning algorithms (such as kNN) with the default scores. Use <em>iris</em> dataset and connect it to <a href="../evaluate/testandscore.md">Test &amp; Score</a>. Then connect <strong>Constant</strong> and <a href="/widget-catalog/model/../model/knn">kNN</a> to <a href="../evaluate/testandscore.md">Test &amp; Score</a> and observe how well <a href="/widget-catalog/model/../model/knn">kNN</a> performs against a constant baseline.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/Constant-classification.png" data-gallery="true"><img alt="" loading="lazy" width="2042" height="1154" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Constant-classification.png"/></a>
 <p>For regression, we use <strong>Constant</strong> to construct a predictor in <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a>. We used the <em>housing</em> dataset. In <strong>Predictions</strong>, you can see that <em>Mean Learner</em> returns one (mean) value for all instances.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/Constant-regression.png" data-gallery="true"><img alt="" loading="lazy" width="2140" height="1050" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Constant-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Constant\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict the most frequent class or mean value from the training set.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: majority/mean learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This learner produces a model that always predicts the\", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Predictive_modelling#Majority_classifier\",\n        children: \"majority\"\n      }), \" for classification tasks and \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Mean\",\n        children: \"mean value\"\n      }), \" for regression tasks.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For classification, when predicting the class value with \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \", the widget will return relative frequencies of the classes in the training set. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Curve Fit</h1>
 <p>Fit a function to data.</p>
 <p><strong>Inputs</strong></p>
@@ -189,4 +189,4 @@ <h2>Preprocessing</h2>
 <p>To remove default preprocessing, connect an empty <a href="/widget-catalog/model/../data/preprocess">Preprocess</a> widget to the learner.</p>
 <h2>Example</h2>
 <p>Below, is a simple workflow with <em>housing</em> dataset. Due to example simplicity we used only a single feature. Unlike the other modelling widgets, the Curve Fit needs data on the input. We trained <strong>Curve Fit</strong> and <a href="../model/linearregression.md">Linear Regression</a> and evaluated their performance in <a href="/widget-catalog/model/../evaluate/testandscore">Test &amp; Score</a>.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/CurveFit-example.png" data-gallery="true"><img alt="" loading="lazy" width="1402" height="747" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/CurveFit-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Curve Fit\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Fit a function to data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: curve fit learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: fitted coefficients\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Curve Fit\"\n      }), \" widget fits an arbitrary function to the input data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Gradient Boosting</h1>
 <p>Predict using gradient boosting on decision trees.</p>
 <p><strong>Inputs</strong></p>
@@ -213,4 +213,4 @@ <h2>Feature Scoring</h2>
 <p>Gradient Boosting can be used with Rank for feature scoring. See <a href="/widget-catalog/model/../../learners-as-scorers/index">Learners as Scorers</a> for an example.</p>
 <h2>Example</h2>
 <p>For a classification tasks, we use the <em>heart disease</em> data. Here, we compare all available methods in the <a href="/widget-catalog/model/../evaluate/testandscore">Test &amp; Score</a> widget.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/GradientBoosting-example.png" data-gallery="true"><img alt="" loading="lazy" width="1750" height="1620" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/GradientBoosting-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gradient Boosting\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict using gradient boosting on decision trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: gradient boosting learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Gradient_boosting\",\n        children: \"Gradient Boosting\"\n      }), \" is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/GradientBoosting-stamped.png\",\n      alt: \"\",\n      width: \"500\",\n      height: \"813\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Specify the name of the model. 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+<a href="/widget-catalog/model/images/__optimized-images__/GradientBoosting-example.png" data-gallery="true"><img alt="" loading="lazy" width="1750" height="1620" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/GradientBoosting-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Gradient Boosting\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict using gradient boosting on decision trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: gradient boosting learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Gradient_boosting\",\n        children: \"Gradient Boosting\"\n      }), \" is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/GradientBoosting-stamped.png\",\n      alt: \"\",\n      width: \"500\",\n      height: \"813\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Specify the name of the model. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>kNN</h1>
 <p>Predict according to the nearest training instances.</p>
 <p><strong>Inputs</strong></p>
@@ -203,4 +203,4 @@ <h2>Examples</h2>
 <p>The first example is a classification task on <em>iris</em> dataset. We compare the results of <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">k-Nearest neighbors</a> with the default model <a href="/widget-catalog/model/../model/constant">Constant</a>, which always predicts the majority class.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/Constant-classification.png" data-gallery="true"><img alt="" loading="lazy" width="2042" height="1154" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Constant-classification.png"/></a>
 <p>The second example is a regression task. This workflow shows how to use the <em>Learner</em> output. For the purpose of this example, we used the <em>housing</em> dataset. We input the <strong>kNN</strong> prediction model into <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a> and observe the predicted values.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/kNN-regression.png" data-gallery="true"><img alt="" loading="lazy" width="1588" height="1268" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/kNN-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"kNN\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict according to the nearest training instances.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: kNN learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"kNN\"\n      }), \" widget uses the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\",\n        children: \"kNN algorithm\"\n      }), \" that searches for k closest training examples in feature space and uses their average as prediction.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/kNN-stamped.png\",\n      alt: \"\",\n      width: \"654\",\n      height: \"568\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"A name under which it will appear in other widgets. The default name is \\\"kNN\\\".\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [\"Metric can be:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Euclidean_distance\",\n                  children: \"Euclidean\"\n                }), \" (\\\"straight line\\\", distance between two points)\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Taxicab_geometry\",\n                  children: \"Manhattan\"\n                }), \" (sum of absolute differences of all attributes)\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Chebyshev_distance\",\n                  children: \"Maximal\"\n                }), \" (greatest of absolute differences between attributes)\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.a, {\n                  href: \"https://en.wikipedia.org/wiki/Mahalanobis_distance\",\n                  children: \"Mahalanobis\"\n                }), \" (distance between point and distribution).\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"The \", _jsx(_components.em, {\n              children: \"Weights\"\n            }), \" you can use are:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.strong, {\n                  children: \"Uniform\"\n                }), \": all points in each neighborhood are weighted equally.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.strong, {\n                  children: \"Distance\"\n                }), \": closer neighbors of a query point have a greater influence than the neighbors further away.\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"When you change one or more settings, you need to click \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \", which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. 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It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"continuizes categorical variables (with one-hot-encoding)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"normalizes the data by centering to mean and scaling to standard deviation of 1\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The first example is a classification task on \", _jsx(_components.em, {\n        children: \"iris\"\n      }), \" dataset. We compare the results of \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\",\n        children: \"k-Nearest neighbors\"\n      }), \" with the default model \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../model/constant\",\n        children: \"Constant\"\n      }), \", which always predicts the majority class.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/Constant-classification.png\",\n      alt: \"\",\n      width: \"2042\",\n      height: \"1154\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The second example is a regression task. This workflow shows how to use the \", _jsx(_components.em, {\n        children: \"Learner\"\n      }), \" output. For the purpose of this example, we used the \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" dataset. We input the \", _jsx(_components.strong, {\n        children: \"kNN\"\n      }), \" prediction model into \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \" and observe the predicted values.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/kNN-regression.png\",\n      alt: \"\",\n      width: \"1588\",\n      height: \"1268\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: 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+<a href="/widget-catalog/model/images/__optimized-images__/kNN-regression.png" data-gallery="true"><img alt="" loading="lazy" width="1588" height="1268" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/kNN-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"kNN\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict according to the nearest training instances.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: kNN learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"kNN\"\n      }), \" widget uses the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\",\n        children: \"kNN algorithm\"\n      }), \" that searches for k closest training examples in feature space and uses their average as prediction.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/kNN-stamped.png\",\n      alt: \"\",\n      width: \"654\",\n      height: \"568\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"A name under which it will appear in other widgets. 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@@ -199,4 +199,4 @@ <h2>Observing Coefficients</h2>
 <a href="/widget-catalog/model/images/__optimized-images__/Linear-Regression-coefficients.png" data-gallery="true"><img alt="" loading="lazy" width="334" height="500" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Linear-Regression-coefficients.png"/></a>
 <h2>Example</h2>
 <p>Below, is a simple workflow with <em>housing</em> dataset. We trained <strong>Linear Regression</strong> and <a href="../model/randomforest.md">Random Forest</a> and evaluated their performance in <a href="/widget-catalog/model/../evaluate/testandscore">Test &amp; Score</a>.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/LinearRegression-regression.png" data-gallery="true"><img alt="" loading="lazy" width="2322" height="1050" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/LinearRegression-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Linear Regression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: linear regression learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: linear regression coefficients\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Linear Regression\"\n      }), \" widget constructs a learner/predictor that learns a \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Linear_regression\",\n        children: \"linear function\"\n      }), \" from its input data. 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Unchecking the option forces the intercept to zero.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Choose a model to train:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"no regularization\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"a \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Least_squares#Lasso_method\",\n              children: \"Ridge\"\n            }), \" regularization (L2-norm penalty)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"a \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Least_squares#Lasso_method\",\n              children: \"Lasso\"\n            }), \" bound (L1-norm penalty)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"an \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Elastic_net_regularization\",\n              children: \"Elastic net\"\n            }), \" regularization\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Linear Regression uses default preprocessing when no other preprocessors are given. It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"continuizes categorical variables (with one-hot-encoding)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Feature Scoring\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Linear Regression can be used with Rank for feature scoring. See \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../../learners-as-scorers/index\",\n        children: \"Learners as Scorers\"\n      }), \" for an example.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Observing Coefficients\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To observe coefficients of linear regression, first build a model, then pass the model to the \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/datatable\",\n        children: \"Data Table\"\n      }), \". This will automatically connect the \", _jsx(_components.em, {\n        children: \"Coefficients\"\n      }), \" output to the Data Table, where you can sort the table by coefficients and observe which variables positively and negatively correlate with the prediction.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/Linear-Regression-workflow.png\",\n      alt: \"\",\n      width: \"422\",\n      height: \"138\"\n    }), \"\\n\", \"\\n\", \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/Linear-Regression-coefficients.png\",\n      alt: \"\",\n      width: \"334\",\n      height: \"500\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Below, is a simple workflow with \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" dataset. 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@@ -166,4 +166,4 @@
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 <h2>Example</h2>
 <p>When you want to use a custom-set model that you&#x27;ve saved before, open the <strong>Load Model</strong> widget and select the desired file with the <em>Browse</em> icon. This widget loads the existing model into <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a> widget. Datasets used with <strong>Load Model</strong> have to contain compatible attributes!</p>
-<a href="/widget-catalog/model/images/__optimized-images__/LoadModel-example.png" data-gallery="true"><img alt="" loading="lazy" width="1448" height="1050" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/LoadModel-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Load Model\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Load a model from an input file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/LoadModel-stamped.png\",\n      alt: \"\",\n      width: \"846\",\n      height: \"194\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Choose from a list of previously used models.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Browse for saved models.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reload the selected model.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"When you want to use a custom-set model that you've saved before, open the \", _jsx(_components.strong, {\n        children: \"Load Model\"\n      }), \" widget and select the desired file with the \", _jsx(_components.em, {\n        children: \"Browse\"\n      }), \" icon. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Logistic Regression</h1>
 <p>The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.</p>
 <p><strong>Inputs</strong></p>
@@ -186,4 +186,4 @@ <h2>Feature Scoring</h2>
 <h2>Example</h2>
 <p>The widget is used just as any other widget for inducing a classifier. This is an example demonstrating prediction results with logistic regression on the <em>hayes-roth</em> dataset. We first load <em>hayes-roth_learn</em> in the <a href="../data/file.md">File</a> widget and pass the data to <strong>Logistic Regression</strong>. Then we pass the trained model to <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a>.</p>
 <p>Now we want to predict class value on a new dataset. We load <em>hayes-roth_test</em> in the second <strong>File</strong> widget and connect it to <strong>Predictions</strong>. We can now observe class values predicted with <strong>Logistic Regression</strong> directly in <strong>Predictions</strong>.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/LogisticRegression-classification.png" data-gallery="true"><img alt="" loading="lazy" width="2502" height="1418" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/LogisticRegression-classification.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Logistic Regression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: logistic regression learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: logistic regression coefficients\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Logistic Regression\"\n      }), \" learns a \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Logistic_regression\",\n        children: \"Logistic Regression\"\n      }), \" model from the data. 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+<a href="/widget-catalog/model/images/__optimized-images__/LogisticRegression-classification.png" data-gallery="true"><img alt="" loading="lazy" width="2502" height="1418" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/LogisticRegression-classification.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Logistic Regression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: logistic regression learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: logistic regression coefficients\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Logistic Regression\"\n      }), \" learns a \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Logistic_regression\",\n        children: \"Logistic Regression\"\n      }), \" model from the data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Naive Bayes</h1>
 <p>A fast and simple probabilistic classifier based on Bayes&#x27; theorem with the assumption of feature independence.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@ <h2>Examples</h2>
 <strong>Naive Bayes</strong> with another model, the <a href="/widget-catalog/model/../model/randomforest">Random Forest</a>. We connect <em>iris</em> data from <a href="../data/file.md">File</a> to <a href="../evaluate/testandscore.md">Test &amp; Score</a>. We also connect <strong>Naive Bayes</strong> and <a href="/widget-catalog/model/../model/randomforest">Random Forest</a> to <strong>Test &amp; Score</strong> and observe their prediction scores.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/NaiveBayes-classification.png" data-gallery="true"><img alt="" loading="lazy" width="1760" height="1248" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/NaiveBayes-classification.png"/></a>
 <p>The second schema shows the quality of predictions made with <strong>Naive Bayes</strong>. We feed the <a href="../evaluate/testandscore.md">Test &amp; Score</a> widget a Naive Bayes learner and then send the data to the <a href="../evaluate/confusionmatrix.md">Confusion Matrix</a>. We also connect <a href="/widget-catalog/model/../visualize/scatterplot">Scatter Plot</a> with <strong>File</strong>. Then we select the misclassified instances in the <strong>Confusion Matrix</strong> and show feed them to <a href="/widget-catalog/model/../visualize/scatterplot">Scatter Plot</a>. The bold dots in the scatterplot are the misclassified instances from <strong>Naive Bayes</strong>.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/NaiveBayes-visualize.png" data-gallery="true"><img alt="" loading="lazy" width="3008" height="1610" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/NaiveBayes-visualize.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Naive Bayes\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A fast and simple probabilistic classifier based on Bayes' theorem with the assumption of feature independence.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: naive bayes learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Naive Bayes\"\n      }), \" learns a \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Naive_Bayes_classifier\",\n        children: \"Naive Bayesian\"\n      }), \" model from the data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Neural Network</h1>
 <p>A multi-layer perceptron (MLP) algorithm with backpropagation.</p>
 <p><strong>Inputs</strong></p>
@@ -215,4 +215,4 @@ <h2>Examples</h2>
 <p>The first example is a classification task on <em>iris</em> dataset. We compare the results of <strong>Neural Network</strong> with the <a href="/widget-catalog/model/../model/logisticregression">Logistic Regression</a>.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/NN-Example-Test.png" data-gallery="true"><img alt="" loading="lazy" width="2524" height="1484" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/NN-Example-Test.png"/></a>
 <p>The second example is a prediction task, still using the <em>iris</em> data. This workflow shows how to use the <em>Learner</em> output. We input the <strong>Neural Network</strong> prediction model into <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a> and observe the predicted values.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/NN-Example-Predict.png" data-gallery="true"><img alt="" loading="lazy" width="2658" height="1546" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/NN-Example-Predict.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Neural Network\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A multi-layer perceptron (MLP) algorithm with backpropagation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: multi-layer perceptron learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Neural Network\"\n      }), \" widget uses sklearn's \", _jsx(_components.a, {\n        href: \"http://scikit-learn.org/stable/modules/neural_networks_supervised.html\",\n        children: \"Multi-layer Perceptron algorithm\"\n      }), \" that can learn non-linear models as well as linear.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/NeuralNetwork-stamped.png\",\n      alt: \"\",\n      width: \"662\",\n      height: \"724\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"A name under which it will appear in other widgets. 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+<a href="/widget-catalog/model/images/__optimized-images__/NN-Example-Predict.png" data-gallery="true"><img alt="" loading="lazy" width="2658" height="1546" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/NN-Example-Predict.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Neural Network\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A multi-layer perceptron (MLP) algorithm with backpropagation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: multi-layer perceptron learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Neural Network\"\n      }), \" widget uses sklearn's \", _jsx(_components.a, {\n        href: \"http://scikit-learn.org/stable/modules/neural_networks_supervised.html\",\n        children: \"Multi-layer Perceptron algorithm\"\n      }), \" that can learn non-linear models as well as linear.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/NeuralNetwork-stamped.png\",\n      alt: \"\",\n      width: \"662\",\n      height: \"724\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"A name under which it will appear in other widgets. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Random Forest</h1>
 <p>Predict using an ensemble of decision trees.</p>
 <p><strong>Inputs</strong></p>
@@ -203,4 +203,4 @@ <h2>Examples</h2>
 <p>For regressions tasks, we will use <em>housing</em> data. Here, we will compare different models, namely <strong>Random Forest</strong>, <a href="../model/linearregression.md">Linear Regression</a> and <a href="../model/constant.md">Constant</a>, in the <a href="/widget-catalog/model/../evaluate/testandscore">Test &amp; Score</a> widget.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/RandomForest-regression.png" data-gallery="true"><img alt="" loading="lazy" width="2182" height="1412" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/RandomForest-regression.png"/></a>
 <h2>References</h2>
-<p>Breiman, L. (2001). Random Forests. In Machine Learning, 45(1), 5-32. Available <a href="https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Random Forest\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict using an ensemble of decision trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: random forest learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Random_forest\",\n        children: \"Random forest\"\n      }), \" is an ensemble learning method used for classification, regression and other tasks. It was first proposed by Tin Kam Ho and further developed by Leo Breiman (Breiman, 2001) and Adele Cutler.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Random Forest\"\n      }), \" builds a set of decision trees. Each tree is developed from a bootstrap sample from the training data. When developing individual trees, an arbitrary subset of attributes is drawn (hence the term \\\"Random\\\"), from which the best attribute for the split is selected. The final model is based on the majority vote from individually developed trees in the forest.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Random Forest\"\n      }), \" works for both classification and regression tasks.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/RandomForest.png\",\n      alt: \"\",\n      width: \"508\",\n      height: \"539\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Specify the name of the model. 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It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"continuizes categorical variables (with one-hot-encoding)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Feature Scoring\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Random Forest can be used with Rank for feature scoring. See \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../../learners-as-scorers/index\",\n        children: \"Learners as Scorers\"\n      }), \" for an example.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For classification tasks, we use \", _jsx(_components.em, {\n        children: \"iris\"\n      }), \" dataset. Connect it to \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \". Then, connect \", _jsx(_components.a, {\n        href: \"../data/file.md\",\n        children: \"File\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Random Forest\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/tree.md\",\n        children: \"Tree\"\n      }), \" and connect them further to \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \". Finally, observe the predictions for the two models.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/RandomForest-classification.png\",\n      alt: \"\",\n      width: \"2524\",\n      height: \"1410\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For regressions tasks, we will use \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" data. Here, we will compare different models, namely \", _jsx(_components.strong, {\n        children: \"Random Forest\"\n      }), \", \", _jsx(_components.a, {\n        href: \"../model/linearregression.md\",\n        children: \"Linear Regression\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/constant.md\",\n        children: \"Constant\"\n      }), \", in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/RandomForest-regression.png\",\n      alt: \"\",\n      width: \"2182\",\n      height: \"1412\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Breiman, L. (2001). Random Forests. In Machine Learning, 45(1), 5-32. Available \", _jsx(_components.a, {\n        href: \"https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf\",\n        children: \"here\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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Plot","category":"Evaluate","keywords":["calibration","plot"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Calibration Plot.png","url":"calibrationplot"}]],["Unsupervised",[{"title":"Distance File","category":"Unsupervised","keywords":["distance file","load","read","open"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance File.png","url":"distancefile"},{"title":"Distance Matrix","category":"Unsupervised","keywords":["distance","matrix"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Matrix.png","url":"distancematrix"},{"title":"t-SNE","category":"Unsupervised","keywords":["t-sne","tsne"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-t-SNE.png","url":"tsne"},{"title":"Correlations","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correlations.png","url":"correlations"},{"title":"Distance 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+<p>Breiman, L. (2001). Random Forests. In Machine Learning, 45(1), 5-32. Available <a href="https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Random Forest\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict using an ensemble of decision trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: random forest learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Random_forest\",\n        children: \"Random forest\"\n      }), \" is an ensemble learning method used for classification, regression and other tasks. 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It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"continuizes categorical variables (with one-hot-encoding)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Feature Scoring\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Random Forest can be used with Rank for feature scoring. See \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../../learners-as-scorers/index\",\n        children: \"Learners as Scorers\"\n      }), \" for an example.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For classification tasks, we use \", _jsx(_components.em, {\n        children: \"iris\"\n      }), \" dataset. Connect it to \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \". Then, connect \", _jsx(_components.a, {\n        href: \"../data/file.md\",\n        children: \"File\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Random Forest\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/tree.md\",\n        children: \"Tree\"\n      }), \" and connect them further to \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \". Finally, observe the predictions for the two models.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/RandomForest-classification.png\",\n      alt: \"\",\n      width: \"2524\",\n      height: \"1410\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For regressions tasks, we will use \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" data. Here, we will compare different models, namely \", _jsx(_components.strong, {\n        children: \"Random Forest\"\n      }), \", \", _jsx(_components.a, {\n        href: \"../model/linearregression.md\",\n        children: \"Linear Regression\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/constant.md\",\n        children: \"Constant\"\n      }), \", in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/testandscore\",\n        children: \"Test \u0026 Score\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/RandomForest-regression.png\",\n      alt: \"\",\n      width: \"2182\",\n      height: \"1412\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Breiman, L. (2001). Random Forests. In Machine Learning, 45(1), 5-32. Available \", _jsx(_components.a, {\n        href: \"https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf\",\n        children: \"here\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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Plot","category":"Evaluate","keywords":["calibration","plot"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Calibration Plot.png","url":"calibrationplot"}]],["Unsupervised",[{"title":"Distance File","category":"Unsupervised","keywords":["distance file","load","read","open"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance File.png","url":"distancefile"},{"title":"Distance Matrix","category":"Unsupervised","keywords":["distance","matrix"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Matrix.png","url":"distancematrix"},{"title":"t-SNE","category":"Unsupervised","keywords":["t-sne","tsne"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-t-SNE.png","url":"tsne"},{"title":"Correlations","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correlations.png","url":"correlations"},{"title":"Distance 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 <p>Save a trained model to an output file.</p>
 <p>If the file is saved to the same directory as the workflow or in the subtree of that directory, the widget remembers the relative path. Otherwise it will store an absolute path, but disable auto save for security reasons.</p>
@@ -168,4 +168,4 @@
 </ol>
 <h2>Example</h2>
 <p>When you want to save a custom-set model, feed the data to the model (e.g. <a href="../model/logisticregression.md">Logistic Regression</a>) and connect it to <strong>Save Model</strong>. Name the model; load it later into workflows with <a href="/widget-catalog/model/../model/loadmodel">Load Model</a>. Datasets used with <strong>Load Model</strong> have to contain compatible attributes.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/SaveModel-example.png" data-gallery="true"><img alt="" loading="lazy" width="1488" height="1050" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/SaveModel-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Save Model\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Save a trained model to an output file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If the file is saved to the same directory as the workflow or in the subtree of that directory, the widget remembers the relative path. 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 <p>Stack multiple models.</p>
 <p><strong>Inputs</strong></p>
@@ -176,4 +176,4 @@
 <h2>Example</h2>
 <p>We will use <a href="../data/paintdata.md">Paint Data</a> to demonstrate how the widget is used. We painted a complex dataset with 4 class labels and sent it to <a href="../evaluate/testandscore.md">Test &amp; Score</a>. We also provided three <a href="/widget-catalog/model/../model/knn">kNN</a> learners, each with a different parameters (number of neighbors is 5, 10 or 15). Evaluation results are good, but can we do better?</p>
 <p>Let&#x27;s use <strong>Stacking</strong>. <strong>Stacking</strong> requires several learners on the input and an aggregation method. In our case, this is <a href="/widget-catalog/model/../model/logisticregression">Logistic Regression</a>. A constructed meta learner is then sent to <strong>Test &amp; Score</strong>. Results have improved, even if only marginally. <strong>Stacking</strong> normally works well on complex data sets.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/Stacking-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2680" height="1748" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Stacking-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Stacking\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Stack multiple models.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learners: learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Aggregate: model aggregation method\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: aggregated (stacked) learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Stacking\"\n      }), \" is an ensemble method that computes a meta model from several base models. 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@@ -246,4 +246,4 @@ <h2>Examples</h2>
 <p>For the classification task, we will use <em>iris</em> dataset and test two models on it. We connected <a href="../model/stochasticgradient.md">Stochastic Gradient Descent</a> and <a href="../model/tree.md">Tree</a> to <a href="../evaluate/testandscore.md">Test &amp; Score</a>. We also connected <a href="/widget-catalog/model/../data/file">File</a> to <strong>Test &amp; Score</strong> and observed model performance in the widget.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/StochasticGradientDescent-classification.png" data-gallery="true"><img alt="" loading="lazy" width="2008" height="1498" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/StochasticGradientDescent-classification.png"/></a>
 <p>For the regression task, we will compare three different models to see which predict what kind of results. For the purpose of this example, the <em>housing</em> dataset is used. We connect the <a href="/widget-catalog/model/../data/file">File</a> widget to <strong>Stochastic Gradient Descent</strong>, <a href="../model/linearregression.md">Linear Regression</a> and <a href="../model/knn.md">kNN</a> widget and all four to the <a href="/widget-catalog/model/../evaluate/predictions">Predictions</a> widget.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/StochasticGradientDescent-regression.png" data-gallery="true"><img alt="" loading="lazy" width="1996" height="1532" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/StochasticGradientDescent-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Stochastic Gradient Descent\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Minimize an objective function using a stochastic approximation of gradient descent.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: stochastic gradient descent learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Stochastic Gradient Descent\"\n      }), \" widget uses \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Stochastic_gradient_descent\",\n        children: \"stochastic gradient descent\"\n      }), \" that minimizes a chosen loss function with a linear function. 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rate is inversely related to the number of iterations\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Initial learning rate.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Inverse scaling exponent: learning rate decay.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Number of iterations: the number of passes through the training data.\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"If \", _jsx(_components.em, {\n              children: \"Shuffle data after each iteration\"\n            }), \" is on, the order of data instances is mixed after each pass.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"If \", _jsx(_components.em, {\n              children: \"Fixed seed for random shuffling\"\n            }), \" is on, the algorithm will use a fixed random seed and enable replicating the results.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Produce a report.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Press \", _jsx(_components.em, {\n            children: \"Apply\"\n          }), \" to commit changes. 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It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"continuizes categorical variables (with one-hot-encoding)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"normalizes the data by centering to mean and scaling to standard deviation of 1\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Feature Scoring\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Stochastic Gradient Descent can be used with Rank for feature scoring. 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We also connected \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/file\",\n        children: \"File\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" and observed model performance in the widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/StochasticGradientDescent-classification.png\",\n      alt: \"\",\n      width: \"2008\",\n      height: \"1498\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the regression task, we will compare three different models to see which predict what kind of results. For the purpose of this example, the \", _jsx(_components.em, {\n        children: \"housing\"\n      }), \" dataset is used. 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It executes them in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"removes instances with unknown target values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"continuizes categorical variables (with one-hot-encoding)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"removes empty columns\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputes missing values with mean values\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"normalizes the data by centering to mean and scaling to standard deviation of 1\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To remove default preprocessing, connect an empty \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget to the learner.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Feature Scoring\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Stochastic Gradient Descent can be used with Rank for feature scoring. See \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../../learners-as-scorers/index\",\n        children: \"Learners as Scorers\"\n      }), \" for an example.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the classification task, we will use \", _jsx(_components.em, {\n        children: \"iris\"\n      }), \" dataset and test two models on it. We connected \", _jsx(_components.a, {\n        href: \"../model/stochasticgradient.md\",\n        children: \"Stochastic Gradient Descent\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/tree.md\",\n        children: \"Tree\"\n      }), \" to \", _jsx(_components.a, {\n        href: \"../evaluate/testandscore.md\",\n        children: \"Test \u0026 Score\"\n      }), \". 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We connect the \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../data/file\",\n        children: \"File\"\n      }), \" widget to \", _jsx(_components.strong, {\n        children: \"Stochastic Gradient Descent\"\n      }), \", \", _jsx(_components.a, {\n        href: \"../model/linearregression.md\",\n        children: \"Linear Regression\"\n      }), \" and \", _jsx(_components.a, {\n        href: \"../model/knn.md\",\n        children: \"kNN\"\n      }), \" widget and all four to the \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../evaluate/predictions\",\n        children: \"Predictions\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/StochasticGradientDescent-regression.png\",\n      alt: \"\",\n      width: \"1996\",\n      height: \"1532\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/constant/">Constant</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/cn2ruleinduction/">CN2 Rule Induction</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/calibratedlearner/">Calibrated Learner</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/knn/">kNN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/tree/">Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/randomforest/">Random Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/gradientboosting/">Gradient Boosting</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/model/svm/">SVM</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/linearregression/">Linear Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/logisticregression/">Logistic Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/naivebayes/">Naive Bayes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/adaboost/">AdaBoost</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/curvefit/">Curve Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/neuralnetwork/">Neural Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stochasticgradient/">Stochastic Gradient Descent</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/stacking/">Stacking</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/savemodel/">Save Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/model/loadmodel/">Load Model</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>SVM</h1>
 <p>Support Vector Machines map inputs to higher-dimensional feature spaces.</p>
 <p><strong>Inputs</strong></p>
@@ -214,4 +214,4 @@ <h2>Examples</h2>
 <p>The second example shows how to use <strong>SVM</strong> in combination with <a href="/widget-catalog/model/../visualize/scatterplot">Scatter Plot</a>. The following workflow trains a SVM model on <em>iris</em> data and outputs support vectors, which are those data instances that were used as support vectors in the learning phase. We can observe which are these data instances in a scatter plot visualization. Note that for the workflow to work correctly, you must set the links between widgets as demonstrated in the screenshot below.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/SVM-support-vectors.png" data-gallery="true"><img alt="" loading="lazy" width="2634" height="1814" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/SVM-support-vectors.png"/></a>
 <h2>References</h2>
-<p><a href="http://www.statsoft.com/Textbook/Support-Vector-Machines">Introduction to SVM on StatSoft</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"SVM\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Support Vector Machines map inputs to higher-dimensional feature spaces.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: linear regression learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Support Vectors: instances used as support vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Support_vector_machine\",\n        children: \"Support vector machine\"\n      }), \" (SVM) is a machine learning technique that separates the attribute space with a hyperplane, thus maximizing the margin between the instances of different classes or class values. 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+<p><a href="http://www.statsoft.com/Textbook/Support-Vector-Machines">Introduction to SVM on StatSoft</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"SVM\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Support Vector Machines map inputs to higher-dimensional feature spaces.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: linear regression learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Support Vectors: instances used as support vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Support_vector_machine\",\n        children: \"Support vector machine\"\n      }), \" (SVM) is a machine learning technique that separates the attribute space with a hyperplane, thus maximizing the margin between the instances of different classes or class values. 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 <p>A tree algorithm with forward pruning.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@ <h2>Examples</h2>
 <p>The second schema trains a model and evaluates its performance against <a href="/widget-catalog/model/../model/logisticregression">Logistic Regression</a>.</p>
 <a href="/widget-catalog/model/images/__optimized-images__/Tree-classification-model.png" data-gallery="true"><img alt="" loading="lazy" width="2222" height="1438" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Tree-classification-model.png"/></a>
 <p>We used the <em>iris</em> dataset in both examples. However, <strong>Tree</strong> works for regression tasks as well. Use <em>housing</em> dataset and pass it to <strong>Tree</strong>. The selected tree node from <a href="/widget-catalog/model/../visualize/treeviewer">Tree Viewer</a> is presented in the <a href="/widget-catalog/model/../visualize/scatterplot">Scatter Plot</a> and we can see that the selected examples exhibit the same features.</p>
-<a href="/widget-catalog/model/images/__optimized-images__/Tree-regression-subset.png" data-gallery="true"><img alt="" loading="lazy" width="2950" height="1480" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/model/images/__optimized-images__/Tree-regression-subset.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tree\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A tree algorithm with forward pruning.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: decision tree learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model: trained model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Tree\"\n      }), \" is a simple algorithm that splits the data into nodes by class purity (information gain for categorical and MSE for numeric target variable). It is a precursor to \", _jsx(_components.a, {\n        href: \"/widget-catalog/model/../model/randomforest\",\n        children: \"Random Forest\"\n      }), \". Tree in Orange is designed in-house and can handle both categorical and numeric datasets.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"It can also be used for both classification and regression tasks.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/model/images/__optimized-images__/Tree-stamped.png\",\n      alt: \"\",\n      width: \"698\",\n      height: \"804\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"The learner can be given a name under which it will appear in other widgets. 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After changing the settings, you need to click \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \", which will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. Alternatively, tick the box on the left and changes will be communicated automatically.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Tree does not use any preprocessing.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"There are two typical uses for this widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkfile/">Network File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkexplorer/">Network Explorer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkgenerator/">Network Generator</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/networks/networkanalysis/">Network Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkclustering/">Network Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkofgroups/">Network Of Groups</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkfromdistances/">Network From Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/singlemode/">Single Mode</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Network Analysis</h1>
 <p>Statistical analysis of network data.</p>
 <p><strong>Inputs</strong></p>
@@ -193,4 +193,4 @@
 <h2>Example</h2>
 <p>This simple example shows how <strong>Network Analysis</strong> can enrich the workflow. We have used <em>lastfm.net</em> as our input network from <a href="/widget-catalog/networks/networkfile">Network File</a> and sent it to <strong>Network Analysis</strong>. We&#x27;ve decided to compute <em>degree</em>, <em>degree centrality</em> and <em>closeness centrality</em> at node level.</p>
 <p>We can visualize the network in <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>. In the widget we color by <em>best tag</em>, as is the default for this data set. But now we can also set the size of the nodes to correspond to the computed <em>Degree centrality</em>. This is a great way to visualize the properties of the network.</p>
-<a href="/widget-catalog/networks/images/__optimized-images__/network-analysis-example.png" data-gallery="true"><img alt="" loading="lazy" width="2624" height="1532" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/network-analysis-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Statistical analysis of network data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Items: Properties of a network file.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph with appended information.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Items: New properties of a network file.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Analysis\"\n      }), \" widget computes node-level and graph-level summary statistics for the network. 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+<a href="/widget-catalog/networks/images/__optimized-images__/network-analysis-example.png" data-gallery="true"><img alt="" loading="lazy" width="2624" height="1532" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/network-analysis-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Statistical analysis of network data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Items: Properties of a network file.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph with appended information.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Items: New properties of a network file.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Analysis\"\n      }), \" widget computes node-level and graph-level summary statistics for the network. 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 <p>Detect clusters in a network.</p>
 <p><strong>Inputs</strong></p>
@@ -177,4 +177,4 @@
 <h2>Example</h2>
 <p><strong>Network Clustering</strong> can help you uncover cliques and highly connected groups in a network. First, we will use <a href="networkfile.md">Network File</a> to load <em>lastfm.net</em> data set. Then we will pass the network through <strong>Network Clustering</strong>. The widget found 79 clusters in a network. To visualize the results, use <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a> and set <em>Color</em> attribute to <em>Cluster</em>. This will color network nodes with the corresponding cluster color - this is a great way to visualize highly connected groups in dense networks.</p>
 <p>Keep in mind that <strong>Network Explorer</strong> will color the 10 largest clusters and color the rest as &#x27;Other&#x27;.</p>
-<a href="/widget-catalog/networks/images/__optimized-images__/network-clustering-example.png" data-gallery="true"><img alt="" loading="lazy" width="2638" height="1518" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/network-clustering-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Clustering\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Detect clusters in a network.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph with clustering information appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Clustering\"\n      }), \" widget finds clusters in a network. 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Clustering works with two algorithms, one from Raghavan et al. (2007), which uses label propagation to find appropriate clusters, and one from Leung et al. 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 <p>Visually explore the network and its properties.</p>
 <p><strong>Inputs</strong></p>
@@ -204,4 +204,4 @@
 <h2>Example</h2>
 <p>In this example we will use the <em>lastfm</em> data set that can be loaded in the <strong>Network File</strong> widget under <em>Browse documentation networks</em>. The nodes of the network are musicians, which are characterized by the genre they play, number of albums produced and so on. The edges are the number of listeners on LastFm.</p>
 <p>The entire data set is visualized in <strong>Network Explorer</strong>. In the widget, we removed the coloring and set the size of the nodes to correspond to the <em>album count</em>. Then we selected some nodes from the network. We can observe the selection in <strong>Network Explorer (1)</strong>.</p>
-<a href="/widget-catalog/networks/images/__optimized-images__/Network-Explorer-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2722" height="1870" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/Network-Explorer-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Explorer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visually explore the network and its properties.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node Subset: A subset of vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node Data: Information on vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node Distances: Data on distances between nodes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected sub-network: A network of selected nodes.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Distance Matrix: Distance matrix.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Items: Information on selected vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Highlighted Items: Information on highlighted vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Remaining Items: Information on remaining items (not selected or highlighted).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Explorer\"\n      }), \" is the primary widget for visualizing network graphs. It displays a graph with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Force-directed_graph_drawing\",\n        children: \"Fruchterman-Reingold layout optimization\"\n      }), \" and enables setting the color, size and label of nodes. One can also highlight nodes of specific properties and output them.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The visualization in \", _jsx(_components.strong, {\n        children: \"Network Explorer\"\n      }), \" works just like the one for \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \". To select a subset of nodes, draw a rectangle around the subset. Shift will add a new group. Ctrl-Shift (Cmd-Shift) will add to the existing group. Alt (Option) will remove from the group. 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+<a href="/widget-catalog/networks/images/__optimized-images__/Network-Explorer-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2722" height="1870" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/Network-Explorer-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Explorer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visually explore the network and its properties.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node Subset: A subset of vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node Data: Information on vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node Distances: Data on distances between nodes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected sub-network: A network of selected nodes.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Distance Matrix: Distance matrix.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Items: Information on selected vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Highlighted Items: Information on highlighted vertices.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Remaining Items: Information on remaining items (not selected or highlighted).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Explorer\"\n      }), \" is the primary widget for visualizing network graphs. 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@@ -171,4 +171,4 @@
 </ol>
 <h2>Example</h2>
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 <p>Constructs a network from distances between instances.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Example</h2>
 <p><strong>Network from Distances</strong> creates networks from distance matrices. It can transform data sets from a data table via distance matrix into a network graph. This widget is great for visualizing instance similarity as a graph of connected instances.</p>
 <a href="/widget-catalog/networks/images/__optimized-images__/network-from-distances-example.png" data-gallery="true"><img alt="" loading="lazy" width="2870" height="1568" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/network-from-distances-example.png"/></a>
 <p>We took <em>iris.tab</em> to visualize instance similarity in a graph. We sent the output of <strong>File</strong> widget to <strong>Distances</strong>, where we computed Euclidean distances between rows (instances). Then we sent the output of <strong>Distances</strong> to <strong>Network from Distances</strong>, where we set the distance threshold (how similar the instances have to be to draw an edge between them) to 0.222. We kept all nodes and set edge weights to <em>proportional to distance</em>.</p>
-<p>Then we observed the constructed network in a <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>. We colored the nodes by <em>iris</em> attribute.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network From Distances\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Constructs a network from distances between instances.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: A distance matrix.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Attribute-valued data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Distances: A distance matrix.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network from Distances\"\n      }), \" constructs a network graph from a given distance matrix. 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In other words, all instances with a distance lower than the selected threshold, will be connected.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/networks/images/__optimized-images__/network-from-distances-stamped.png\",\n      alt: \"\",\n      width: \"834\",\n      height: \"470\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Edges:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Distance threshold: a closeness threshold for the formation of edges.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Percentile: the percentile of data instances to be connected.\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Include also closest neighbors\"\n            }), \": include a number of closest neighbors to the selected instances.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Node selection:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Keep all nodes: entire network is on the output.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Components with at least X nodes: filters out nodes with less than the set number of nodes.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Largest connected component: keep only the largest cluster.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Edge weights:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Proportional to distance: weights are set to reflect the distance (closeness).\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Inverted distance: weights are set to reflect the inverted distance (difference).\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Information on the constructed network:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Data items on input: number of instances on the input.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Network nodes: number of nodes in the network (and the percentage of the original data).\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Network edges: number of constructed edges/connections (and the average number of connections per node).\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network from Distances\"\n      }), \" creates networks from distance matrices. 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+<p>Then we observed the constructed network in a <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>. We colored the nodes by <em>iris</em> attribute.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network From Distances\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Constructs a network from distances between instances.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: A distance matrix.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of Network Graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Attribute-valued data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Distances: A distance matrix.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network from Distances\"\n      }), \" constructs a network graph from a given distance matrix. 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In other words, all instances with a distance lower than the selected threshold, will be connected.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/networks/images/__optimized-images__/network-from-distances-stamped.png\",\n      alt: \"\",\n      width: \"834\",\n      height: \"470\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Edges:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Distance threshold: a closeness threshold for the formation of edges.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Percentile: the percentile of data instances to be connected.\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Include also closest neighbors\"\n            }), \": include a number of closest neighbors to the selected instances.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Node selection:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Keep all nodes: entire network is on the output.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Components with at least X nodes: filters out nodes with less than the set number of nodes.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Largest connected component: keep only the largest cluster.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Edge weights:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Proportional to distance: weights are set to reflect the distance (closeness).\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Inverted distance: weights are set to reflect the inverted distance (difference).\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Information on the constructed network:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Data items on input: number of instances on the input.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Network nodes: number of nodes in the network (and the percentage of the original data).\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Network edges: number of constructed edges/connections (and the average number of connections per node).\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network from Distances\"\n      }), \" creates networks from distance matrices. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkfile/">Network File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkexplorer/">Network Explorer</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/networks/networkgenerator/">Network Generator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkanalysis/">Network Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkclustering/">Network Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkofgroups/">Network Of Groups</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkfromdistances/">Network From Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/singlemode/">Single Mode</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Network Generator</h1>
 <p>Construct example graphs.</p>
 <p><strong>Outputs</strong></p>
@@ -179,4 +179,4 @@
 <h2>Example</h2>
 <p><strong>Network Generator</strong> is a nice tool to explore typical graph structures.</p>
 <a href="/widget-catalog/networks/images/__optimized-images__/network-generator-example.png" data-gallery="true"><img alt="" loading="lazy" width="2464" height="1668" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/network-generator-example.png"/></a>
-<p>Here, we generated a <em>Grid</em> graph of height 4 and width 5 and sent it to <a href="networkanalysis.md">Network Analysis</a>. We computed node degrees and sent the data to <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>. Finally, we observed the generated graph in the visualization and set the size and color of the nodes to <em>Degree</em>. This is a nice tool to observe and explain the properties of networks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Generator\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Construct example graphs.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Generated Network: An instance of Network Graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Generator\"\n      }), \" constructs exemplary networks. 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+<p>Here, we generated a <em>Grid</em> graph of height 4 and width 5 and sent it to <a href="networkanalysis.md">Network Analysis</a>. We computed node degrees and sent the data to <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>. Finally, we observed the generated graph in the visualization and set the size and color of the nodes to <em>Degree</em>. This is a nice tool to observe and explain the properties of networks.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Generator\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Construct example graphs.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Generated Network: An instance of Network Graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network Generator\"\n      }), \" constructs exemplary networks. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkfile/">Network File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkexplorer/">Network Explorer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkgenerator/">Network Generator</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkanalysis/">Network Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkclustering/">Network Clustering</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/networks/networkofgroups/">Network Of Groups</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/networkfromdistances/">Network From Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/networks/singlemode/">Single Mode</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Network Of Groups</h1>
 <p>Group instances by feature and connect related groups.</p>
 <p><strong>Inputs</strong></p>
@@ -182,4 +182,4 @@ <h2>Example</h2>
 <p>In this example we are using <em>airtraffic</em> data set, that we loaded in the <a href="/widget-catalog/networks/networkfile">Network File</a> widget. We see the entire data set in <strong>Network Explorer (1)</strong>.</p>
 <p>Then we use <strong>Network of Groups</strong> to group the network by the <em>FAA Classifications</em> attribute. All nodes with the same value of this attribute will be represented as a single node in the output. There is an edge between the two nodes, if they share connections in the original network.</p>
 <p>The grouped network is shown in <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>.</p>
-<a href="/widget-catalog/networks/images/__optimized-images__/Network-of-Groups-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2588" height="1844" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/Network-of-Groups-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Of Groups\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Group instances by feature and connect related groups.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of network graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Properties of a network graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Outputs\"\n      }), \":\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: A grouped network graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Properties of the group network graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network of Groups\"\n      }), \" is the network version of the group-by operation. Nodes with the same values of the attribute, selected in the dropdown, will be represented as a single node.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/networks/images/__optimized-images__/Network-of-Groups-stamped.png\",\n      alt: \"\",\n      width: \"229\",\n      height: \"314\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the input and output network.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the attribute to group by.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Compute weights:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"No weights\"\n            }), \": all weights are set to 1.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Number of connections\"\n            }), \": weight edges by the number of connections between the groups.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Sum of connection weights\"\n            }), \":  weight edges by the sum of weights of connections between the groups.\\n\", _jsx(_components.em, {\n              children: \"Normalize by geometric mean\"\n            }), \" divides weights by the geometric mean of the number of connections between the two groups.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In this example we are using \", _jsx(_components.em, {\n        children: \"airtraffic\"\n      }), \" data set, that we loaded in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/networks/networkfile\",\n        children: \"Network File\"\n      }), \" widget. 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+<a href="/widget-catalog/networks/images/__optimized-images__/Network-of-Groups-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2588" height="1844" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/Network-of-Groups-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Network Of Groups\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Group instances by feature and connect related groups.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of network graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Properties of a network graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Outputs\"\n      }), \":\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: A grouped network graph.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: Properties of the group network graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Network of Groups\"\n      }), \" is the network version of the group-by operation. 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 <p>Convert multimodal graphs to single modal.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@ <h2>Example</h2>
 <p>We see the original file in <a href="/widget-catalog/networks/networkexplorer">Network Explorer</a>. The blue nodes are events and the red ones are persons. Events are attended by persons. A node connects a person with the event, if the person attended the event. Now we can observe which people attended the same event or, conversely, which events were attended by the same people.</p>
 <p>In <strong>Single Mode</strong> we set the feature describing the role of the nodes. It so happens that our attribute is named <em>role</em>. We connect persons (nodes) by the events they attended (edges). Edge weight will be the number of connections in common. In translation, edge weights will be the number of events both people attended.</p>
 <p><strong>Network Explorer (1)</strong> shows the final network.</p>
-<a href="/widget-catalog/networks/images/__optimized-images__/Single-Mode-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2598" height="1836" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/networks/images/__optimized-images__/Single-Mode-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Single Mode\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Convert multimodal graphs to single modal.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of a bipartite network graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: An instance of single network graph.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Single Mode\"\n      }), \" works with bipartite (or multipartite) networks, where different parts are distinguished by values of the chosen discrete variable. 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 <p>Alignment of multiple datasets with a diagram of correlation visualization.</p>
 <p><strong>Inputs</strong></p>
@@ -170,4 +170,4 @@
 <li>Number of shared genes and the scoring method for alignment. Scoring can be done with <a href="https://en.wikipedia.org/wiki/Pearson_correlation_coefficient">Pearson</a>, <a href="https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient">Spearman</a> and <a href="https://en.wikipedia.org/wiki/Biweight_midcorrelation">Biweights midcorrelation</a>.</li>
 <li>Tick the box to use the percent of quantile normalization and dynamic time warping.</li>
 <li>If <em>Apply automatically</em> is ticked, the results will be communicated automatically. Alternatively, press <em>Apply</em>.</li>
-</ol></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Align Datasets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Alignment of multiple datasets with a diagram of correlation visualization.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: single cell dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Transformed Data: aligned data\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Genes per n. 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 <p><strong>Outputs</strong></p>
 <ul>
 <li>Data: Single cell dataset.</li>
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 <p>Perform cluster analysis.</p>
 <p><strong>Inputs</strong></p>
@@ -163,4 +163,4 @@
 <li>Selected Data</li>
 <li>Data: Single cell dataset.</li>
 <li>Contingency Table</li>
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 <p>Filter cells/genes.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@
 <h2>Example</h2>
 <p>The <strong>Filter</strong> widget is used for filtering uninteresting cells, genes or data. By uninteresting we mean too frequent, too unfrequent or where data is zero (no expression). This allows us to have leaner data sets, which speeds up computation and enables easier analysis of results.</p>
 <p>We have used <a href="/widget-catalog/single-cell/singlecelldatasets">ingle Cell Datasets</a> to load <em>Cell cycle in mESC (Fluidigm)</em> data set. Then we used <strong>Filter</strong> widget to narrow down the selection of genes from 38,293 to 11,932. The width of our data table (number of columns) has descreased significantly. We have set the selection here manually (20 for lower and 170 for upper threshold), but you can also set the selection in the visualization by dragging the green field up or down.</p>
-<a href="/widget-catalog/single-cell/images/__optimized-images__/Filter-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2488" height="1798" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/single-cell/images/__optimized-images__/Filter-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Filter\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Filter cells/genes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: filtered dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Filter\"\n      }), \" widget filters cells or genes by the number of positive measurements. 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 <p>Add a cell score based on the given set of genes.</p>
 <p><strong>Inputs</strong></p>
@@ -174,4 +174,4 @@ <h2>Example</h2>
 <p>But can we find subpopulations in these cells? Let us load <em>Bone marrow mononuclear cells with AML (markers)</em> with <a href="/widget-catalog/single-cell/single_cell_datasets">Single Cell Datasets</a>. Now, pass the marker genes to <strong>Data Table</strong> and select, for example, natural killer cells from the list (NKG7).</p>
 <p>Pass the markers and k-Means results to <strong>Score Cells</strong> widget and select <em>geneName</em> to match markers with genes. Finally, add <a href="https://orange-visual-programming.readthedocs.io/widgets/unsupervised/tsne.html">t-SNE</a> to visualize the results.</p>
 <p>In <a href="https://orange-visual-programming.readthedocs.io/widgets/unsupervised/tsne.html">t-SNE</a>, use <em>Scores</em> attribute to color the points and set their size. We see that killer cells are nicely clustered together and that t-SNE indeed found subpopulations.</p>
-<a href="/widget-catalog/single-cell/images/__optimized-images__/tSNE-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3062" height="1652" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/single-cell/images/__optimized-images__/tSNE-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Score Cells\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Add a cell score based on the given set of genes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Genes: dataset with gene names\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with a meta column reporting on cell scores\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Score Cells\"\n      }), \" scores the cells (rows) in the input data based on expression of input marker genes. 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 <p>Gene scoring based on statistics of their expression profiles or information content about cell types.</p>
 <p><strong>Inputs</strong></p>
@@ -186,4 +186,4 @@ <h2>Scoring methods</h2>
 see also <a href="https://en.wikipedia.org/wiki/Negative_binomial_distribution">negative binomial distribution</a></li>
 <li>Coefficient of variation: <a href="https://en.wikipedia.org/wiki/Coefficient_of_variation">relative standard deviation</a>
 of a gene expression. Measures relative variability of gene expression, and is the ratio of the standard deviation to the mean.</li>
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+</ol></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Score Genes\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Gene scoring based on statistics of their expression profiles or information content about cell types.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scorer: (multiple) prediction model(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reduced Data: expression data containing selected genes\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: gene scores\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Score Genes\"\n      }), \" widget considers gene expression data with genes in columns and single cell\\nexpression profiles in rows. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/load_data/">Load Data</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/single-cell/single_cell_datasets/">Single Cell Datasets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/filter/">Filter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/single_cell_preprocess/">Single Cell Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/batch_effect_removal/">Batch Effect Removal</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/align_datasets/">Align Datasets</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/score_genes/">Score Genes</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/score_cells/">Score Cells</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/single-cell/dot_matrix/">Dot Matrix</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Single Cell Datasets</h1>
 <p>Load a single cell data from an online repository.</p>
 <p><strong>Outputs</strong></p>
@@ -166,4 +166,4 @@
 <li>List of available data sets with information on the number of cells (instances) and genes (variables).</li>
 <li>Textual description of the selected data set and its source.</li>
 <li>If <em>Send Data Automatically</em> is ticked, selected data set automatically loaded and pushed to the output of the widget. Notice that some data sets are big and downloading them may take time. Alternatively, uncheck the <em>Send Data Automatically</em>, browse through the data set list and press <em>Send Data</em> upon finding a suitable one for analysis.</li>
-</ol></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Single Cell Datasets\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Load a single cell data from an online repository.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: A single cell dataset containing cells and their gene expression or gene markers.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Single Cell Datasets\"\n      }), \" retrieves a selected data set from the data base server and sends the data to the output. 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 <p>Preprocess Single Cell data set.</p>
 <p><strong>Inputs</strong></p>
@@ -161,4 +161,4 @@
 <p><strong>Outputs</strong></p>
 <ul>
 <li>Preprocessed Data: Preprocessed dataset.</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get 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 <p>Average spectra.</p>
 <p><strong>Inputs</strong></p>
@@ -165,4 +165,4 @@
 <p>The <strong>Average Spectra</strong> widget enables you to calculate average spectra. It can output the average of the entire dataset, or average into groups defined by a Categorical feature.</p>
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 <p>Use <em>Group by</em> to output averages defined by a Categorical feature.</p>
-<p>Columns of non-Numerical data will return a value if every row in that group has the same value, otherwise it will return Unknown.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Average Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Average spectra.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Averages: averaged dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Average Spectra\"\n      }), \" widget enables you to calculate average spectra. 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+<p>Columns of non-Numerical data will return a value if every row in that group has the same value, otherwise it will return Unknown.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Average Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Average spectra.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Averages: averaged dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Average Spectra\"\n      }), \" widget enables you to calculate average spectra. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/spectra/">Spectra</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/spectroscopy/hyperspectra/">HyperSpectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/preprocess-spectra/">Preprocess Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/integrate-spectra/">Integrate Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/peakfit/">Peak Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/multifile/">Multifile</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/tilefile/">Tile File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/average/">Average Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interferogram-to-spectrum/">Interferogram to Spectrum</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/reshape-map/">Reshape Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/snr/">SNR</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>HyperSpectra</h1>
 <p>Plots 2D map of hyperspectra.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@
 <li>The spectral plot of the selected image region. It behaves like the <a href="/widget-catalog/spectroscopy/spectra">Spectra</a> widget.</li>
 <li>Region selectors for the chosen integration method.</li>
 <li>Split between image and spectral view: move it to increase the image size.</li>
-</ol></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"HyperSpectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots 2D map of hyperspectra.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selection: spectra from selected area\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with information whether a spectrum was selected or not\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"HyperSpectra\"\n      }), \" widget plots hyperspectra that were read from the \", _jsx(_components.em, {\n        children: \".map\"\n      }), \" file. 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 <p>Integrate spectra in various ways.</p>
 <p><strong>Inputs</strong></p>
@@ -187,4 +187,4 @@ <h2>Example</h2>
 <p>We are using the <em>liver spectroscopy</em> data set from the <strong>Datasets</strong> widget. In <strong>Integrate Spectra</strong> we have selected <em>integral from 0</em> and set the lower and upper limit with the red lines. We could also do it by setting the <em>Low limit</em> and <em>High limit</em> values on the left.</p>
 <p>To observe the integrated area, we need to press the triangular play button next to the method. To output the data, we need to press <em>Commit</em>.</p>
 <p>Finally, we can observe the additional column with the integral values of the area in a <strong>Data Table</strong>.</p>
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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Integrate Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Integrate spectra in various 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Integrate Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Integrate spectra in various 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/spectra/">Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/hyperspectra/">HyperSpectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/preprocess-spectra/">Preprocess Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/integrate-spectra/">Integrate Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/peakfit/">Peak Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/multifile/">Multifile</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/tilefile/">Tile File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/average/">Average Spectra</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/spectroscopy/interferogram-to-spectrum/">Interferogram to Spectrum</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/reshape-map/">Reshape Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/snr/">SNR</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Interferogram to Spectrum</h1>
 <p>Performs Fast Fourier Transform on an interferogram, including zero filling, apodization and phase correction.</p>
 <p><strong>Inputs</strong></p>
@@ -162,4 +162,4 @@
 <ul>
 <li>Spectra: dataset with spectra</li>
 <li>Phases: phases</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/spectra/">Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/hyperspectra/">HyperSpectra</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/spectroscopy/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/preprocess-spectra/">Preprocess Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/integrate-spectra/">Integrate Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/peakfit/">Peak Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/multifile/">Multifile</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/tilefile/">Tile File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/average/">Average Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interferogram-to-spectrum/">Interferogram to Spectrum</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/reshape-map/">Reshape Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/snr/">SNR</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Interpolate</h1>
 <p>Interpolate spectra.</p>
 <p><strong>Inputs</strong></p>
@@ -184,4 +184,4 @@ <h2>Examples</h2>
 <p>The second use case is a tad more advanced. We will use <strong>Interpolate</strong> to determine how much granularity we can afford to lose in our measurement. Say we wish to perform a diagnostic much faster. Could we measure only every 10th wavenumber? Or every 50th?</p>
 <p>We will use the <em>Liver spectroscopy</em> data from the <strong>Datasets</strong> widget. Connect the widget to <strong>Interpolate</strong> and use the <em>Linear interval</em> option. The delta is set to 10. Then observe the performance of predictive models in <strong>Test &amp; Score</strong>. Use any classifier you want; we chose <strong>Logistic Regression</strong> and <strong>Random Forest</strong>. The AUC is quite high.</p>
 <p>Now, set the delta to, say, 50 and observe how the AUC changes. Not much. Try setting the delta to 100 or 150. The AUC is still high, which means the classifier is stable even at such a low resolution. This is a nice way to determine how much granularity you can afford to lose to be still able to achieve a good separation between class values.</p>
-<a href="/widget-catalog/spectroscopy/images/__optimized-images__/Interpolate-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1230" height="763" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Interpolate-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Interpolate\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interpolate spectra.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Points: a reference data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Interpolated Data: aligned dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Interpolate\"\n      }), \" widget enables you to align datasets with different wavenumbers. 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+<a href="/widget-catalog/spectroscopy/images/__optimized-images__/Interpolate-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1230" height="763" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Interpolate-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Interpolate\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interpolate spectra.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Points: a reference data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Interpolated Data: aligned dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Interpolate\"\n      }), \" widget enables you to align datasets with different wavenumbers. 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@@ -173,4 +173,4 @@
 <h2>Example</h2>
 <p>Here is a simple example on how to use the <strong>Multifile</strong> widget. We have loaded two data set that were stored on our local machine. We used the folder icon to access the files and load them. Now our files are displayed in the top box. We have labelled the files <em>collagen</em>, make it clear what it is about.</p>
 <p>We can observe the concatenated data in the <strong>Spectra</strong> widget or in a <strong>Data Table</strong>.</p>
-<a href="/widget-catalog/spectroscopy/images/__optimized-images__/Multifile-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1201" height="682" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Multifile-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Multifile\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Read data from input files and send a data table to the output.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: a data table of all the loaded files\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Multifile\"\n      }), \" widget loads data from different sources and works like \", _jsx(_components.strong, {\n        children: \"Concatenate\"\n      }), \" widget for spectroscopy. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/spectra/">Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/hyperspectra/">HyperSpectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/preprocess-spectra/">Preprocess Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/integrate-spectra/">Integrate Spectra</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/spectroscopy/peakfit/">Peak Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/multifile/">Multifile</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/tilefile/">Tile File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/average/">Average Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interferogram-to-spectrum/">Interferogram to Spectrum</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/reshape-map/">Reshape Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/snr/">SNR</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Peak Fit</h1>
 <p>Fit data to a composite peak model.</p>
 <p><strong>Inputs</strong></p>
@@ -249,4 +249,4 @@ <h2>Constraints</h2>
 <li>Limiting the <em>center</em> position to some range of x values</li>
 <li>Setting a minimum <em>amplitude</em> to force positive peaks</li>
 <li>Setting a maximum <em>sigma</em> to exclude unreasonably wide peaks</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Peak Fit\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Fit data to a composite peak model.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Fit Parameters: Best fit values for the model parameters\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Fits: Total evaluated best fit\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Residuals: Difference between \", _jsx(_components.em, {\n          children: \"Fits\"\n        }), \" and \", _jsx(_components.em, {\n          children: \"Data\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Data: Input data set annotated with \", _jsx(_components.em, {\n          children: \"Fit Parameters\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Peak Fit\"\n      }), \" widget computes the least-squares minimization curve fit for arbitrary,\\nuser-defined composite peak models. It outputs the best fit parameters for the defined model and the\\nresulting total fit.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/spectroscopy/images/__optimized-images__/Peak-Fit-stamped.png\",\n      alt: \"\",\n      width: \"1010\",\n      height: \"621\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Add a \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/#models-and-parameters\",\n          children: \"model\"\n        }), \" component from the dropdown menu.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Input model initial parameters and \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/#constraints\",\n          children: \"constraints\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Visualize the initial peak and peak color.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select subsample of data for preview fit calculation.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Preview plot of fit results for subsample. Center line of selected model is visualized along with\\nthe fit results for the selected curve:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Black dash: selected curve\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Red line: total fit\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Colored line: individual component fit\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Colored dash: individual component initial values\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Light black: other subsample spectra which can be selected\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Commit to start fit calculation on entire dataset.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Models and Parameters\"\n    }), \"\\n\", 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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Peak Fit\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Fit data to a composite peak model.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Fit Parameters: Best fit values for the model parameters\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Fits: Total evaluated best fit\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Residuals: Difference between \", _jsx(_components.em, {\n          children: \"Fits\"\n        }), \" and \", _jsx(_components.em, {\n          children: \"Data\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Data: Input data set annotated with \", _jsx(_components.em, {\n          children: \"Fit Parameters\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Peak Fit\"\n      }), \" widget computes the least-squares minimization curve fit for arbitrary,\\nuser-defined composite peak models. It outputs the best fit parameters for the defined model and the\\nresulting total fit.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/spectroscopy/images/__optimized-images__/Peak-Fit-stamped.png\",\n      alt: \"\",\n      width: \"1010\",\n      height: \"621\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Add a \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/#models-and-parameters\",\n          children: \"model\"\n        }), \" component from the dropdown menu.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Input model initial parameters and \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/#constraints\",\n          children: \"constraints\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Visualize the initial peak and peak color.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select subsample of data for preview fit calculation.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Preview plot of fit results for subsample. Center line of selected model is visualized along with\\nthe fit results for the selected curve:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Black dash: selected curve\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Red line: total fit\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Colored line: individual component fit\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Colored dash: individual component initial values\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Light black: other subsample spectra which can be selected\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Commit to start fit calculation on entire dataset.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Models and Parameters\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Peak Fit\"\n      }), \" widget uses the excellent \", _jsx(_components.a, {\n        href: \"https://lmfit.github.io/lmfit-py/index.html\",\n        children: _jsx(_components.code, {\n          children: \"lmfit\"\n        })\n      }), \" package\\nfor model definitions and non-linear optimization calculations.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The varied model parameters are specific to the model, however each peak-like model includes at least:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"center\"\n        }), \": the centroid x value of the peak\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"amplitude\"\n        }), \": multiplicative factor for peak strength or area\"]\n      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\":\\nA model based on a Lorentzian or Cauchy-Lorentz distribution function.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://lmfit.github.io/lmfit-py/builtin_models.html#lmfit.models.SplitLorentzianModel\",\n          children: _jsx(_components.em, {\n            children: \"Split Lorentzian\"\n          })\n        }), \":\\nA Lorentzian model with independent left/right width parameters.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://lmfit.github.io/lmfit-py/builtin_models.html#lmfit.models.VoigtModel\",\n          children: _jsx(_components.em, {\n            children: \"Voigt\"\n          })\n        }), \":\\nA model based on a Voigt distribution function.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: 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@@ -208,4 +208,4 @@ <h2>Example</h2>
 <p>In <strong>Preprocess Spectra</strong> we will select a couple of preprocessing methods and observe their output. First, let us use the <em>Baseline Correction</em> which removes the baseline from the spectra.</p>
 <p>Then we will cut an area of interest with the <em>Cut (keep)</em> method. To set the area we wish to keep, drag the red lines left or right in the plot. You will see how the bottom changes with a change in selection.</p>
 <p>To see the end result of preprocessing, press <em>Final preview</em> and once you are satisfied with the results, press <em>Commit</em>. We can observe the end result in a <strong>Spectra</strong> widget or use the preprocessed data in the downstream analysis.</p>
-<a href="/widget-catalog/spectroscopy/images/__optimized-images__/Preprocess-Spectra-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1253" height="800" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Preprocess-Spectra-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Preprocess Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Construct a data preprocessing pipeline.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: required input data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference: optional reference data set used in some preprocessing methods\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Preprocessed Data: transformed data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing methods\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" widget applies a series of preprocessing methods to spectral data. 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The top plot shows the data before and the bottom after preprocessing.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preview a single preprocessor (the upper plot will show its input, the plot below its output).\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Observe the final result of preprocessing by clicking the \", _jsx(_components.em, {\n          children: \"Final Preview\"\n        }), \" button. Change the number of spectra shown in the plot.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \" to calculate and output the preprocessed data.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing Methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Cut: Select the cutoff value of the spectral area and whether you wish to keep or discard that area.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Gaussian smoothing: apply Gaussian smoothing.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Savitzky-Golay Filter: apply Savitzky-Golay filter.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Baseline Correction: correct the baseline\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Normalize Spectra: apply normalization.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Vector normalization: calculates the L2 norm\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Min-Max normalization: divides each spectra with its Y\", \"max\", \" - Y\", \"min\", \" range\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Area normalization: provides several methods, also allows the selection of a specific range for the calculation\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Attribute normalization: normalize each spectrum with one of the available pre-calculated attributes\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Standard Normal Variate (SNV): \\\\(\\\\tilde{X}^{SNV}_i = (X_i - \\\\tilde{X}_i) / \\\\sigma_i\\\\)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Normalize by Reference: divides each spectrum with the reference spectrum on the input\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Integrate: compute integrals of selected area. Similar to the \", _jsx(_components.strong, {\n          children: \"Integrate Spectra\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"PCA denoising: denoise the data with PCA.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Transmittance to Absorbance: convert from transmittance to absorbance spectra.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Absorbance to Transmittance: convert absorbance spectra to transmittance.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Shift spectra: shift things around.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"EMSC: special Norweigan method.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Spike Removal: Removes spikes in spectra through a modified z-score. \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/SpikeRemoval\",\n          children: \"More...\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Asymmetric Least Squares Smoothing: Three ALS methods which can be used for baseline subtraction. \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/als\",\n          children: \"More...\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Atmospheric gas correction: remove H20/CO2 contributions using a reference spectrum. \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/atmcorr\",\n          children: \"More...\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Normally, we would use \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" at the beginning of the analysis. We will use the \", _jsx(_components.em, {\n        children: \"liver spectroscopy\"\n      }), \" data from the \", _jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" we will select a couple of preprocessing methods and observe their output. First, let us use the \", _jsx(_components.em, {\n        children: \"Baseline Correction\"\n      }), \" which removes the baseline from the spectra.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we will cut an area of interest with the \", _jsx(_components.em, {\n        children: \"Cut (keep)\"\n      }), \" method. To set the area we wish to keep, drag the red lines left or right in the plot. You will see how the bottom changes with a change in selection.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To see the end result of preprocessing, press \", _jsx(_components.em, {\n        children: \"Final preview\"\n      }), \" and once you are satisfied with the results, press \", _jsx(_components.em, {\n        children: \"Commit\"\n      }), \". We can observe the end result in a \", _jsx(_components.strong, {\n        children: \"Spectra\"\n      }), \" widget or use the preprocessed data in the downstream analysis.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/spectroscopy/images/__optimized-images__/Preprocess-Spectra-Example.png\",\n      alt: \"\",\n      width: \"1253\",\n      height: \"800\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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If the reference needs to stay fixed, split your preprocessing methods among multiple \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" widgets and connect references accordingly.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Below is an example of the \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" widget in action with some explanation of the main features.\\n\", _jsx(_components.img, {\n        src: \"/widget-catalog/spectroscopy/images/__optimized-images__/Preprocess-Spectra-stamped.png\",\n        alt: \"\",\n        width: \"986\",\n        height: \"603\"\n      })]\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Add a preprocessor from the dropdown menu.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Preview plot with its editor menu like in the \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/spectra\",\n          children: \"Spectra\"\n        }), \" widget. The top plot shows the data before and the bottom after preprocessing.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preview a single preprocessor (the upper plot will show its input, the plot below its output).\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Observe the final result of preprocessing by clicking the \", _jsx(_components.em, {\n          children: \"Final Preview\"\n        }), \" button. Change the number of spectra shown in the plot.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \" to calculate and output the preprocessed data.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing Methods\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Cut: Select the cutoff value of the spectral area and whether you wish to keep or discard that area.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Gaussian smoothing: apply Gaussian smoothing.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Savitzky-Golay Filter: apply Savitzky-Golay filter.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Baseline Correction: correct the baseline\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Normalize Spectra: apply normalization.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Vector normalization: calculates the L2 norm\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Min-Max normalization: divides each spectra with its Y\", \"max\", \" - Y\", \"min\", \" range\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Area normalization: provides several methods, also allows the selection of a specific range for the calculation\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Attribute normalization: normalize each spectrum with one of the available pre-calculated attributes\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Standard Normal Variate (SNV): \\\\(\\\\tilde{X}^{SNV}_i = (X_i - \\\\tilde{X}_i) / \\\\sigma_i\\\\)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Normalize by Reference: divides each spectrum with the reference spectrum on the input\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Integrate: compute integrals of selected area. Similar to the \", _jsx(_components.strong, {\n          children: \"Integrate Spectra\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"PCA denoising: denoise the data with PCA.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Transmittance to Absorbance: convert from transmittance to absorbance spectra.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Absorbance to Transmittance: convert absorbance spectra to transmittance.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Shift spectra: shift things around.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"EMSC: special Norweigan method.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Spike Removal: Removes spikes in spectra through a modified z-score. \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/SpikeRemoval\",\n          children: \"More...\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Asymmetric Least Squares Smoothing: Three ALS methods which can be used for baseline subtraction. \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/als\",\n          children: \"More...\"\n        })]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Atmospheric gas correction: remove H20/CO2 contributions using a reference spectrum. \", _jsx(_components.a, {\n          href: \"/widget-catalog/spectroscopy/atmcorr\",\n          children: \"More...\"\n        })]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Normally, we would use \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" at the beginning of the analysis. We will use the \", _jsx(_components.em, {\n        children: \"liver spectroscopy\"\n      }), \" data from the \", _jsx(_components.strong, {\n        children: \"Datasets\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" we will select a couple of preprocessing methods and observe their output. First, let us use the \", _jsx(_components.em, {\n        children: \"Baseline Correction\"\n      }), \" which removes the baseline from the spectra.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we will cut an area of interest with the \", _jsx(_components.em, {\n        children: \"Cut (keep)\"\n      }), \" method. To set the area we wish to keep, drag the red lines left or right in the plot. You will see how the bottom changes with a change in selection.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To see the end result of preprocessing, press \", _jsx(_components.em, {\n        children: \"Final preview\"\n      }), \" and once you are satisfied with the results, press \", _jsx(_components.em, {\n        children: \"Commit\"\n      }), \". We can observe the end result in a \", _jsx(_components.strong, {\n        children: \"Spectra\"\n      }), \" widget or use the preprocessed data in the downstream analysis.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/spectroscopy/images/__optimized-images__/Preprocess-Spectra-Example.png\",\n      alt: \"\",\n      width: \"1253\",\n      height: \"800\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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 <p>Builds or modifies the shape of the input dataset to create 2D maps from series data or change the dimensions of existing 2D datasets.</p>
 <p><strong>Inputs</strong></p>
@@ -172,4 +172,4 @@
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 <p>Signal-to-Noise Ratio (SNR)</p>
 <p><strong>Inputs</strong></p>
@@ -194,4 +194,4 @@
 <p><strong>output = Standard Deviation(x)</strong></p>
 <p><em>Standard Deviation = \(\sigma _{column}\)</em></p>
 <hr/>
-<p>If you want the result of the complete data set, you can just leave both as None.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    hr: \"hr\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"SNR\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Signal-to-Noise Ratio (SNR)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Signal-to-noise ratio: signal-to-noise ratio dataset\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"SNR = \\\\(\\\\frac{\\\\overline{Spectra_{x, y}}}{\\\\sigma _{x, y}}\\\\)\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Averages: averaged dataset\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"Averages = \\\\(\\\\overline{Spectra_{x, y}}\\\\)\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Standard Deviation: standard deviation dataset\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"Standard Deviation = \\\\(\\\\sigma _{x, y}\\\\)\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"SNR\"\n      }), \" widget computes the SNR, average, or standard deviation of spectra. It can output the results of an entire dataset or by coordinates (x, y).\"]\n    }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsxs(_components.p, {\n      children: [\"Use \", _jsx(_components.em, {\n        children: \"Select axis: x\"\n      }), \" to select an axis that will act as the first element for your coordinate system defined by a numeric meta.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Use \", _jsx(_components.em, {\n        children: \"Select axis: y\"\n      }), \" to select an axis that will act as the second element for your coordinate system defined by a numeric meta.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/spectroscopy/images/__optimized-images__/snr_print.png\",\n      alt: \"\",\n      width: \"209\",\n      height: \"322\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the example above, the result will be:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, 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+<p>If you want the result of the complete data set, you can just leave both as None.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    hr: \"hr\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"SNR\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Signal-to-Noise Ratio (SNR)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Signal-to-noise ratio: signal-to-noise ratio dataset\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"SNR = \\\\(\\\\frac{\\\\overline{Spectra_{x, y}}}{\\\\sigma _{x, y}}\\\\)\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Averages: averaged dataset\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"Averages = \\\\(\\\\overline{Spectra_{x, y}}\\\\)\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Standard Deviation: standard deviation dataset\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"Standard Deviation = \\\\(\\\\sigma _{x, y}\\\\)\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"SNR\"\n      }), \" widget computes the SNR, average, or standard deviation of spectra. 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 <p>Visually explore series of spectra with no spatial information.</p>
 <p><strong>Inputs</strong></p>
@@ -206,4 +206,4 @@ <h2>Example</h2>
 <p>We have used <em>Color by</em> option to display the type of each spectrum. Or you can also press &#x27;C&#x27; and the plot will show colors. Colors are defined with the data; to change colors, use the <strong>Color</strong> widget.</p>
 <p>Now, let&#x27;s say I am interested in those spectra, that are quite separated from the rest at wavenumber around 1027. I will press &#x27;S&#x27; and drag a line. This will select the spectra under the line I have dragged.</p>
 <p>I can observe the selection in another <strong>Spectra</strong> widget or use it for further analysis.</p>
-<a href="/widget-catalog/spectroscopy/images/__optimized-images__/Spectra-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1258" height="730" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Spectra-Example1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visually explore series of spectra with no spatial information.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset:subset of the data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selection: selected spectra\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Spectra\"\n      }), \" widget allows visual exploration of multiple spectra.\\nTo output some spectra, select them by clicking.\\nFor multiple selection, hold the modifier key (Ctrl or Cmd) or use\\nline selection (see the \", _jsx(_components.a, {\n        href: \"/widget-catalog/spectroscopy/#PlotOptions\",\n        children: \"plot options menu\"\n      }), \"). 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+<a href="/widget-catalog/spectroscopy/images/__optimized-images__/Spectra-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1258" height="730" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Spectra-Example1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Spectra\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visually explore series of spectra with no spatial information.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset:subset of the data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selection: selected spectra\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Spectra\"\n      }), \" widget allows visual exploration of multiple spectra.\\nTo output some spectra, select them by clicking.\\nFor multiple selection, hold the modifier key (Ctrl or Cmd) or use\\nline selection (see the \", _jsx(_components.a, {\n        href: \"/widget-catalog/spectroscopy/#PlotOptions\",\n        children: \"plot options menu\"\n      }), \"). 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/spectra/">Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/hyperspectra/">HyperSpectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/preprocess-spectra/">Preprocess Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/integrate-spectra/">Integrate Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/peakfit/">Peak Fit</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/multifile/">Multifile</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/spectroscopy/tilefile/">Tile File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/average/">Average Spectra</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/interferogram-to-spectrum/">Interferogram to Spectrum</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/reshape-map/">Reshape Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/spectroscopy/snr/">SNR</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Tile File</h1>
 <p>Read data tile-by-tile from input file(s), preprocess the spectra, and send a data table to the output.</p>
 <p><strong>Inputs</strong></p>
@@ -189,4 +189,4 @@ <h2>Example</h2>
 Now information about the preprocessed dataset is displayed in the info box and domain editor.</p>
 <p>We can observe the preprocessed data in the <strong>HyperSpectra</strong> widget or in a <strong>Data Table</strong>.</p>
 <a href="/widget-catalog/spectroscopy/images/__optimized-images__/Tilefile-Example.png" data-gallery="true"><img alt="" loading="lazy" width="812" height="287" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/spectroscopy/images/__optimized-images__/Tilefile-Example.png"/></a>
-<p>This example workflow can be found in <em>Help/Example Workflows</em>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tile File\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Read data tile-by-tile from input file(s), preprocess the spectra, and send a data table to the output.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: A preprocessor list from the Preprocess Spectra widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: preprocessed dataset read from the input file(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Tilefile\"\n      }), \" widgets loads data from compatible mosaic spectral images and applies the supplied\\npreprocessor(s) to the data. The preprocessing is applied one mosaic tile at a time, and the resulting\\nprocessed dataset is combined into a single Data Table.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At least one of the preprocessors should reduce the dataset size (such as \", _jsx(_components.strong, {\n        children: \"Cut\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"Integrate\"\n      }), \") to take\\nadvantage of this file loader and reduce total memory usage.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"By default, the widget will not load the dataset automatically. This prevents loading a large dataset into\\nmemory before the desired preprocessor chain is configured. 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+<p>This example workflow can be found in <em>Help/Example Workflows</em>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tile File\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Read data tile-by-tile from input file(s), preprocess the spectra, and send a data table to the output.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: A preprocessor list from the Preprocess Spectra widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: preprocessed dataset read from the input file(s)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Tilefile\"\n      }), \" widgets loads data from compatible mosaic spectral images and applies the supplied\\npreprocessor(s) to the data. The preprocessing is applied one mosaic tile at a time, and the resulting\\nprocessed dataset is combined into a single Data Table.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"At least one of the preprocessors should reduce the dataset size (such as \", _jsx(_components.strong, {\n        children: \"Cut\"\n      }), \", \", _jsx(_components.strong, {\n        children: \"Integrate\"\n      }), \") to take\\nadvantage of this file loader and reduce total memory usage.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"By default, the widget will not load the dataset automatically. This prevents loading a large dataset into\\nmemory before the desired preprocessor chain is configured. 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Features can be edited by double-clicking on them. The user can change the attribute names, select the type of variable per each attribute (Continuous, Nominal, String, Datetime), and choose how to further define the attributes (as Features, Targets or Meta). The user can also decide to ignore an attribute.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Browse documentation datasets.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on the applied preprocessor list.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is a simple example on how to use the \", _jsx(_components.strong, {\n        children: \"Tilefile\"\n      }), \" widget. We configured a preprocessor list in\\n\", _jsx(_components.strong, {\n        children: \"Preprocess Spectra\"\n      }), \" and connected the \", _jsx(_components.strong, {\n        children: \"Preprocessor\"\n      }), \" output to the input on the \", _jsx(_components.strong, {\n        children: \"Tilefile\"\n      }), \" widget.\\nWe have loaded a mosaic data set that was stored on our local machine. We used the folder icon to access\\nthe file and load them. 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@@ -166,4 +166,4 @@
 <h2>Example</h2>
 <p>The available survival datasets in Orange already have correctly defined Time and Event variable as target. However in some cases there are more than one survival enpoints to choose from: in survival analysis we typically encounter either Overall Survival (OS), Relapse-Free Survival (RFS) or Progression-Free Survival (PFS). In such cases <strong>As Survival</strong> can come in handy to pick the correct Time/Event pair with ease.</p>
 <p>In the example bellow, we use one of the survival datasets already available in Orange, the METABRIC dataset, which offers two clinical endpoints to choose from: OS and RFS. We load the dataset using <a href="https://orangedatamining.com/widget-catalog/data/datasets/">Datasets</a> and simply connect the widget output to <strong>As Survival</strong>. We can then select either Overall Survival Time/Overall Survival or Relapse-Free Survival Time/Relapse-Free Survival as our designated target variables. We use <a href="https://orangedatamining.com/widget-catalog/data/datatable/">Data Table</a> to inspect the data.</p>
-<a href="/widget-catalog/survival-analysis/images/__optimized-images__/AsSurvival-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1274" height="1146" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/survival-analysis/images/__optimized-images__/AsSurvival-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"As Survival Data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Mark features Time and Event as target variables.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference dataset with selected Time and Event marked as target variables\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"As Survival\"\n      }), \" enables the user to manually select which feature will be treated as the Time and Event target variable in the survival dataset.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The available survival datasets in Orange already have correctly defined Time and Event variable as target. 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 <p>Calculate the risk score for each data instance and stratify into high and low-risk cohorts.</p>
 <p><strong>Inputs</strong></p>
@@ -169,4 +169,4 @@
 Stratification is based on defining a threshold using either the median, the mean or log rank test.</p>
 <h2>Example</h2>
 <p>In the example below we load the already available German breast cancer study group 2 (German BC2) dataset using the widget <a href="https://orangedatamining.com/widget-catalog/data/datasets/">Datasets</a>. We input the survival data to the <strong>Cohorts</strong> widget and select what kind of spliting criteria we want to use to stratify the data instances into low and high-risk cohorts. Here we choose the threshold to be the meadian. The defalut Orange setting is set to commit automatically, so the widget immidiately starts calculating the risk score derived from the fitted Cox regression model. We can then inspect the calculated risk score and derived binary stratification in tabular form using the <a href="https://orangedatamining.com/widget-catalog/data/datatable/">Data Table</a> and visualize the estimated survival curves for the high and low risk cohorts using <a href="https://orangedatamining.com/widget-catalog/survival-analysis/kaplan-meier-plot/">Kaplan-Meier Plot</a>.</p>
-<a href="/widget-catalog/survival-analysis/images/__optimized-images__/Cohorts-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1912" height="1886" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/survival-analysis/images/__optimized-images__/Cohorts-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Cohorts\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Calculate the risk score for each data instance and stratify into high and low-risk cohorts.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference data set augumented with two new variables, one with the computed risk scores and a binary variable stratifying instances into low and high-risk\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Cohorts\"\n      }), \" is a widget that receives a survival dataset and assigns a risk score to each data instance. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/as-survival-data/">As Survival Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/kaplan-meier-plot/">Kaplan-Meier Plot</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/survival-analysis/cox-regression/">Cox regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/rank-survival-features/">Rank Survival Features</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/stepwise-cox-regression/">Stepwise Cox Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/cohorts/">Cohorts</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Cox Regression</h1>
 <p>Fit the Cox regression model on input data.</p>
 <p><strong>Inputs</strong></p>
@@ -168,4 +168,4 @@
 <p><strong>Cox Regression</strong> is a method for investigating the effect of several variables upon the time a specified event takes to happen. It assumes that the effects of the predictor variables upon survival are constant over time and are additive in one scale.</p>
 <h2>Example</h2>
 <p>In this example we estimate the concordence index for the Cox regression model trained on data instances from selected features by using cross-validation. The concordance index quantifies the quality of rankings and is the standard performance measure for model assessment in survival analysis.We first load the German breast cancer study group 2 (German BC2) dataset using the widget <a href="https://orangedatamining.com/widget-catalog/data/datasets/">Datasets</a>. Next we input the data into the <a href="https://orangedatamining.com/widget-catalog/survival-analysis/rank-survival-features/">Rank Survival Features</a> widget and select the top two most informative features, Number of Positive Nodes and Progesterone Receptor. We input this reduced dataset comprised only of the selected features and the <strong>Cox Regression</strong> learner into <a href="https://orangedatamining.com/widget-catalog/evaluate/testandscore/">Test and Score</a>, where we can inspect the cross-validated concordance measure.</p>
-<a href="/widget-catalog/survival-analysis/images/__optimized-images__/CoxRegression-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2002" height="1480" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/survival-analysis/images/__optimized-images__/CoxRegression-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Cox Regression\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Fit the Cox regression model on input data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Learner: Cox regression learning algorithm\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Model trained model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Coefficients: Cox regression coefficients\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Cox Regression\"\n      }), \" is a method for investigating the effect of several variables upon the time a specified event takes to happen. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/as-survival-data/">As Survival Data</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/survival-analysis/kaplan-meier-plot/">Kaplan-Meier Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/cox-regression/">Cox regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/rank-survival-features/">Rank Survival Features</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/stepwise-cox-regression/">Stepwise Cox Regression</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/survival-analysis/cohorts/">Cohorts</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Kaplan-Meier Plot</h1>
 <p>Visualisation of Kaplan-Meier estimator.</p>
 <p><strong>Inputs</strong></p>
@@ -168,4 +168,4 @@ <h2>Example</h2>
 <p>In this simple example we use the <strong>Kaplan-Meier Plot</strong> to visualize the survival function of the investigated population in the German breast cancer study group 2. We load the already available data with the use of <a href="https://orangedatamining.com/widget-catalog/data/datasets/">Datasets</a> and simply connect it to the <strong>Kaplan-Meier Plot</strong>. The use of <a href="/widget-catalog/survival-analysis/as-survival-data">As Survival</a>, in this case, is not necessary, as there is only one Time/Event pair, and the dataset was pre-curated for use in survival widgets. The <strong>Kaplan-Meier Plot</strong> estimates and plots the survival function from the lifeline data provided at the input. On the left side of the widget we select to compare survival curves of groups based on wheter or not the patient recieved hormonal therapy and choose to display the confidence intervals.</p>
 <a href="/widget-catalog/survival-analysis/images/__optimized-images__/KaplanMeierPlot-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1952" height="1268" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/survival-analysis/images/__optimized-images__/KaplanMeierPlot-Example.png"/></a>
 <h2>References</h2>
-<p>Davidson-Pilon, (2019). lifelines: survival analysis in Python. Journal of Open Source Software, 4(40), 1317, <a href="https://doi.org/10.21105/joss.01317">https://doi.org/10.21105/joss.01317</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    h2: \"h2\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Kaplan-Meier Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualisation of Kaplan-Meier estimator.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Kaplan-Meier Plot\"\n      }), \" is a visual representation of the estimated survival function that shows the probability of an event at a respective time interval. 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+<p>Davidson-Pilon, (2019). lifelines: survival analysis in Python. Journal of Open Source Software, 4(40), 1317, <a href="https://doi.org/10.21105/joss.01317">https://doi.org/10.21105/joss.01317</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    h2: \"h2\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Kaplan-Meier Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualisation of Kaplan-Meier estimator.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Kaplan-Meier Plot\"\n      }), \" is a visual representation of the estimated survival function that shows the probability of an event at a respective time interval. 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 <p>Rank features according to univariate estimate of importance by Cox regression model.</p>
 <p><strong>Inputs</strong></p>
@@ -167,4 +167,4 @@
 <h2>Example</h2>
 <p>In this example we use <strong>Rank Survival Features</strong> to discover the most important survival feature in the German breast cancer study group 2. We load the already available data with the use of <a href="https://orangedatamining.com/widget-catalog/data/datasets/">Datasets</a> and connect it to <strong>Rank Survival Features</strong>. We sort the features according to the p-value and select the top feature (with the smallest p-value), Number of Positive Nodes. This way the widget outputs a dataset comprised only of the selected feature. In this example we proceed to display the distribution of the feature of interest and select part of the distribution to define the target cohort. Interactively selecting patients in the
 distribution plot allows us to rapidly generate new groups of patients. We can visualize and compare the survival curves of the generated cohort to that of other patients,  using the <a href="https://orangedatamining.com/widget-catalog/survival-analysis/kaplan-meier-plot/">Kaplan-Meier Plot</a>.</p>
-<a href="/widget-catalog/survival-analysis/images/__optimized-images__/RankSurvivalFeatures-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2250" height="1832" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/survival-analysis/images/__optimized-images__/RankSurvivalFeatures-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    h2: \"h2\",\n    a: \"a\",\n    img: \"img\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Rank Survival Features\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Rank features according to univariate estimate of importance by Cox regression model.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference survival dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Reduced data: data comprised of selected features\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Rank Survival Features\"\n      }), \" receives a survival dataset on the input and ranks each feature according to a univariate estimate of importance by the Cox regression model. 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 <p>Perform feature selection through backward elimination process.</p>
 <p><strong>Inputs</strong></p>
@@ -162,4 +162,4 @@
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 <li>todo</li>
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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>LDAvis</h1>
 <p>Interactive exploration of LDA topics.</p>
 <p><strong>Inputs</strong></p>
@@ -171,4 +171,4 @@ <h2>Example</h2>
 <p>In MDS, we have set the color and size of the point to <em>Marginal Topic Probability</em> and labelled the point with their topic names. This represent the left part of LDAvis visualization, namely the PCA-based MDS projection of topic similarity and their corresponding weights in the corpus. It seems that Topic 8 is the most frequent topic in the corpus.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/LDAvis-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2968" height="1744" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/LDAvis-Example.png"/></a>
 <h2>References</h2>
-<p>Sievert, Carson  and Kenneth Shirley (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. <a href="https://aclanthology.org/W14-3110">Available online.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"LDAvis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interactive exploration of LDA topics.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Topics: All LDA topics from topic modeling.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"LDAvis\"\n      }), \" is an Orange implementation of pyLDAvis, which is further derived from an R package LDAvis by Sievert and Shirely (2014). 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+<p>Sievert, Carson  and Kenneth Shirley (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. <a href="https://aclanthology.org/W14-3110">Available online.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"LDAvis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interactive exploration of LDA topics.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Topics: All LDA topics from topic modeling.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"LDAvis\"\n      }), \" is an Orange implementation of pyLDAvis, which is further derived from an R package LDAvis by Sievert and Shirely (2014). The visualization is designed for the exploration of LDA topic models by adjusting the weights of top ranked topic words. Parameter Relevance adjusts word weights and it can be between 0 and 1, where 1 represents the words ranked solely by their topic frequency, and 0 those ranked solely by their lift (ratio of the probability of the word in a topic to the probability of thw word in the corpus).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/LDAvis.png\",\n      alt: \"\",\n      width: \"888\",\n      height: \"634\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Parameter for adjusting the word's relevance in the topic. 1 would show words as seen in the Topic Modeling widget.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"List of topics.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The plot on the right shows the top 20 words as ranked by the computed relevance. The default relevance is 0.5, which strikes a good balance between in-topic probability and lift (the authors suggest 0.6). The weights (x axis) show absolute word counts. The red bar shows in-topic probability, while the gray bar shows the in-corpus probability of a given word.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We show how to approximate the exact LDAvis in Orange. We are using \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" corpus in the example, which we load with \", _jsx(_components.a, {\n        href: \"corpus-widget.md\",\n        children: \"Corpus\"\n      }), \", pass it to \", _jsx(_components.a, {\n        href: \"preprocesstext.md\",\n        children: \"Preprocess Text\"\n      }), \", then to \", _jsx(_components.a, {\n        href: \"bagofwords-widget.md\",\n        children: \"Bag of Words\"\n      }), \" and finally to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/topicmodelling-widget\",\n        children: \"Topic Modelling\"\n      }), \", where we compute 10 topics with the LDA method.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, we pass the \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" output to \", _jsx(_components.strong, {\n        children: \"LDAvis\"\n      }), \" and to \", _jsx(_components.strong, {\n        children: \"MDS\"\n      }), \". LDAvis shows the top ranked words for Topic 3, which seems to describe an industrial realm. One can adjust the Relevance slider to interactively explore word rankings.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In MDS, we have set the color and size of the point to \", _jsx(_components.em, {\n        children: \"Marginal Topic Probability\"\n      }), \" and labelled the point with their topic names. This represent the left part of LDAvis visualization, namely the PCA-based MDS projection of topic similarity and their corresponding weights in the corpus. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Annotated Corpus Map</h1>
 <p>Annotated Corpus Map visualises, cluster, and annotates documents with keywords
 in two-dimensional projection.</p>
@@ -238,4 +238,4 @@ <h2>Example</h2>
 related to work and students.</p>
 <p>We can select a subset of documents of interest and observe them in the
 <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a> widget connected to the output.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Annotator-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2566" height="1778" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Annotator-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    code: \"code\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Annotated Corpus Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Annotated Corpus Map visualises, cluster, and annotates documents with keywords\\nin two-dimensional projection.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents to visualise.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Document: Documents selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Data with an additional column showing whether a document is selected and information about the cluster the document belongs to\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: Table with scores and p-values for each keyword averaged per cluster\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Annotated Corpus Map\"\n      }), \" widget provides a 2-dimensional scatter plot visualization\\nof the document. The documents are displayed as a collection of points, each having\\nthe value of the x-axis attribute determining the position on the horizontal axis\\nand the value of the y-axis attribute determining the position on the vertical axis.\\nWidget clusters documents according to annotation settings and assigns keywords to\\neach cluster. 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Unchecking\\nepsilon will instruct the widget to set epsilon automatically.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: _jsx(_components.a, {\n                href: \"https://scikit-learn.org/stable/modules/mixture.html#gaussian-mixture\",\n                children: \"Gaussian mixture models\"\n              })\n            }), \":\\nSelect the number of desired clusters. Unchecking clusters will instruct the widget\\nto set the numbers of clusters automatically.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"From variable\"\n            }), \" clusters documents according to a selected categorical variable.\"]\n          }), \"\\n\"]\n        }), \"\\n\", \"FDR Threshold sets the threshold for selecting a keyword as a cluster's keyword.\\nKeywords with corrected p-value (FDR) from the hypergeometric test lower than\\nthe threshold are selected.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"Attributes\"\n        }), \": Set the colour of the displayed points (you will get colours\\nfor categorical values and blue-green-yellow points for numeric). 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Annotator-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2566" height="1778" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Annotator-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    code: \"code\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Annotated Corpus Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Annotated Corpus Map visualises, cluster, and annotates documents with keywords\\nin two-dimensional projection.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents to visualise.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Document: Documents selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Data with an additional column showing whether a document is selected and information about the cluster the document belongs to\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Scores: Table with scores and p-values for each keyword averaged per cluster\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Annotated Corpus Map\"\n      }), \" widget provides a 2-dimensional scatter plot visualization\\nof the document. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Bag of Words</h1>
 <p>Generates a bag of words from the input corpus.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Example</h2>
 <p>In the second example we will try to predict document category. We are still using the <em>book-excerpts.tab</em> data set, which we sent through <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a> with default parameters. Then we connected <strong>Preprocess Text</strong> to <strong>Bag of Words</strong> to obtain term frequencies by which we will compute the model.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Bag-of-Words-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1495" height="1018" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Bag-of-Words-Example2.png"/></a>
 <p>Connect <strong>Bag of Words</strong> to <strong>Test &amp; Score</strong> for predictive modelling. Connect <strong>SVM</strong> or any other classifier to <strong>Test &amp; Score</strong> as well (both on the left side). <strong>Test &amp; Score</strong> will now compute performance scores for each learner on the input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.</p>
-<p>Add <strong>Confusion Matrix</strong> to <strong>Test &amp; Score</strong>. Confusion matrix displays correctly and incorrectly classified documents. <em>Select Misclassified</em> will output misclassified documents, which we can further inspect with <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Bag of Words\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generates a bag of words from the input corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with bag of words features appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" model creates a corpus with word counts for each data instance (document). 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Alternatively press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example we will simply check how the bag of words model looks like. Load \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget and connect it to \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \". Here we kept the defaults - a simple count of term frequencies. Check what the \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" outputs with \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \". The final column in white represents term frequencies for each document.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Bag-of-Words-Example1.png\",\n      alt: \"\",\n      width: \"1026\",\n      height: \"708\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example we will try to predict document category. We are still using the \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" data set, which we sent through \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \" with default parameters. Then we connected \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" to obtain term frequencies by which we will compute the model.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Bag-of-Words-Example2.png\",\n      alt: \"\",\n      width: \"1495\",\n      height: \"1018\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" for predictive modelling. Connect \", _jsx(_components.strong, {\n        children: \"SVM\"\n      }), \" or any other classifier to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" as well (both on the left side). \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" will now compute performance scores for each learner on the input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Add \", _jsx(_components.strong, {\n        children: \"Confusion Matrix\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \". Confusion matrix displays correctly and incorrectly classified documents. \", _jsx(_components.em, {\n        children: \"Select Misclassified\"\n      }), \" will output misclassified documents, which we can further inspect with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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+<p>Add <strong>Confusion Matrix</strong> to <strong>Test &amp; Score</strong>. Confusion matrix displays correctly and incorrectly classified documents. <em>Select Misclassified</em> will output misclassified documents, which we can further inspect with <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Bag of Words\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generates a bag of words from the input corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with bag of words features appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" model creates a corpus with word counts for each data instance (document). The count can be either absolute, binary (contains or does not contain) or sublinear (logarithm of the term frequency). 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Alternatively press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example we will simply check how the bag of words model looks like. Load \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget and connect it to \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \". Here we kept the defaults - a simple count of term frequencies. Check what the \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" outputs with \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \". The final column in white represents term frequencies for each document.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Bag-of-Words-Example1.png\",\n      alt: \"\",\n      width: \"1026\",\n      height: \"708\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example we will try to predict document category. We are still using the \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" data set, which we sent through \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \" with default parameters. Then we connected \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" to obtain term frequencies by which we will compute the model.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Bag-of-Words-Example2.png\",\n      alt: \"\",\n      width: \"1495\",\n      height: \"1018\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect \", _jsx(_components.strong, {\n        children: \"Bag of Words\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" for predictive modelling. Connect \", _jsx(_components.strong, {\n        children: \"SVM\"\n      }), \" or any other classifier to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" as well (both on the left side). \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \" will now compute performance scores for each learner on the input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Add \", _jsx(_components.strong, {\n        children: \"Confusion Matrix\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Test \u0026 Score\"\n      }), \". Confusion matrix displays correctly and incorrectly classified documents. \", _jsx(_components.em, {\n        children: \"Select Misclassified\"\n      }), \" will output misclassified documents, which we can further inspect with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Collocations</h1>
 <p>Compute significant bigrams and trigrams.</p>
 <p><strong>Inputs</strong></p>
@@ -187,4 +187,4 @@ <h2>Example</h2>
 <p>We use the <em>grimm-tales-selected</em> data in the <a href="/widget-catalog/text-mining/corpus-widget">Corpus</a> and send the data to Collocations.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Collocations-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1224" height="1284" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Collocations-Example.png"/></a>
 <h2>References</h2>
-<p>Manning, Christopher, and Hinrich Schütze. 1999. Collocations. Available at: <a href="https://nlp.stanford.edu/fsnlp/promo/colloc.pdf">https://nlp.stanford.edu/fsnlp/promo/colloc.pdf</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Collocations\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute significant bigrams and trigrams.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Table: A list of bigrams or trigrams.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Collocations\"\n      }), \" finds frequently co-occurring words in a corpus. 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+<p>Manning, Christopher, and Hinrich Schütze. 1999. Collocations. Available at: <a href="https://nlp.stanford.edu/fsnlp/promo/colloc.pdf">https://nlp.stanford.edu/fsnlp/promo/colloc.pdf</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Collocations\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute significant bigrams and trigrams.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Table: A list of bigrams or trigrams.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Collocations\"\n      }), \" finds frequently co-occurring words in a corpus. 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kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Concordance</h1>
 <p>Display the context of the word.</p>
 <p><strong>Inputs</strong></p>
@@ -184,4 +184,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Concordance-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1369" height="685" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Concordance-Example1.png"/></a>
 <p>In the second example, we will output concordances instead. We will keep the <em>book-excerpts.tab</em> in <a href="/widget-catalog/text-mining/corpus-widget">Corpus</a> and the connection to <strong>Concordance</strong>. Our queried word remains &#x27;doctor&#x27;.</p>
 <p>This time, we will connect <strong>Data Table</strong> to <strong>Concordance</strong> and select Concordances output instead. In the <strong>Data Table</strong>, we get a list of concordances for the queried word and the corresponding documents. Now, we will save this table with <strong>Save Data</strong> widget, so we can use it in other projects or for further analysis.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Concordance-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2832" height="1268" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Concordance-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Concordance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Display the context of the word.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Documents: Documents containing the queried word.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Concordances: A table of concordances.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Concordance\"\n      }), \" finds the queried word in a text and displays the context in which this word is used. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Concordance-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2832" height="1268" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Concordance-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Concordance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Display the context of the word.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Documents: Documents containing the queried word.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Concordances: A table of concordances.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Concordance\"\n      }), \" finds the queried word in a text and displays the context in which this word is used. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Corpus</h1>
 <p>Load a corpus of text documents, (optionally) tagged with categories, or change the data input signal to the corpus.</p>
 <p><strong>Inputs</strong></p>
@@ -185,4 +185,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="2310" height="1246" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Example1.png"/></a>
 <p>The second example demonstrates how to quickly visualize your corpus with <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>. We could connect <strong>Word Cloud</strong> directly to <strong>Corpus</strong>, but instead, we decided to apply some preprocessing
 with <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a>. We are again working with <em>book-excerpts.tab</em>. We&#x27;ve put all text to lowercase, tokenized (split) the text to words only, filtered out English stopwords and selected 100 most frequent tokens.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2482" height="1716" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Corpus\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Load a corpus of text documents, (optionally) tagged with categories, or change the data input signal to the corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data (optional)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget can work in two modes:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"When no data on input, it reads text corpora from files and sends a corpus instance to its output channel. History of the most recently opened files is maintained in the widget. The widget also includes a directory with sample corpora that come pre-installed with the add-on.\\nThe widget reads data from Excel (\", _jsx(_components.strong, {\n          children: \".xlsx\"\n        }), \"), comma-separated (\", _jsx(_components.strong, {\n          children: \".csv\"\n        }), \") and native tab-delimited (\", _jsx(_components.strong, {\n          children: \".tab\"\n        }), \") files.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"When the user provides data to the input, it transforms data into the corpus. Users can select which features are used as text features.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Corpus-stamped.png\",\n      alt: \"\",\n      width: \"990\",\n      height: \"812\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Browse through previously opened data files, or load any of the sample ones.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Browse for a data file.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reloads currently selected data file.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select the variable that is shown as a document title in Corpus Viewer.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features that will be used in text analysis.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features that won't be used in text analysis.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Browse through the datasets that come together with an add-on.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Access help, make a report and get information on the loaded data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You can drag and drop features between the two boxes and also change the order in which they appear.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The first example shows a very simple use of \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2482" height="1716" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Corpus\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Load a corpus of text documents, (optionally) tagged with categories, or change the data input signal to the corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Input data (optional)\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget can work in two modes:\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"When no data on input, it reads text corpora from files and sends a corpus instance to its output channel. 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Corpus to Network</h1>
 <p>Creates a network from given corpus. Network nodes can be either documents or words (ngrams).</p>
 <p><strong>Inputs</strong></p>
@@ -184,4 +184,4 @@ <h2>Examples</h2>
 <p>In <a href="documentembedding.md">Document Embedding</a> widget, we saw how we can predict document category using it. Let&#x27;s now try to improve the score even further by adding features obtained from network. We will keep working on <em>book-excerpts.tab</em> loaded with <a href="/widget-catalog/text-mining/corpus-widget">Corpus</a> widget and sent through <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a> with default parameters. Connect <strong>Preprocess Text</strong> to <strong>Document Embedding</strong> to obtain features for predictive modelling. Here we set aggregator to Sum.</p>
 <p>The first part was the same. Let&#x27;s now obtain some features from network. Connect <strong>Document Embedding</strong> widget to <strong>Corpus to Network</strong>, set <em>Node type</em> to Document, <em>Threshold</em> to 50 and press Start. Connect <strong>Corpus to Network</strong> to <strong>Network Analysis</strong> widget. Double click on the connection and connect Node data to Items so that the output data contains previously obtained embedding features. Open <strong>Network Analysis</strong> widget and uncheck everything under the Graph-level indices tab and check everything under the Node-level indices tab. You can connect <strong>Data Table</strong> widget to inspect the output.</p>
 <p>Now connect <strong>Network Analysis</strong> widget to <strong>Test and Score</strong> and also connect learner of choice to the left side of <strong>Test and Score</strong>. We chose SVM and changed kernel to Linear. <strong>Test and Score</strong> will now compute performance of each learner on the input. We can see that we obtained even better results by adding network features. Let&#x27;s connect <strong>Test and Score</strong> to <strong>Confusion Matrix</strong>. New features helped us correctly classify two more examples.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Corpus-to-Network-Example3.png" data-gallery="true"><img alt="" loading="lazy" width="1296" height="740" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Corpus-to-Network-Example3.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Corpus to Network\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Creates a network from given corpus. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Corpus-to-Network-Example3.png" data-gallery="true"><img alt="" loading="lazy" width="1296" height="740" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Corpus-to-Network-Example3.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Corpus to Network\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Creates a network from given corpus. Network nodes can be either documents or words (ngrams).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Network: A network generated from input corpus.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Node data: Additional data about nodes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Corpus to Network\"\n      }), \" can operate either on documents or words (ngrams). If nodes are documents, there's an edge between two documents if number of words (ngrams) that appears in both documents is at least \", _jsx(_components.em, {\n        children: \"Threshold\"\n      }), \". If nodes are words (ngrams), there's an edge between two words if the number of times they both appear inside of a window (of size 2 * \", _jsx(_components.em, {\n        children: \"Window size\"\n      }), \" + 1) is at least \", _jsx(_components.em, {\n        children: \"Threshold\"\n      }), \". Only words that have frequency higher than \", _jsx(_components.em, {\n        children: \"Frequency Threshold\"\n      }), \" will be included as nodes. This is a word \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Co-occurrence_network\",\n        children: \"co-occurrence network\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Corpus-to-Network-stamped.png\",\n      alt: \"\",\n      width: \"304\",\n      height: \"279\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Widget parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Node type: controls whether nodes will be documents or words (ngrams)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Threshold: controls threshold needed for defining the edge between two nodes (see explanation above)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Window size: controls window size (see explanation above), applicable only if Node type is set to Word\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Frequency Threshold: controls threshold for word frequency (see explanation above), applicable only if Node type is set to Word\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Button for starting computation after stopping it or changing parameters while the widget is running. If clicked, it is replaced with Stop button.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In first example, we will inspect how the widget works.  Load \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" using \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget and connect it to \", _jsx(_components.strong, {\n        children: \"Corpus to Network\"\n      }), \". We see that the input contains 140 documents and the output is network with 140 nodes and 9730 edges. This is a complete graph, which is expected because the \", _jsx(_components.em, {\n        children: \"Threshold\"\n      }), \" parameter is set to 1 (it is expected that each document shares at least 1 word). Change it a little to see how the number of edges changes.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Corpus-to-Network-Example1.png\",\n      alt: \"\",\n      width: \"1296\",\n      height: \"742\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Let's now create a word co-occurrence network and visualize it. Load \", _jsx(_components.em, {\n        children: \"book-excerpts.tab\"\n      }), \" using \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget, connect it to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \" with default parameters and connect \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Corpus to Network\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Corpus Viewer</h1>
 <p>Displays corpus content.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@
 <h2>Example</h2>
 <p><em>Corpus Viewer</em> can be used for displaying all or some documents in corpus. In this example, we will first load <em>book-excerpts.tab</em>, that already comes with the add-on, into <a href="corpus-widget.md">Corpus</a> widget. Then we will preprocess the text into words, filter out the stopwords, create bi-grams and add POS tags (more on preprocessing in <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a>. Now we want to see the results of preprocessing. In <em>Corpus Viewer</em> we can see, how many unique tokens we got and what they are (tick <em>Show Tokens &amp; Tags</em>). Since we used also POS tagger to show part-of-speech labels, they will be displayed alongside tokens underneath the text.</p>
 <p>Now we will filter out just the documents talking about a character Bill. We use regular expression <em>\bBill\b</em> to find the documents containing only the word Bill. You can output matching or non-matching documents, view them in another <em>Corpus Viewer</em> or further analyse them.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Viewer-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1498" height="1010" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Corpus-Viewer-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Corpus Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays corpus content.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Documents containing the queried word.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Corpus Viewer\"\n      }), \" is meant for viewing text files (instances of Corpus). 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Create Corpus</h1>
 <p>Write/paste documents to create a corpus</p>
 <p><strong>Outputs</strong></p>
@@ -168,4 +168,4 @@
 </ol>
 <h2>Example</h2>
 <p>In this simple example we entered three new documents in the <strong>Create Corpus</strong> widget and sent them to <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a> to display them.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Create-Corpus-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2552" height="1628" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Create-Corpus-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Create Corpus\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Write/paste documents to create a corpus\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: The created corpus.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Create Corpus\"\n      }), \" is a widget for quick creation of a sample of documents. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Document Map</h1>
 <p>Displays geographic locations mentioned in the text.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/DocMap-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1380" height="1004" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/DocMap-Example.png"/></a>
 <p>Then we sent the data to <strong>Document Map</strong> to see distributions of geolocations by <em>country</em> attribute. The attribute already contains country tags for each article, which is why <strong>NY Times</strong> is great in combinations with <strong>Document Map</strong>. We selected Germany, which sends all the documents tagged with Germany to the output. Remember, we queried <strong>NY Times</strong> for articles on Slovenia.</p>
 <p>We can again inspect the output with <strong>Corpus Viewer</strong>. But there&#x27;s a more interesting way of visualizing the data. We&#x27;ve sent selected documents to <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a>, where we&#x27;ve tokenized text to words and removed stopwords.</p>
-<p>Finally, we can inspect the top words appearing in last year&#x27;s documents on Slovenia and mentioning also Germany with <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Document Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays geographic locations mentioned in the text.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Documents containing mentions of selected geographical regions.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \" widget shows geolocations from textual (string) data. It finds mentions of geographic names (countries and capitals) and displays distributions (frequency of mentions) of these names on a map. It works with any Orange widget that outputs a data table and that contains at least one string attribute. The widget outputs selected data instances, that is all documents containing mentions of a selected country (or countries).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/DocMap-stamped.png\",\n      alt: \"\",\n      width: \"818\",\n      height: \"562\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the meta attribute you want to search geolocations by. The widget will find all mentions of geolocations in a text and display distributions on a map.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select the type of map you wish to display. The options are \", _jsx(_components.em, {\n          children: \"World\"\n        }), \", \", _jsx(_components.em, {\n          children: \"Europe\"\n        }), \" and \", _jsx(_components.em, {\n          children: \"USA\"\n        }), \". You can zoom in and out of the map by pressing + and - buttons on a map or by mouse scroll.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"The legend for the geographic distribution of data. Countries with the boldest color are most often mentioned in the selected region attribute (highest frequency).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To select documents mentioning a specific country, click on a country and the widget will output matching documents. To select more than one country hold Ctrl/Cmd upon selection.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \" widget can be used for simply visualizing distributions of geolocations or for a more complex interactive data analysis. Here, we've queried \", _jsx(_components.a, {\n        href: \"nytimes.md\",\n        children: \"NY Times\"\n      }), \" for articles on Slovenia for the time period of the last year (2015-2016). First we checked the results with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/DocMap-Example.png\",\n      alt: \"\",\n      width: \"1380\",\n      height: \"1004\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we sent the data to \", _jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \" to see distributions of geolocations by \", _jsx(_components.em, {\n        children: \"country\"\n      }), \" attribute. The attribute already contains country tags for each article, which is why \", _jsx(_components.strong, {\n        children: \"NY Times\"\n      }), \" is great in combinations with \", _jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \". 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+<p>Finally, we can inspect the top words appearing in last year&#x27;s documents on Slovenia and mentioning also Germany with <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Document Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays geographic locations mentioned in the text.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Data set.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Documents containing mentions of selected geographical regions.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \" widget shows geolocations from textual (string) data. It finds mentions of geographic names (countries and capitals) and displays distributions (frequency of mentions) of these names on a map. It works with any Orange widget that outputs a data table and that contains at least one string attribute. The widget outputs selected data instances, that is all documents containing mentions of a selected country (or countries).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/DocMap-stamped.png\",\n      alt: \"\",\n      width: \"818\",\n      height: \"562\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the meta attribute you want to search geolocations by. The widget will find all mentions of geolocations in a text and display distributions on a map.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select the type of map you wish to display. The options are \", _jsx(_components.em, {\n          children: \"World\"\n        }), \", \", _jsx(_components.em, {\n          children: \"Europe\"\n        }), \" and \", _jsx(_components.em, {\n          children: \"USA\"\n        }), \". You can zoom in and out of the map by pressing + and - buttons on a map or by mouse scroll.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"The legend for the geographic distribution of data. Countries with the boldest color are most often mentioned in the selected region attribute (highest frequency).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To select documents mentioning a specific country, click on a country and the widget will output matching documents. To select more than one country hold Ctrl/Cmd upon selection.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \" widget can be used for simply visualizing distributions of geolocations or for a more complex interactive data analysis. Here, we've queried \", _jsx(_components.a, {\n        href: \"nytimes.md\",\n        children: \"NY Times\"\n      }), \" for articles on Slovenia for the time period of the last year (2015-2016). First we checked the results with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/DocMap-Example.png\",\n      alt: \"\",\n      width: \"1380\",\n      height: \"1004\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we sent the data to \", _jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \" to see distributions of geolocations by \", _jsx(_components.em, {\n        children: \"country\"\n      }), \" attribute. The attribute already contains country tags for each article, which is why \", _jsx(_components.strong, {\n        children: \"NY Times\"\n      }), \" is great in combinations with \", _jsx(_components.strong, {\n        children: \"Document Map\"\n      }), \". We selected Germany, which sends all the documents tagged with Germany to the output. Remember, we queried \", _jsx(_components.strong, {\n        children: \"NY Times\"\n      }), \" for articles on Slovenia.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can again inspect the output with \", _jsx(_components.strong, {\n        children: \"Corpus Viewer\"\n      }), \". But there's a more interesting way of visualizing the data. We've sent selected documents to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \", where we've tokenized text to words and removed stopwords.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, we can inspect the top words appearing in last year's documents on Slovenia and mentioning also Germany with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordcloud\",\n        children: \"Word Cloud\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File Import.png","url":"csvfileimport"},{"title":"Datasets","category":"Data","keywords":["datasets","online","data","sets"],"background":"#FFD39F","icon":"widget-icons/Data-Datasets.png","url":"datasets"},{"title":"SQL Table","category":"Data","keywords":["sql table","load"],"background":"#FFD39F","icon":"widget-icons/Data-SQL Table.png","url":"sqltable"},{"title":"Data Table","category":"Data","keywords":["data table","view"],"background":"#FFD39F","icon":"widget-icons/Data-Data Table.png","url":"datatable"},{"title":"Paint Data","category":"Data","keywords":["paint 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Document Embedding</h1>
 <p>Embeds documents from input corpus into vector space by using pre-trained
 <a href="https://fasttext.cc/docs/en/crawl-vectors.html">fastText</a> models described in E. Grave et al. (2018).</p>
@@ -187,4 +187,4 @@ <h2>Examples</h2>
 Clicking on <em>Select Misclassified</em> will output documents that were misclassified. We can further inspect them by connecting <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a> to <strong>Confusion Matrix</strong>.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Document-Embedding-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1297" height="738" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Document-Embedding-Example2.png"/></a>
 <h2>References</h2>
-<p>E. Grave, P. Bojanowski, P. Gupta, A. Joulin, T. Mikolov. &quot;Learning Word Vectors for 157 Languages.&quot; <em>Proceedings of the International Conference on Language Resources and Evaluation</em>, 2018.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Document Embedding\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Embeds documents from input corpus into vector space by using pre-trained\\n\", _jsx(_components.a, {\n        href: \"https://fasttext.cc/docs/en/crawl-vectors.html\",\n        children: \"fastText\"\n      }), \" models described in E. Grave et al. (2018).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with new features appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Embedding\"\n      }), \" parses n-grams of each document in corpus, obtains embedding\\nfor each n-gram using pre-trained model for chosen language and obtains one vector for each document by aggregating n-gram embeddings using one of offered aggregators. Note that method will work on any n-grams but it will give best results if corpus is preprocessed such that n-grams are words (because model was trained to embed words).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Document-Embedding-stamped.png\",\n      alt: \"\",\n      width: \"304\",\n      height: \"266\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Widget parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Language: widget will use a model trained on documents in chosen language.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Aggregator: operation to perform on n-gram embeddings to aggregate them into a single document vector.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Cancel current execution.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Apply automatically\"\n        }), \" is checked, changes in parameters are sent automatically. Alternatively press \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Embedding retrieval\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Embedding\"\n      }), \" takes n-grams (tokens), usually produced by the \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \" widget. One can see tokens in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \" widget by selection \", _jsx(_components.em, {\n        children: \"Show tokens and tags\"\n      }), \" or in \", _jsx(_components.a, {\n        href: \"wordcloud.md\",\n        children: \"Word Cloud\"\n      }), \". Tokens are sent to the server where each token is \", _jsx(_components.a, {\n        href: \"https://fasttext.cc/docs/en/python-module.html#model-object\",\n        children: \"vectorized\"\n      }), \" separately and then the aggregation function is used to compute the document embedding. The server returns the vector for each document. Currently, the server runs \", _jsx(_components.code, {\n        children: \"fasttext==0.9.1\"\n      }), \". For out-of-vocabulary (OOV) words, fastText obtain vectors by summing up vectors for its component character n-grams.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In first example, we will inspect how the widget works. 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+<p>E. Grave, P. Bojanowski, P. Gupta, A. Joulin, T. Mikolov. &quot;Learning Word Vectors for 157 Languages.&quot; <em>Proceedings of the International Conference on Language Resources and Evaluation</em>, 2018.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Document Embedding\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Embeds documents from input corpus into vector space by using pre-trained\\n\", _jsx(_components.a, {\n        href: \"https://fasttext.cc/docs/en/crawl-vectors.html\",\n        children: \"fastText\"\n      }), \" models described in E. Grave et al. (2018).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with new features appended.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Document Embedding\"\n      }), \" parses n-grams of each document in corpus, obtains embedding\\nfor each n-gram using pre-trained model for chosen language and obtains one vector for each document by aggregating n-gram embeddings using one of offered aggregators. Note that method will work on any n-grams but it will give best results if corpus is preprocessed such that n-grams are words (because model was trained to embed words).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Document-Embedding-stamped.png\",\n      alt: \"\",\n      width: \"304\",\n      height: \"266\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Widget parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Language: widget will use a model trained on documents in chosen language.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Aggregator: operation to perform on n-gram embeddings to aggregate them into a single document vector.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Cancel current execution.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Apply automatically\"\n        }), \" is checked, changes in parameters are sent automatically. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Duplicate Detection</h1>
 <p>Detect &amp; remove duplicates from a corpus.</p>
 <p><strong>Inputs</strong></p>
@@ -178,4 +178,4 @@ <h2>Example</h2>
 <p>This simple example uses <em>iris</em> data to find identical data instances. Load <em>iris</em> with the <strong>File</strong> widget and pass it to <strong>Distances</strong>. In <strong>Distances</strong>, use Euclidean distance for computing the distance matrix. Pass distances to <strong>Duplicate Detection</strong>.</p>
 <p>It looks like cluster C147 contain three duplicate entries. Let us select it in the widget and observe it in a <strong>Data Table</strong>. Remember to set the output to <em>Duplicates Cluster</em>. IThe three data instances are identical. To use the data set without duplicates, use the first output, <em>Corpus Without Duplicates</em>.</p>
 <p>The same procedure can be used also for corpora. Remember to use the <a href="bagofwords-widget.md">Bag of Words</a> between <a href="/widget-catalog/text-mining/corpus-widget">Corpus</a> and <strong>Distances</strong>.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Duplicate-Detection-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2434" height="1244" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Duplicate-Detection-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Duplicate Detection\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Detect \u0026 remove duplicates from a corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: A distance matrix.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus Without Duplicated: Corpus with duplicates removed.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Duplicates Cluster: Documents belonging to selected cluster.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with appended cluster labels.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Duplicate Detection\"\n      }), \" uses clustering to find duplicates in the corpus. It is great with the \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/twitter-widget\",\n        children: \"Twitter\"\n      }), \" widget for removing retweets and other similar documents.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To set the level of similarity, drag the line vertical line left or right in the visualization. The further left the line, the more similar the documents have to be in order to be considered duplicates. You can also set the threshold manually in the control area.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Duplicate-Detection-stamped.png\",\n      alt: \"\",\n      width: \"1724\",\n      height: \"718\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on unique and duplicate documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Linkage used for clustering (Single, Average, Complete, Weighted and Ward).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Distance threshold sets the similarity cutoff. The lower the value, the more similar the data instances have to be to belong to the same cluster. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Duplicate-Detection-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2434" height="1244" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Duplicate-Detection-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Duplicate Detection\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Detect \u0026 remove duplicates from a corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: A distance matrix.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus Without Duplicated: Corpus with duplicates removed.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Duplicates Cluster: Documents belonging to selected cluster.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with appended cluster labels.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Duplicate Detection\"\n      }), \" uses clustering to find duplicates in the corpus. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>The Guardian</h1>
 <p>Fetching data from <a href="http://open-platform.theguardian.com">The Guardian Open Platform</a>.</p>
 <p><strong>Inputs</strong></p>
@@ -186,4 +186,4 @@ <h2>Example</h2>
 <p><strong>Guardian</strong> can be used just like any other data retrieval widget in Orange, namely <a href="nytimes.md">NY Times</a>, <a href="wikipedia-widget.md">Wikipedia</a>, <a href="twitter-widget.md">Twitter</a> or <a href="/widget-catalog/text-mining/pubmed">PubMed</a>.</p>
 <p>We will retrieve 240 articles mentioning <em>slovenia</em> between september 2017 and september 2018. The text will include article headline and content. Upon pressing <em>Search</em>, the articles will be retrieved.</p>
 <p>We can observe the results in the <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a> widget.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Guardian-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3058" height="1278" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Guardian-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"The Guardian\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetching data from \", _jsx(_components.a, {\n        href: \"http://open-platform.theguardian.com\",\n        children: \"The Guardian Open Platform\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the Guardian newspaper.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Guardian\"\n      }), \" retrieves articles from the Guardian newspaper via their API. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Guardian-Example.png" data-gallery="true"><img alt="" loading="lazy" width="3058" height="1278" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Guardian-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"The Guardian\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetching data from \", _jsx(_components.a, {\n        href: \"http://open-platform.theguardian.com\",\n        children: \"The Guardian Open Platform\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the Guardian newspaper.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Guardian\"\n      }), \" retrieves articles from the Guardian newspaper via their API. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Import Documents</h1>
 <p>Import text documents from folders.</p>
 <p><strong>Inputs</strong></p>
@@ -181,4 +181,4 @@ <h2>Example</h2>
 <p>To retrieve the data, select the folder icon on the right side of the widget. Select the folder you wish to turn into corpus. Once the loading is finished, you will see how many documents the widget retrieved. To inspect them, connect the widget to <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a>. We&#x27;ve used a set of Kennedy&#x27;s speeches in a plain text format.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Import-Documents-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1290" height="634" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Import-Documents-Example1.png"/></a>
 <p>Now let us try it with subfolders. We have placed Kennedy&#x27;s speeches in two folders - pre-1962 and post-1962. If I load the parent folder, these two subfolders will be used as class labels. Check the output of the widget in a <strong>Data Table</strong>.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Import-Documents-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1208" height="576" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Import-Documents-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Import Documents\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Import text documents from folders.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the local machine.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Skipped Documents: A list of documents that couldn't be imported.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Import Documents\"\n      }), \" widget retrieves text files from folders and creates a corpus. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Extract Keywords</h1>
 <p>Infers characteristic words from the input corpus.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@ <h2>Example</h2>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2590" height="1552" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png"/></a>
 <h2>References</h2>
 <p>Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In <em>Information Sciences Journal</em>. Elsevier, Vol 509, pp 257-289</p>
-<p>Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010). Automatic Keyword Extraction from Individual Documents. In <em>Text Mining</em> (eds M.W. Berry and J. Kogan). <a href="https://doi.org/10.1002/9780470689646.ch1">https://doi.org/10.1002/9780470689646.ch1</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Extract Keywords\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Infers characteristic words from the input corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Extract Keywords\"\n      }), \" infers characteristic words from corpus.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Extract-Keywords.png\",\n      alt: \"\",\n      width: \"661\",\n      height: \"553\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Scoring methods for extracting words:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"TF-IDF, a method that scores by term frequency weighted by inverse document frequency. A word that is characteristic for a small number of documents, will have a high TF-IDF score, while words that appear in the entire corpus will have a low score.\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"http://yake.inesctec.pt/\",\n              children: \"YAKE!\"\n            }), \", an unsupervised state-of-the-art method that works with texts of different sizes.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"https://github.com/zelandiya/RAKE-tutorial\",\n              children: \"Rake\"\n            }), \", an unsupervised domain-independent method based around stopword delimiters.\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Embedding, a proprietary method that gives higher scores to words that have the closest cosine distance to most documents. The distance is computed on SBERT word embeddings.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the below example, we are using the \", _jsx(_components.em, {\n        children: \"book-excerpts\"\n      }), \" corpus, which is available in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We pass the corpus to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \", where we lowercase the text, split it into words with tokenization, use Lemmagen lemmatizer to cover tokens to their base form and finally remove stopwords.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, we find characteristic words with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/keywords\",\n        children: \"Extract Keywords\"\n      }), \" widget using the TF-IDF method. The widget returns a list of words and we can select the top-ranked words to send to the output.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can use these words in \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordlist\",\n        children: \"Word List\"\n      }), \", where we can edit them, add to them or remove them. Alternatively, we can send the candidate words directly to Semantic Viewer or Score Documents.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png\",\n      alt: \"\",\n      width: \"2590\",\n      height: \"1552\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In \", _jsx(_components.em, {\n        children: \"Information Sciences Journal\"\n      }), \". Elsevier, Vol 509, pp 257-289\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010). Automatic Keyword Extraction from Individual Documents. In \", _jsx(_components.em, {\n        children: \"Text Mining\"\n      }), \" (eds M.W. Berry and J. Kogan). \", _jsx(_components.a, {\n        href: \"https://doi.org/10.1002/9780470689646.ch1\",\n        children: \"https://doi.org/10.1002/9780470689646.ch1\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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+<p>Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010). Automatic Keyword Extraction from Individual Documents. In <em>Text Mining</em> (eds M.W. Berry and J. Kogan). <a href="https://doi.org/10.1002/9780470689646.ch1">https://doi.org/10.1002/9780470689646.ch1</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Extract Keywords\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Infers characteristic words from the input corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Extract Keywords\"\n      }), \" infers characteristic words from corpus.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Extract-Keywords.png\",\n      alt: \"\",\n      width: \"661\",\n      height: \"553\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Scoring methods for extracting words:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"TF-IDF, a method that scores by term frequency weighted by inverse document frequency. A word that is characteristic for a small number of documents, will have a high TF-IDF score, while words that appear in the entire corpus will have a low score.\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"http://yake.inesctec.pt/\",\n              children: \"YAKE!\"\n            }), \", an unsupervised state-of-the-art method that works with texts of different sizes.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"https://github.com/zelandiya/RAKE-tutorial\",\n              children: \"Rake\"\n            }), \", an unsupervised domain-independent method based around stopword delimiters.\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Embedding, a proprietary method that gives higher scores to words that have the closest cosine distance to most documents. The distance is computed on SBERT word embeddings.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the below example, we are using the \", _jsx(_components.em, {\n        children: \"book-excerpts\"\n      }), \" corpus, which is available in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We pass the corpus to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \", where we lowercase the text, split it into words with tokenization, use Lemmagen lemmatizer to cover tokens to their base form and finally remove stopwords.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, we find characteristic words with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/keywords\",\n        children: \"Extract Keywords\"\n      }), \" widget using the TF-IDF method. The widget returns a list of words and we can select the top-ranked words to send to the output.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can use these words in \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordlist\",\n        children: \"Word List\"\n      }), \", where we can edit them, add to them or remove them. Alternatively, we can send the candidate words directly to Semantic Viewer or Score Documents.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png\",\n      alt: \"\",\n      width: \"2590\",\n      height: \"1552\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>NY Times</h1>
 <p>Loads data from the New York Times&#x27; <a href="https://developer.nytimes.com/">Article Search API</a>.</p>
 <p><strong>Inputs</strong></p>
@@ -194,4 +194,4 @@ <h2>Example</h2>
 <p><strong>NYTimes</strong> is a data retrieving widget, similar to <a href="twitter-widget.md">Twitter</a> and <a href="wikipedia-widget.md">Wikipedia</a>. As it can retrieve geolocations, that is geographical locations the article mentions, it is great in combination with <a href="/widget-catalog/text-mining/docmap">Document Map</a> widget.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/NYTimes-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1426" height="1045" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/NYTimes-Example1.png"/></a>
 <p>First, let&#x27;s query <strong>NYTimes</strong> for all articles on Slovenia. We can retrieve the articles found and view the results in <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a>. The widget displays all the retrieved features, but includes on selected features as text mining features.</p>
-<p>Now, let&#x27;s inspect the distribution of geolocations from the articles mentioning Slovenia. We can do this with <a href="/widget-catalog/text-mining/docmap">Document Map</a>. Unsurprisingly, Croatia and Hungary appear the most often in articles on Slovenia (discounting Slovenia itself), with the rest of Europe being mentioned very often as well.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"NY Times\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Loads data from the New York Times' \", _jsx(_components.a, {\n        href: \"https://developer.nytimes.com/\",\n        children: \"Article Search API\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the New York Times newspaper.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"NYTimes\"\n      }), \" widget loads data from New York Times' Article Search API. 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+<p>Now, let&#x27;s inspect the distribution of geolocations from the articles mentioning Slovenia. We can do this with <a href="/widget-catalog/text-mining/docmap">Document Map</a>. Unsurprisingly, Croatia and Hungary appear the most often in articles on Slovenia (discounting Slovenia itself), with the rest of Europe being mentioned very often as well.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"NY Times\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Loads data from the New York Times' \", _jsx(_components.a, {\n        href: \"https://developer.nytimes.com/\",\n        children: \"Article Search API\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the New York Times newspaper.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"NYTimes\"\n      }), \" widget loads data from New York Times' Article Search API. 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The key is securely saved in your system keyring service (like Credential Vault, Keychain, KWallet, etc.) and won't be deleted when clearing widget settings.\"\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/text-mining/images/__optimized-images__/NYT-API.png\",\n          alt: \"\",\n          width: \"450\",\n          height: \"101\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Set query parameters:\"\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.em, {\n              children: \"Query\"\n            })\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Query time frame. The widget allows querying articles from September 18, 1851 onwards. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 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href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Ontology</h1>
 <p>Generate, edit, load and save ontologies.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Example</h2>
 to the pane on the right and click the Generate button. After the widget generates
 the ontology, we can manually perform some minor changes to make ontology even more
 meaningful.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Ontology-Example.png" data-gallery="true"><img alt="" loading="lazy" width="905" height="789" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Ontology-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    code: \"code\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Ontology\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generate, edit, load and save ontologies.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The ontology widget enables various operations with ontologies:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Generate ontology from words on the input\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Load existing ontology from a file or URL. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Ontology-Example.png" data-gallery="true"><img alt="" loading="lazy" width="905" height="789" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Ontology-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    code: \"code\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Ontology\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generate, edit, load and save ontologies.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The ontology widget enables various operations with ontologies:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Generate ontology from words on the input\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Load existing ontology from a file or URL. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Preprocess Text</h1>
 <p>Preprocesses corpus with selected methods.</p>
 <p><strong>Inputs</strong></p>
@@ -233,4 +233,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Preprocess-Text-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1529" height="861" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Preprocess-Text-Example1.png"/></a>
 <p>The second example is slightly more complex. We first acquired our data with <a href="/widget-catalog/text-mining/twitter-widget">Twitter</a> widget. We quired the internet for tweets from users @HillaryClinton and @realDonaldTrump and got their tweets from the past two weeks, 242 in total.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Preprocess-Text-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1649" height="1019" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Preprocess-Text-Example2.png"/></a>
-<p>In <strong>Preprocess Text</strong> there&#x27;s <em>Tweet</em> tokenization available, which retains hashtags, emojis, mentions and so on. However, this tokenizer doesn&#x27;t get rid of punctuation, thus we expanded the Regexp filtering with symbols that we wanted to get rid of. We ended up with word-only tokens, which we displayed in <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>. Then we created a schema for predicting author based on tweet content, which is explained in more details in the documentation for <a href="/widget-catalog/text-mining/twitter-widget">Twitter</a> widget.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    code: \"code\",\n    br: \"br\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Preprocess Text\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Preprocesses corpus with selected methods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Preprocessed corpus.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" splits your text into smaller units (tokens), filters them, runs \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Stemming\",\n        children: \"normalization\"\n      }), \" (stemming, lemmatization), creates \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/N-gram\",\n        children: \"n-grams\"\n      }), \" and tags tokens with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Part_of_speech\",\n        children: \"part-of-speech\"\n      }), \" labels. 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+<p>In <strong>Preprocess Text</strong> there&#x27;s <em>Tweet</em> tokenization available, which retains hashtags, emojis, mentions and so on. However, this tokenizer doesn&#x27;t get rid of punctuation, thus we expanded the Regexp filtering with symbols that we wanted to get rid of. We ended up with word-only tokens, which we displayed in <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>. Then we created a schema for predicting author based on tweet content, which is explained in more details in the documentation for <a href="/widget-catalog/text-mining/twitter-widget">Twitter</a> widget.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    code: \"code\",\n    br: \"br\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Preprocess Text\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Preprocesses corpus with selected methods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Preprocessed corpus.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" splits your text into smaller units (tokens), filters them, runs \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Stemming\",\n        children: \"normalization\"\n      }), \" (stemming, lemmatization), creates \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/N-gram\",\n        children: \"n-grams\"\n      }), \" and tags tokens with \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Part_of_speech\",\n        children: \"part-of-speech\"\n      }), \" labels. 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href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Pubmed</h1>
 <p>Fetch data from <a href="http://www.ncbi.nlm.nih.gov/pubmed">PubMed</a> journals.</p>
 <p><strong>Inputs</strong></p>
@@ -181,4 +181,4 @@
 <h2>Example</h2>
 <p><strong>PubMed</strong> can be used just like any other data widget. In this example we&#x27;ve queried the database for records on orchids. We retrieved 1000 records and kept only &#x27;abstract&#x27; in our meta features to limit the construction of tokens only to this feature.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Pubmed-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1371" height="711" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Pubmed-Example.png"/></a>
-<p>We used <a href="preprocesstext.md">Preprocess Text</a> to remove stopword and words shorter than 3 characters (regexp <code>\b\w{1,2}\b</code>). This will perhaps get rid of some important words denoting chemicals, so we need to be careful with what we filter out. For the sake of quick inspection we only retained longer words, which are displayed by frequency in <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pubmed\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetch data from \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/pubmed\",\n        children: \"PubMed\"\n      }), \" journals.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the PubMed online service.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/pubmed\",\n        children: \"PubMed\"\n      }), \" comprises more than 26 million citations for biomedical literature from MEDLINE, life science journals, and online books. 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+<p>We used <a href="preprocesstext.md">Preprocess Text</a> to remove stopword and words shorter than 3 characters (regexp <code>\b\w{1,2}\b</code>). This will perhaps get rid of some important words denoting chemicals, so we need to be careful with what we filter out. For the sake of quick inspection we only retained longer words, which are displayed by frequency in <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    code: \"code\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pubmed\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetch data from \", _jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/pubmed\",\n        children: \"PubMed\"\n      }), \" journals.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the PubMed online service.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"http://www.ncbi.nlm.nih.gov/pubmed\",\n        children: \"PubMed\"\n      }), \" comprises more than 26 million citations for biomedical literature from MEDLINE, life science journals, and online books. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpus-widget/">Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/importdocuments/">Import Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Score Documents</h1>
 <p>Scores documents based on word appearance.</p>
 <p><strong>Inputs</strong></p>
@@ -191,4 +191,4 @@ <h2>Example</h2>
 <p>We pass the corpus to <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a>, where we lowercase the text, split it into words with tokenization, use Lemmagen lemmatizer to cover tokens to their base form and finally remove stopwords.</p>
 <p>Next, we find characteristic words with <a href="keywords.md">Extract Keywords</a> widget and send these words to <a href="/widget-catalog/text-mining/wordlist">Word List</a>. There, we add some of our own words, such as princess, prince, king and queen.</p>
 <p>Finally, we pass the preprocess corpus from Preprocess Text to Score Documents and the word list from Word List widget. Score Documents scores each document based on how frequently the input words appear in it.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Score-Documents-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2462" height="1680" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Score-Documents-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Score Documents\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scores documents based on word appearance.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Words: A table with at least one column with words\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents with scores\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Score Document\"\n      }), \" assigns scores to each document based on words at the input. The score with the selected scoring method is calculated for each word and aggregated together with the selected aggregation function on the document level.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Score-Documents.png\",\n      alt: \"\",\n      width: \"803\",\n      height: \"518\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select one or more scoring methods for words:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Word frequency\"\n            }), \": The count of the frequency of a word in the text.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Word ratio\"\n            }), \": Indicate the appearance of the word in the document.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Similarity\"\n            }), \": The cosine similarity between document embedding and word embedding.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select aggregation function to aggregate word scores to document scores (mean, median, min or max).\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select documents:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"None: no documents are on the output\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"All: the entire corpus is on the output\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Manual: manually select the documents from the table\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Top documents: n top-scored documents are sent to the output\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Send Automatically\"\n        }), \", changes are communicated automatically. Alternatively press \", _jsx(_components.em, {\n          children: \"Send\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Filter documents based on the document title in the first column. Below is the table with the document titles in the first column and scores in other columns.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Note\"\n      }), \": Score Documents will apply preprocessing from the input Corpus to words before scoring.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Score Documents is used to find documents that are semantically similar to the input word list. In the example below, we are using the \", _jsx(_components.em, {\n        children: \"book-excerpts\"\n      }), \" corpus from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We pass the corpus to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \", where we lowercase the text, split it into words with tokenization, use Lemmagen lemmatizer to cover tokens to their base form and finally remove stopwords.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Next, we find characteristic words with \", _jsx(_components.a, {\n        href: \"keywords.md\",\n        children: \"Extract Keywords\"\n      }), \" widget and send these words to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordlist\",\n        children: \"Word List\"\n      }), \". There, we add some of our own words, such as princess, prince, king and queen.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Finally, we pass the preprocess corpus from Preprocess Text to Score Documents and the word list from Word List widget. 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Score-Documents-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2462" height="1680" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Score-Documents-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Score Documents\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scores documents based on word appearance.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Words: A table with at least one column with words\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents with scores\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Score Document\"\n      }), \" assigns scores to each document based on words at the input. 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Semantic Viewer</h1>
 <p>Displays corpus semantics.</p>
 <p><strong>Inputs</strong></p>
@@ -177,4 +177,4 @@ <h2>Example</h2>
 <p>We pass the corpus to <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a>, where we lowercase the text, split it into words with tokenization, use Lemmagen lemmatizer to cover tokens to their base form and finally remove stopwords.</p>
 <p>Next, we find characteristic words with <a href="keywords.md">Extract Keywords</a> widget and send these words to <a href="/widget-catalog/text-mining/wordlist">Word List</a>. There, we add some of our own words, such as princess, prince, king and queen.</p>
 <p>Finally, we pass the entire list of words to Semantic Viewer along with the corpus from Prepreprocess Text. The widget uses input word list to find matching passages in each document. We can now see parts of the text talking about princesses, queens, and so on.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2590" height="1552" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Semantic Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays corpus semantics.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matching Docs: Selected documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Other Docs: Other documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Semantic Viewer\"\n      }), \" is meant for viewing corpus semantics. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Sentiment Analysis</h1>
 <p>Predict sentiment from text.</p>
 <p><strong>Inputs</strong></p>
@@ -239,4 +239,4 @@ <h2>Multilingual Sentiment Languages</h2>
 <li>Swedish</li>
 <li>Turkish</li>
 <li>Ukrainian</li>
-</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Sentiment Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict sentiment from text.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A corpus with information on the sentiment of each document.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \" predicts sentiment for each document in a corpus. It uses Liu \u0026 Hu and Vader sentiment modules from \", _jsx(_components.a, {\n        href: \"http://www.nltk.org/api/nltk.sentiment.html\",\n        children: \"NLTK\"\n      }), \", multilingual sentiment lexicons from the \", _jsx(_components.a, {\n        href: \"https://sites.google.com/site/datascienceslab/projects/multilingualsentiment\",\n        children: \"Data Science Lab\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.frontiersin.org/articles/10.3389/frobt.2019.00053/full\",\n        children: \"SentiArt\"\n      }), \" from Arthur Jacobs, and \", _jsx(_components.a, {\n        href: \"http://hdl.handle.net/11356/1318\",\n        children: \"LiLaH sentiment\"\n      }), \" from Walter Daelemans et al. All of them are lexicon-based. For Liu \u0026 Hu, you can choose English or Slovenian version. Vader works only on English. Multilingual sentiment supports several languages, which are listed at the bottom of this page. 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Then we connect \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \". The widget will append 4 new features for Vader method: positive score, negative score, neutral score and compound (combined score).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can observe new features in a \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \", where we sorted the \", _jsx(_components.em, {\n        children: \"compound\"\n      }), \" by score. Compound represents the total sentiment of a tweet, where -1 is the most negative and 1 the most positive.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Sentiment-DataTable.png\",\n      alt: \"\",\n      width: \"891\",\n      height: \"444\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now let us visualize the data. We have some features we are currently not interested in, so we will remove them with \", _jsx(_components.strong, {\n        children: \"Select Columns\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Sentiment-SelectColumns.png\",\n      alt: \"\",\n      width: \"375\",\n      height: \"451\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we will make our corpus a little smaller, so it will be easier to visualize. Pass the data to \", _jsx(_components.strong, {\n        children: \"Data Sampler\"\n      }), \" and retain a random 10% of the tweets.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Sentiment-DataSampler.png\",\n      alt: \"\",\n      width: \"261\",\n      height: \"520\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now pass the filtered corpus to \", _jsx(_components.strong, {\n        children: \"Heat Map\"\n      }), \". Use \", _jsx(_components.em, {\n        children: \"Merge by k-means\"\n      }), \" to merge tweets with the same polarity into one line. Then use \", _jsx(_components.em, {\n        children: \"Cluster\"\n      }), \" by \", _jsx(_components.em, {\n        children: \"rows\"\n      }), \" to create a clustered visualization where similar tweets are grouped together. Click on a cluster to select a group of tweets - we selected the negative cluster.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Sentiment-HeatMap.png\",\n      alt: \"\",\n      width: \"527\",\n      height: \"672\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To observe the selected subset, pass the tweets to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Sentiment-CorpusViewer.png\",\n      alt: \"\",\n      width: \"800\",\n      height: \"571\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Sentiment-workflow.png\",\n      alt: \"\",\n      width: \"1323\",\n      height: \"739\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Hutto, C.J. and E. E. Gilbert (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Hu, Minqing and Bing Liu (2004). Mining opinion features in customer reviews. In Proceedings of AAAI Conference on Artificial Intelligence, vol. 4, pp. 755–760. \", _jsx(_components.a, {\n        href: \"http://www.aaai.org/Papers/AAAI/2004/AAAI04-119.pdf\",\n        children: \"Available online.\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Jacobs, Arthur M. (2019). Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics. Frontiers in Robotics and AI 6. \", _jsx(_components.a, {\n        href: \"https://doi.org/10.3389/frobt.2019.00053\",\n        children: \"https://doi.org/10.3389/frobt.2019.00053\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Kadunc, Klemen and Marko Robnik-Šikonja (2016). Analiza mnenj s pomočjo strojnega učenja in slovenskega leksikona sentimenta. Conference on Language Technologies \u0026 Digital Humanities, Ljubljana (in Slovene). \", _jsx(_components.a, {\n        href: \"http://www.sdjt.si/wp/wp-content/uploads/2016/09/JTDH-2016_Kadunc-et-al_Analiza-mnenj-s-pomocjo-strojnega-ucenja.pdf\",\n        children: \"Available online.\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Ljubešić, Nikola, Ilia Markov, Darja Fišer and Walter Daelemans (2020). The LiLaH Emotion Lexicon of Croatian, Dutch and Slovene. In Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media (PEOPLES 2020), pp. 153–157. \", _jsx(_components.a, {\n        href: \"https://aclanthology.org/2020.peoples-1.15\",\n        children: \"Available online.\"\n      })]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Multilingual Sentiment Languages\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Afrikaans\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Arabic\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Azerbaijani\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Belarusian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Bosnian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Bulgarian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Catalan\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Chinese\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Chinese Characters\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Croatian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Czech\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Danish\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Dutch\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"English\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Estonian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Farsi\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Finnish\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"French\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Gaelic\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"German\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Greek\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Hebrew\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Hindi\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Hungarian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Indonesian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Italian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Japanese\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Korean\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Latin\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Latvian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Lithuanian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Macedonian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Norwegian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Norwegian Nynorsk\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Polish\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Portuguese\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Romanian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Russian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Serbian\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Slovak\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Slovene\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Spanish\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Swedish\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Turkish\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Ukrainian\"\n      }), \"\\n\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, 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+</ul></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Sentiment Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Predict sentiment from text.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A corpus with information on the sentiment of each document.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \" predicts sentiment for each document in a corpus. It uses Liu \u0026 Hu and Vader sentiment modules from \", _jsx(_components.a, {\n        href: \"http://www.nltk.org/api/nltk.sentiment.html\",\n        children: \"NLTK\"\n      }), \", multilingual sentiment lexicons from the \", _jsx(_components.a, {\n        href: \"https://sites.google.com/site/datascienceslab/projects/multilingualsentiment\",\n        children: \"Data Science Lab\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://www.frontiersin.org/articles/10.3389/frobt.2019.00053/full\",\n        children: \"SentiArt\"\n      }), \" from Arthur Jacobs, and \", _jsx(_components.a, {\n        href: \"http://hdl.handle.net/11356/1318\",\n        children: \"LiLaH sentiment\"\n      }), \" from Walter Daelemans et al. All of them are lexicon-based. For Liu \u0026 Hu, you can choose English or Slovenian version. Vader works only on English. Multilingual sentiment supports several languages, which are listed at the bottom of this page. SentiArt works on English and German. LiLaH sentiment supports Slovenian, Croatian, and Dutch. Custom dictionary enables one to upload custom positive and negative sentiment dictionaries. Custom files should be plain text files (.txt) with each word in its own line.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/SentimentAnalysis-stamped.png\",\n      alt: \"\",\n      width: \"493\",\n      height: \"488\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Liu Hu\"\n        }), \": lexicon-based sentiment analysis (supports English and Slovenian). The final score is the difference between the sum of positive and sum of negative words, normalized by the length of the document and multiplied by a 100. The final score reflects the percentage of sentiment difference in the document.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Vader\"\n        }), \": lexicon- and rule-based sentiment analysis\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Multilingual sentiment\"\n        }), \": lexicon-based sentiment analysis for several languages\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"SentiArt\"\n        }), \": sentiment analysis based on vector space models returning text valence\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"LiLaH sentiment\"\n        }), \": manual translations of the NRC Emotion Lexicon\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Custom dictionary\"\n        }), \": add you own positive and negative sentiment dictionaries. 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Similarity Hashing</h1>
 <p>Computes documents hashes.</p>
 <p><strong>Inputs</strong></p>
@@ -172,4 +172,4 @@ <h2>Example</h2>
 <p>We will use <em>deerwester.tab</em> to find similar documents in this small corpus. Load the data with <a href="/widget-catalog/text-mining/corpus-widget">Corpus</a> and pass it to <strong>Similarity Hashing</strong>. We will keep the default hash size and shingle length. We can observe what the widget outputs in a <strong>Data Table</strong>. There are 64 new attributes available, corresponding to the <em>Simhash size</em> parameter.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Similarity-Hashing-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2656" height="1270" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Similarity-Hashing-Example.png"/></a>
 <h2>References</h2>
-<p>Charikar, M. (2002) Similarity estimation techniques from rounding algorithms. STOC &#x27;02 Proceedings of the thirty-fourth annual ACM symposium on Theory of computing, p. 380-388.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Similarity Hashing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Computes documents hashes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with simhash value as attributes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Similarity Hashing\"\n      }), \" is a widget that transforms documents into similarity vectors. 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+<p>Charikar, M. (2002) Similarity estimation techniques from rounding algorithms. STOC &#x27;02 Proceedings of the thirty-fourth annual ACM symposium on Theory of computing, p. 380-388.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Similarity Hashing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Computes documents hashes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with simhash value as attributes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Similarity Hashing\"\n      }), \" is a widget that transforms documents into similarity vectors. 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Statistics</h1>
 <p>Create new statistic variables for documents.</p>
 <p><strong>Inputs</strong></p>
@@ -194,4 +194,4 @@
 <h2>Example</h2>
 <p>Here is a simple example how <strong>Statistics</strong> widget works. As it is a basic feature construction widget, it can be used directly after <a href="corpus-widget.md">Corpus</a>. We have added a couple of features, namely word count, character count, percent unique words and number of words containing &#x27;oran&#x27;. We can observe the table with additional columns in a <a href="https://orange-visual-programming.readthedocs.io/widgets/data/datatable.html">Data Table</a>.</p>
 <p>We can also use the output of Statistics for predictive modeling with <a href="https://orange-visual-programming.readthedocs.io/widgets/evaluate/testandscore.html">Test and Score</a>. Normally, however, we would use Statistics only to enhance features from the <a href="bagofwords-widget.md">Bag of Words</a> widget. Some features require POS tagged tokens, which can be created with <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a> widget.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/statistics-example.png" data-gallery="true"><img alt="" loading="lazy" width="2650" height="1634" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/statistics-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Statistics\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Create new statistic variables for documents.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with additional attributes.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Statistics\"\n      }), \" is a feature constructor widget that adds simple document statistics to a corpus. 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kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" 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href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Topic Modelling</h1>
 <p>Topic modelling with Latent Dirichlet Allocation, Latent Semantic Indexing or Hierarchical Dirichlet Process.</p>
 <p><strong>Inputs</strong></p>
@@ -207,4 +207,4 @@ <h4>Topic Visualization</h4>
 <p>We can now explore which words are representative for the topic. Select, say, Topic 5 from the plot and connect MDS to <strong>Box Plot</strong>. Make sure the output is set to <em>Data</em> - <em>Data</em> (not <em>Selected Data</em> - <em>Data</em>).</p>
 <p>In Box Plot, set the subgroup to Selected and check the <em>Order by relevance to subgroups</em> box. This option will sort the variables by how well they separate between the selected subgroup values. In our case, this means which words are the most representative for the topic we have selected in the plot (subgroup Yes means selected).</p>
 <p>We can see that little, children and kings are the most representative words for Topic 5, with good separation between the word frequency for this topic and all the others. Select other topics in MDS and see how the Box Plot changes.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-BoxPlot.png" data-gallery="true"><img alt="" loading="lazy" width="3362" height="1684" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-BoxPlot.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Topic Modelling\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Topic modelling with Latent Dirichlet Allocation, Latent Semantic Indexing or Hierarchical Dirichlet Process.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with topic weights appended.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Topics: Selected topics with word weights.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"All Topics: Token weights per topic.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \" discovers abstract topics in a corpus based on clusters of words found in each document and their respective frequency. A document typically contains multiple topics in different proportions, thus the widget also reports on the topic weight per document.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget wraps gensim's topic models (\", _jsx(_components.a, {\n        href: \"https://radimrehurek.com/gensim/models/lsimodel.html\",\n        children: \"LSI\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://radimrehurek.com/gensim/models/ldamodel.html\",\n        children: \"LDA\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://radimrehurek.com/gensim/models/hdpmodel.html\",\n        children: \"HDP\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The first, LSI, can return both positive and negative words (words that are in a topic and those that aren't) and concurrently topic weights, that can be positive or negative. As stated by the main gensim's developer, Radim Řehůřek: \", _jsx(_components.em, {\n        children: \"\\\"LSI topics are not supposed to make sense; since LSI allows negative numbers, it boils down to delicate cancellations between topics and there's no straightforward way to interpret a topic.\\\"\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"LDA can be more easily interpreted, while HDP has many parameters - the parameter that corresponds to the number of topics is \", _jsx(_components.em, {\n        children: \"Top level truncation level (T)\"\n      }), \". The smallest number of topics that one can retrieve is 10.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-stamped.png\",\n      alt: \"\",\n      width: \"1618\",\n      height: \"594\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Topic modelling algorithm:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Latent_semantic_analysis\",\n              children: \"Latent Semantic Indexing\"\n            }), \". Returns both negative and positive words and topic weights.\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation\",\n              children: \"Latent Dirichlet Allocation\"\n            })\n          }), \"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Hierarchical_Dirichlet_process\",\n              children: \"Hierarchical Dirichlet Process\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Parameters for the algorithm. LSI and LDA accept only the number of topics modelled, with the default set to 10. HDP, however, has more parameters. As this algorithm is computationally very demanding, we recommend you to try it on a subset or set all the required parameters in advance and only then run the algorithm (connect the input to the widget).\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"First level concentration (γ): distribution at the first (corpus) level of Dirichlet Process\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Second level concentration (α): distribution at the second (document) level of Dirichlet Process\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"The topic Dirichlet (α): concentration parameter used for the topic draws\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Top level truncation (Τ): corpus-level truncation (no of topics)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Second level truncation (Κ): document-level truncation (no of topics)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Learning rate (κ): step size\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Slow down parameter (τ)\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Commit Automatically\"\n        }), \" is on, changes are communicated automatically. Alternatively press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget has three outputs. The first is a corpus, with added topic probabilities per each document. This can be used in a \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/visualize/heatmap/\",\n        children: \"Heat Map\"\n      }), \" to observe the distribution of topics across a corpus. The second output is the selected topic, which can be used in combination with a \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordcloud\",\n        children: \"Word Cloud\"\n      }), \" to observe significant words in a topic. The third output is a topic-term matrix, which shows probability distributions of words in topics. It can be used in combination with \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/unsupervised/mds/\",\n        children: \"MDS\"\n      }), \" to observe topic similarities.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Exploring Individual Topics\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example, we present a simple use of the \", _jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \" widget. First we load \", _jsx(_components.em, {\n        children: \"grimm-tales-selected.tab\"\n      }), \" data set and use \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \" to tokenize by words only and remove stopwords. Then we connect \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \", where we use a simple \", _jsx(_components.em, {\n        children: \"Latent Semantic Indexing\"\n      }), \" to find 10 topics in the text.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example1.png\",\n      alt: \"\",\n      width: \"3302\",\n      height: \"1730\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"LSI provides both positive and negative weights per topic. A positive weight means the word is highly representative of a topic, while a negative weight means the word is highly unrepresentative of a topic (the less it occurs in a text, the more likely the topic). Positive words are colored green and negative words are colored red.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We then select the first topic and display the most frequent words in the topic in \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordcloud\",\n        children: \"Word Cloud\"\n      }), \". We also connected \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Word Cloud\"\n      }), \" in order to be able to output selected documents. Now we can select a specific word in the word cloud, say \", _jsx(_components.em, {\n        children: \"little\"\n      }), \". It will be colored red and also highlighted in the word list on the left.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we can observe all the documents containing the word \", _jsx(_components.em, {\n        children: \"little\"\n      }), \" in \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Topic Visualization\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we will look at the correlation between topics and words/documents. We are still using the \", _jsx(_components.em, {\n        children: \"grimm-tales-selected.tab\"\n      }), \" corpus. In \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" we are using the default preprocessing, with an additional filter by \", _jsx(_components.em, {\n        children: \"document frequency\"\n      }), \" (0.1 - 0.9). In \", _jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \" we are using LDA model with 5 topics.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect Topic Modelling to \", _jsx(_components.strong, {\n        children: \"MDS\"\n      }), \". Ensure the link is set to \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" - \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \". Topic Modelling will output a matrix of word weights by topic.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In MDS, the points are now topics. We have set the size of the points to \", _jsx(_components.em, {\n        children: \"Marginal topic probability\"\n      }), \", which is an additional columns of \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" - it reports on the marginal probability of the topic in the corpus (how strongly represented is the topic in the corpus).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-MDS.png\",\n      alt: \"\",\n      width: \"735\",\n      height: \"498\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can now explore which words are representative for the topic. Select, say, Topic 5 from the plot and connect MDS to \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \". Make sure the output is set to \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \" - \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \" (not \", _jsx(_components.em, {\n        children: \"Selected Data\"\n      }), \" - \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In Box Plot, set the subgroup to Selected and check the \", _jsx(_components.em, {\n        children: \"Order by relevance to subgroups\"\n      }), \" box. This option will sort the variables by how well they separate between the selected subgroup values. In our case, this means which words are the most representative for the topic we have selected in the plot (subgroup Yes means selected).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that little, children and kings are the most representative words for Topic 5, with good separation between the word frequency for this topic and all the others. Select other topics in MDS and see how the Box Plot changes.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-BoxPlot.png\",\n      alt: \"\",\n      width: \"3362\",\n      height: \"1684\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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+<a href="/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-BoxPlot.png" data-gallery="true"><img alt="" loading="lazy" width="3362" height="1684" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-BoxPlot.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Topic Modelling\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Topic modelling with Latent Dirichlet Allocation, Latent Semantic Indexing or Hierarchical Dirichlet Process.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Corpus with topic weights appended.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Topics: Selected topics with word weights.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"All Topics: Token weights per topic.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \" discovers abstract topics in a corpus based on clusters of words found in each document and their respective frequency. A document typically contains multiple topics in different proportions, thus the widget also reports on the topic weight per document.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget wraps gensim's topic models (\", _jsx(_components.a, {\n        href: \"https://radimrehurek.com/gensim/models/lsimodel.html\",\n        children: \"LSI\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://radimrehurek.com/gensim/models/ldamodel.html\",\n        children: \"LDA\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://radimrehurek.com/gensim/models/hdpmodel.html\",\n        children: \"HDP\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The first, LSI, can return both positive and negative words (words that are in a topic and those that aren't) and concurrently topic weights, that can be positive or negative. As stated by the main gensim's developer, Radim Řehůřek: \", _jsx(_components.em, {\n        children: \"\\\"LSI topics are not supposed to make sense; since LSI allows negative numbers, it boils down to delicate cancellations between topics and there's no straightforward way to interpret a topic.\\\"\"\n      })]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"LDA can be more easily interpreted, while HDP has many parameters - the parameter that corresponds to the number of topics is \", _jsx(_components.em, {\n        children: \"Top level truncation level (T)\"\n      }), \". The smallest number of topics that one can retrieve is 10.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-stamped.png\",\n      alt: \"\",\n      width: \"1618\",\n      height: \"594\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Topic modelling algorithm:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Latent_semantic_analysis\",\n              children: \"Latent Semantic Indexing\"\n            }), \". Returns both negative and positive words and topic weights.\"]\n          }), \"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation\",\n              children: \"Latent Dirichlet Allocation\"\n            })\n          }), \"\\n\", _jsx(_components.li, {\n            children: _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Hierarchical_Dirichlet_process\",\n              children: \"Hierarchical Dirichlet Process\"\n            })\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Parameters for the algorithm. LSI and LDA accept only the number of topics modelled, with the default set to 10. HDP, however, has more parameters. As this algorithm is computationally very demanding, we recommend you to try it on a subset or set all the required parameters in advance and only then run the algorithm (connect the input to the widget).\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"First level concentration (γ): distribution at the first (corpus) level of Dirichlet Process\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Second level concentration (α): distribution at the second (document) level of Dirichlet Process\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"The topic Dirichlet (α): concentration parameter used for the topic draws\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Top level truncation (Τ): corpus-level truncation (no of topics)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Second level truncation (Κ): document-level truncation (no of topics)\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Learning rate (κ): step size\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Slow down parameter (τ)\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Commit Automatically\"\n        }), \" is on, changes are communicated automatically. Alternatively press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget has three outputs. The first is a corpus, with added topic probabilities per each document. This can be used in a \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/visualize/heatmap/\",\n        children: \"Heat Map\"\n      }), \" to observe the distribution of topics across a corpus. The second output is the selected topic, which can be used in combination with a \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordcloud\",\n        children: \"Word Cloud\"\n      }), \" to observe significant words in a topic. The third output is a topic-term matrix, which shows probability distributions of words in topics. 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Then we connect \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \", where we use a simple \", _jsx(_components.em, {\n        children: \"Latent Semantic Indexing\"\n      }), \" to find 10 topics in the text.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example1.png\",\n      alt: \"\",\n      width: \"3302\",\n      height: \"1730\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"LSI provides both positive and negative weights per topic. A positive weight means the word is highly representative of a topic, while a negative weight means the word is highly unrepresentative of a topic (the less it occurs in a text, the more likely the topic). Positive words are colored green and negative words are colored red.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We then select the first topic and display the most frequent words in the topic in \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordcloud\",\n        children: \"Word Cloud\"\n      }), \". We also connected \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" to \", _jsx(_components.strong, {\n        children: \"Word Cloud\"\n      }), \" in order to be able to output selected documents. Now we can select a specific word in the word cloud, say \", _jsx(_components.em, {\n        children: \"little\"\n      }), \". It will be colored red and also highlighted in the word list on the left.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Now we can observe all the documents containing the word \", _jsx(_components.em, {\n        children: \"little\"\n      }), \" in \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpusviewer\",\n        children: \"Corpus Viewer\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Topic Visualization\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we will look at the correlation between topics and words/documents. We are still using the \", _jsx(_components.em, {\n        children: \"grimm-tales-selected.tab\"\n      }), \" corpus. In \", _jsx(_components.strong, {\n        children: \"Preprocess Text\"\n      }), \" we are using the default preprocessing, with an additional filter by \", _jsx(_components.em, {\n        children: \"document frequency\"\n      }), \" (0.1 - 0.9). In \", _jsx(_components.strong, {\n        children: \"Topic Modelling\"\n      }), \" we are using LDA model with 5 topics.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Connect Topic Modelling to \", _jsx(_components.strong, {\n        children: \"MDS\"\n      }), \". Ensure the link is set to \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" - \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \". Topic Modelling will output a matrix of word weights by topic.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In MDS, the points are now topics. We have set the size of the points to \", _jsx(_components.em, {\n        children: \"Marginal topic probability\"\n      }), \", which is an additional columns of \", _jsx(_components.em, {\n        children: \"All Topics\"\n      }), \" - it reports on the marginal probability of the topic in the corpus (how strongly represented is the topic in the corpus).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-MDS.png\",\n      alt: \"\",\n      width: \"735\",\n      height: \"498\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We can now explore which words are representative for the topic. Select, say, Topic 5 from the plot and connect MDS to \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \". Make sure the output is set to \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \" - \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \" (not \", _jsx(_components.em, {\n        children: \"Selected Data\"\n      }), \" - \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \").\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In Box Plot, set the subgroup to Selected and check the \", _jsx(_components.em, {\n        children: \"Order by relevance to subgroups\"\n      }), \" box. This option will sort the variables by how well they separate between the selected subgroup values. In our case, this means which words are the most representative for the topic we have selected in the plot (subgroup Yes means selected).\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We can see that little, children and kings are the most representative words for Topic 5, with good separation between the word frequency for this topic and all the others. Select other topics in MDS and see how the Box Plot changes.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Topic-Modelling-Example2-BoxPlot.png\",\n      alt: \"\",\n      width: \"3362\",\n      height: \"1684\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Tweet Profiler</h1>
 <p>Detect Ekman&#x27;s, Plutchik&#x27;s or Profile of Mood States&#x27; emotions in tweets.</p>
 <p><strong>Inputs</strong></p>
@@ -178,4 +178,4 @@ <h2>Example</h2>
 <p>We will use <em>election-tweets-2016.tab</em> for this example. Load the data with <a href="/widget-catalog/text-mining/corpus-widget">Corpus</a> and connect it to <strong>Tweet Profiler</strong>. We will use <em>Content</em> attribute for the analysis, Ekman&#x27;s classification of emotion with multi-class option and we will output the result as class. We will observe the results in a <strong>Box Plot</strong>. In the widget, we have selected to observe the <em>Emotion</em> variable, grouped by <em>Author</em>. This way we can see which emotion prevails by which author.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Tweet-Profiler-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2494" height="1330" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Tweet-Profiler-Example.png"/></a>
 <h2>References</h2>
-<p>Colnerič, Niko and Janez Demšar (2018). Emotion Recognition on Twitter: Comparative Study and Training a Unison Model. In IEEE Transactions on Affective Computing. <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=8295234">Available online.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tweet Profiler\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Detect Ekman's, Plutchik's or Profile of Mood States' emotions in tweets.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of tweets (or other documents).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A corpus with information on the sentiment of each document.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Tweet Profiler\"\n      }), \" retrieves information on sentiment from the server for each given tweet (or document). 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+<p>Colnerič, Niko and Janez Demšar (2018). Emotion Recognition on Twitter: Comparative Study and Training a Unison Model. In IEEE Transactions on Affective Computing. <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=8295234">Available online.</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tweet Profiler\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Detect Ekman's, Plutchik's or Profile of Mood States' emotions in tweets.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of tweets (or other documents).\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A corpus with information on the sentiment of each document.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Tweet Profiler\"\n      }), \" retrieves information on sentiment from the server for each given tweet (or document). 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Multi-class will output one most probable emotion per document, while multi-label will output values in columns per each emotion.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"The widget can output classes of emotion (categorical), probabilities (numeric), or embeddings (an emotional vector of the document).\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Commit Automatically\"\n        }), \" automatically outputs the result. Alternatively, press \", _jsx(_components.em, {\n          children: \"Commit\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use \", _jsx(_components.em, {\n        children: \"election-tweets-2016.tab\"\n      }), \" for this example. Load the data with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/corpus-widget\",\n        children: \"Corpus\"\n      }), \" and connect it to \", _jsx(_components.strong, {\n        children: \"Tweet Profiler\"\n      }), \". We will use \", _jsx(_components.em, {\n        children: \"Content\"\n      }), \" attribute for the analysis, Ekman's classification of emotion with multi-class option and we will output the result as class. We will observe the results in a \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \". In the widget, we have selected to observe the \", _jsx(_components.em, {\n        children: \"Emotion\"\n      }), \" variable, grouped by \", _jsx(_components.em, {\n        children: \"Author\"\n      }), \". This way we can see which emotion prevails by which author.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Tweet-Profiler-Example.png\",\n      alt: \"\",\n      width: \"2494\",\n      height: \"1330\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Colnerič, Niko and Janez Demšar (2018). Emotion Recognition on Twitter: Comparative Study and Training a Unison Model. In IEEE Transactions on Affective Computing. \", _jsx(_components.a, {\n        href: \"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8295234\",\n        children: \"Available online.\"\n      })]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Twitter</h1>
 <p>Fetching data from <a href="https://dev.twitter.com/rest/public/search">The Twitter Search API</a>.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Twitter-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1593" height="928" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Twitter-Example2.png"/></a>
 <p>Then we&#x27;ve used <a href="preprocesstext.md">Preprocess Text</a> to get suitable tokens on our output. We&#x27;ve connected <strong>Preprocess Text</strong> to <a href="/widget-catalog/text-mining/bagofwords-widget">Bag of Words</a> in order to create a table with words as features and their counts as values. A quick check in <strong>Word Cloud</strong> gives us an idea about the results.</p>
 <p>Now we would like to predict the author of the tweet. With <strong>Select Columns</strong> we&#x27;re setting &#x27;Author&#x27; as our target variable. Then we connect <strong>Select Columns</strong> to <strong>Test &amp; Score</strong>. We&#x27;ll be using <strong>Logistic Regression</strong> as our learner, which we also connect to <strong>Test &amp; Score</strong>.</p>
-<p>We can observe the results of our author predictions directly in the widget. AUC score is quite ok. Seems like we can to some extent predict who is the author of the tweet based on the tweet content.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Twitter\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetching data from \", _jsx(_components.a, {\n        href: \"https://dev.twitter.com/rest/public/search\",\n        children: \"The Twitter Search API\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of tweets from the Twitter API.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Twitter\"\n      }), \" widget enables querying tweets through Twitter API. You can query by content, author or both and  accumulate results should you wish to create a larger data set. The widget only supports REST API and allows queries for up to two weeks back.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Note\"\n      }), \": After the \", _jsx(_components.a, {\n        href: \"https://twittercommunity.com/t/announcing-new-access-tiers-for-the-twitter-api/188728\",\n        children: \"API plans changed on March 30, 2023\"\n      }), \", Twitter no longer supports free Tweet retrieval with free access. At least Basic access is required.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Twitter-stamped.png\",\n      alt: \"\",\n      width: \"401\",\n      height: \"496\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"To begin your queries, insert Twitter key and secret. They are securely saved in your system keyring service (like Credential Vault, Keychain, KWallet, etc.) and won't be deleted when clearing widget settings. You must first create a \", _jsx(_components.a, {\n            href: \"https://apps.twitter.com/\",\n            children: \"Twitter app\"\n          }), \" to get API keys.\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/text-mining/images/__optimized-images__/Twitter-key.png\",\n          alt: \"\",\n          width: \"463\",\n          height: \"125\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Set query parameters:\"\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Query word list\"\n            }), \": list desired queries, one per line. Queries are automatically joined by OR.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Search by\"\n            }), \": specify whether you want to search by content, author or both. If searching by author, you must enter proper Twitter handle (without @) in the query list.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Language\"\n            }), \": set the language of retrieved tweets. Any will retrieve tweets in any language.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Max tweets\"\n            }), \": set the top limit of retrieved tweets. If box is not ticked, no upper bound will be set - widget will retrieve all available tweets.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Allow retweets\"\n            }), \": if 'Allow retweets' is checked, retweeted tweets will also appear on the output. This might duplicate some results.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Collect results\"\n            }), \": if 'Collect results' is ticked, widget will append new queries to the previous ones. Enter new queries, run \", _jsx(_components.em, {\n              children: \"Search\"\n            }), \" and new results will be appended to the previous ones.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Define which features to include as text features.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Information on the number of tweets on the output.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Run query.\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"First, let's try a simple query. 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+<p>We can observe the results of our author predictions directly in the widget. AUC score is quite ok. Seems like we can to some extent predict who is the author of the tweet based on the tweet content.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Twitter\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetching data from \", _jsx(_components.a, {\n        href: \"https://dev.twitter.com/rest/public/search\",\n        children: \"The Twitter Search API\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of tweets from the Twitter API.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Twitter\"\n      }), \" widget enables querying tweets through Twitter API. 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At least Basic access is required.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Twitter-stamped.png\",\n      alt: \"\",\n      width: \"401\",\n      height: \"496\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"To begin your queries, insert Twitter key and secret. They are securely saved in your system keyring service (like Credential Vault, Keychain, KWallet, etc.) and won't be deleted when clearing widget settings. 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If box is not ticked, no upper bound will be set - widget will retrieve all available tweets.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Allow retweets\"\n            }), \": if 'Allow retweets' is checked, retweeted tweets will also appear on the output. This might duplicate some results.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Collect results\"\n            }), \": if 'Collect results' is ticked, widget will append new queries to the previous ones. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Wikipedia</h1>
 <p>Fetching data from <a href="https://www.mediawiki.org/wiki/API:Tutorial">MediaWiki RESTful web service API</a>.</p>
 <p><strong>Inputs</strong></p>
@@ -180,4 +180,4 @@
 <h2>Example</h2>
 <p>This is a simple example, where we use <strong>Wikipedia</strong> and retrieve the articles on &#x27;Slovenia&#x27; and &#x27;Germany&#x27;. Then we simply apply default preprocessing with <a href="preprocesstext.md">Preprocess Text</a> and observe the most frequent words in those articles with <a href="/widget-catalog/text-mining/wordcloud">Word Cloud</a>.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Wikipedia-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1357" height="703" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Wikipedia-Example.png"/></a>
-<p>Wikipedia works just like any other corpus widget (<a href="nytimes.md">NY Times</a>, <a href="/widget-catalog/text-mining/twitter-widget">Twitter</a>) and can be used accordingly.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Wikipedia\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetching data from \", _jsx(_components.a, {\n        href: \"https://www.mediawiki.org/wiki/API:Tutorial\",\n        children: \"MediaWiki RESTful web service API\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the Wikipedia.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Wikipedia\"\n      }), \" widget is used to retrieve texts from Wikipedia API and it is useful mostly for teaching and demonstration.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Wikipedia-stamped.png\",\n      alt: \"\",\n      width: \"382\",\n      height: \"487\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Query parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Query word list, where each query is listed in a new line.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Language of the query. 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\ No newline at end of file
+<p>Wikipedia works just like any other corpus widget (<a href="nytimes.md">NY Times</a>, <a href="/widget-catalog/text-mining/twitter-widget">Twitter</a>) and can be used accordingly.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Wikipedia\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Fetching data from \", _jsx(_components.a, {\n        href: \"https://www.mediawiki.org/wiki/API:Tutorial\",\n        children: \"MediaWiki RESTful web service API\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents from the Wikipedia.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Wikipedia\"\n      }), \" widget is used to retrieve texts from Wikipedia API and it is useful mostly for teaching and demonstration.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Wikipedia-stamped.png\",\n      alt: \"\",\n      width: \"382\",\n      height: \"487\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Query parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Query word list, where each query is listed in a new line.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Language of the query. English is set by default.\"\n          }), \"\\n\", _jsx(_components.li, {\n            children: \"Number of articles to retrieve per query (range 1-25). Please note that querying is done recursively and that disambiguations are also retrieved, sometimes resulting in a larger number of queries than set on the slider.\"\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select which features to include as text features.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on the output.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Run query.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a simple example, where we use \", _jsx(_components.strong, {\n        children: \"Wikipedia\"\n      }), \" and retrieve the articles on 'Slovenia' and 'Germany'. Then we simply apply default preprocessing with \", _jsx(_components.a, {\n        href: \"preprocesstext.md\",\n        children: \"Preprocess Text\"\n      }), \" and observe the most frequent words in those articles with \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/wordcloud\",\n        children: \"Word Cloud\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Wikipedia-Example.png\",\n      alt: \"\",\n      width: \"1357\",\n      height: \"703\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Wikipedia works just like any other corpus widget (\", _jsx(_components.a, {\n        href: \"nytimes.md\",\n        children: \"NY Times\"\n      }), \", \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/twitter-widget\",\n        children: \"Twitter\"\n      }), \") and can be used accordingly.\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n 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href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Word Cloud</h1>
 <p>Generates a word cloud from corpus.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@ <h2>Example</h2>
 <p><strong>Word Cloud</strong> is an excellent widget for displaying the current state of the corpus and for monitoring the effects of preprocessing.</p>
 <p>Use <a href="corpus-widget.md">Corpus</a> to load the data. Connect <a href="/widget-catalog/text-mining/preprocesstext">Preprocess Text</a> to it and set your parameters. We&#x27;ve used defaults here, just to see the difference between the default preprocessing in the <strong>Word Cloud</strong> widget and the <strong>Preprocess Text</strong> widget.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Word-Cloud-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1558" height="913" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Word-Cloud-Example.png"/></a>
-<p>We can see from the two widgets, that <strong>Preprocess Text</strong> displays only words, while default preprocessing in the <strong>Word Cloud</strong> tokenizes by word and punctuation.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Word Cloud\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generates a word cloud from corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Topic: Selected topic.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Documents that match the selection.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Selected Word: Selected word that can be used as query in \", _jsx(_components.a, {\n          href: \"/widget-catalog/text-mining/concordance\",\n          children: \"Concordance\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Word Counts: Words and their weights.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Word Cloud\"\n      }), \" displays tokens in the corpus, their size denoting the frequency of the word in corpus or an average bag of words count, when bag of words features are at the input of the widget. 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+<p>We can see from the two widgets, that <strong>Preprocess Text</strong> displays only words, while default preprocessing in the <strong>Word Cloud</strong> tokenizes by word and punctuation.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Word Cloud\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generates a word cloud from corpus.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Topic: Selected topic.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: Documents that match the selection.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Selected Word: Selected word that can be used as query in \", _jsx(_components.a, {\n          href: \"/widget-catalog/text-mining/concordance\",\n          children: \"Concordance\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Word Counts: Words and their weights.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Word Cloud\"\n      }), \" displays tokens in the corpus, their size denoting the frequency of the word in corpus or an average bag of words count, when bag of words features are at the input of the widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Word Enrichment</h1>
 <p>Word enrichment analysis for selected documents.</p>
 <p><strong>Inputs</strong></p>
@@ -184,4 +184,4 @@ <h2>Example</h2>
 <p>In the example below, we&#x27;re retrieved recent tweets from the 2016 presidential candidates, Donald Trump and Hillary Clinton. Then we&#x27;ve preprocessed the tweets to get only words as tokens and to remove the stopwords. We&#x27;ve connected the preprocessed corpus to <a href="/widget-catalog/text-mining/bagofwords-widget">Bag of Words</a> to get a table with word counts for our corpus.</p>
 <a href="/widget-catalog/text-mining/images/__optimized-images__/Word-Enrichment-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1248" height="939" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Word-Enrichment-Example.png"/></a>
 <p>Then we&#x27;ve connected <a href="/widget-catalog/text-mining/corpusviewer">Corpus Viewer</a> to <strong>Bag of Words</strong> and selected only those tweets that were published by Donald Trump. See how we marked only the <em>Author</em> as our <em>Search feature</em> to retrieve those tweets.</p>
-<p><strong>Word Enrichment</strong> accepts two inputs - the entire corpus to serve as a reference and a selected subset from the corpus to do the enrichment on. First connect <strong>Corpus Viewer</strong> to <strong>Word Enrichment</strong> (input Matching Docs → Selected Data) and then connect <strong>Bag of Words</strong> to it (input Corpus → Data). In the <strong>Word Enrichment</strong> widget we can see the list of words that are more significant for Donald Trump than they are for Hillary Clinton.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Word Enrichment\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Word enrichment analysis for selected documents.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: Selected instances from corpus.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Word Enrichment\"\n      }), \" displays a list of words with lower p-values (higher significance) for a selected subset compared to the entire corpus. 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+<p><strong>Word Enrichment</strong> accepts two inputs - the entire corpus to serve as a reference and a selected subset from the corpus to do the enrichment on. First connect <strong>Corpus Viewer</strong> to <strong>Word Enrichment</strong> (input Matching Docs → Selected Data) and then connect <strong>Bag of Words</strong> to it (input Corpus → Data). In the <strong>Word Enrichment</strong> widget we can see the list of words that are more significant for Donald Trump than they are for Hillary Clinton.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Word Enrichment\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Word enrichment analysis for selected documents.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Corpus: A collection of documents.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: Selected instances from corpus.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"None\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Word Enrichment\"\n      }), \" displays a list of words with lower p-values (higher significance) for a selected subset compared to the entire corpus. 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Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/createcorpus/">Create Corpus</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/guardian-widget/">The Guardian</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/nytimes/">NY Times</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/pubmed/">Pubmed</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/twitter-widget/">Twitter</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wikipedia-widget/">Wikipedia</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/preprocesstext/">Preprocess Text</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpustonetwork/">Corpus to Network</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/bagofwords-widget/">Bag of Words</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/documentembedding/">Document Embedding</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/similarityhashing/">Similarity Hashing</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/sentimentanalysis/">Sentiment Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/tweetprofiler/">Tweet Profiler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/topicmodelling-widget/">Topic Modelling</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/LDAvis/">LDAvis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/corpusviewer/">Corpus Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/score-documents/">Score Documents</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordcloud/">Word Cloud</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/concordance/">Concordance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/docmap/">Document Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/wordenrichment/">Word Enrichment</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/duplicatedetection/">Duplicate Detection</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/text-mining/wordlist/">Word List</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/keywords/">Extract Keywords</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/annotator/">Annotated Corpus Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/ontology/">Ontology</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/semanticviewer/">Semantic Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/collocations/">Collocations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/text-mining/statistics/">Statistics</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Word List</h1>
 <p>Create a list of words.</p>
 <p><strong>Inputs</strong></p>
@@ -181,4 +181,4 @@ <h2>Example</h2>
 <p>In the Word List, we have previously defined some words that we would like to find in the text, namely princess, prince, king, queen. We have used <em>Union</em> to keep both the list we have manually defined and the one we have input from the Extract Keywords.</p>
 <p>Finally, we send the entire word list to <a href="/widget-catalog/text-mining/semanticviewer">Semantic Viewer</a> and add the <em>Corpus</em> output from Preprocess Text as well. Semantic Viewer now scores documents based on the input word list. The higher the score, the more matches the document has.</p>
 <p>This is a nice way to find a content of interest (say princes and princesses) in a collection of texts.</p>
-<a href="/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2590" height="1552" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/text-mining/images/__optimized-images__/Semantic-Viewer-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Word List\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Create a list of words.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Words: Words selected from the table.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Words: A table of words.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Word List\"\n      }), \" is meant for creating and joining lists of words for semantic analysis. The user can manually enter words into the widget or import them from other widgets, for example \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/keywords\",\n        children: \"Extract Keywords\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/text-mining/images/__optimized-images__/Word-List-Union.png\",\n      alt: \"\",\n      width: \"584\",\n      height: \"553\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Library of existing word lists. Add a new list with a \\\"+\\\" or remove it with \\\"-\\\". Use \\\"Update\\\" to save the current list from the right as a word list in the widget. With \\\"More\\\" you can load an existing list in a .txt format using the \", _jsx(_components.em, {\n          children: \"Import Words from File\"\n        }), \" option or save the list locally using the \", _jsx(_components.em, {\n          children: \"Save Words to File\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Input options:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"Word variable: set which string variable to use as a list of words.\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Update: define how to use an existing list and the input Words. \", _jsx(_components.em, {\n              children: \"Intersection\"\n            }), \" will use only overlapping words, \", _jsx(_components.em, {\n              children: \"Union\"\n            }), \" will use all the words, \", _jsx(_components.em, {\n              children: \"Only input\"\n            }), \" will ignore the list from the widget and use only the input list and \", _jsx(_components.em, {\n              children: \"Ignore input\"\n            }), \" will use only the list from the widget.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Use \", _jsx(_components.em, {\n          children: \"Filter\"\n        }), \" to find a word from the list. 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Use \\\"+\\\" to add a new word and \\\"-\\\" to remove it from the list. \", _jsx(_components.em, {\n          children: \"Sort\"\n        }), \" will sort the list alphabetically.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In this example we are using the pre-loaded \", _jsx(_components.em, {\n        children: \"book-excerpts\"\n      }), \" corpus from the \", _jsx(_components.a, {\n        href: \"corpus-widget.md\",\n        children: \"Corpus\"\n      }), \" widget. \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/preprocesstext\",\n        children: \"Preprocess Text\"\n      }), \" creates tokens by transforming the text to lowercase, splitting it into words, normalizing the words with Lemmagen lemmatizer and finally removing stopwords.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then we pass the preprocessed data to \", _jsx(_components.a, {\n        href: \"/widget-catalog/text-mining/keywords\",\n        children: \"Extract Keywords\"\n      }), \", a widget that finds characteristic words in the corpus. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/yahoo_finance/">Yahoo Finance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/as_timeseries/">As Timeseries</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/moving_transform_w/">Moving Transform</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/line_chart/">Line Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/periodogram_w/">Periodogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/correlogram/">Correlogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/spiralogram/">Spiralogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/granger_causality/">Granger Causality</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/time-series/arima/">ARIMA Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/var/">VAR Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/model_evaluation_w/">Model Evaluation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/time_slice/">Time Slice</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/difference/">Difference</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/seasonal_adjustment/">Seasonal Adjustment</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>ARIMA Model</h1>
 <p>Model the time series using <a href="https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model">ARMA</a>, ARIMA, or ARIMAX model.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@
 <h2>Example</h2>
 <a href="/widget-catalog/time-series/images/__optimized-images__/arima-model-ex1.png" data-gallery="true"><img alt="" loading="lazy" width="768" height="597" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/arima-model-ex1.png"/></a>
 <h4>See also</h4>
-<p><a href="var.md">VAR Model</a>, <a href="/widget-catalog/time-series/model_evaluation_w">Model Evaluation</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"ARIMA Model\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Model the time series using \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model\",\n        children: \"ARMA\"\n      }), \", ARIMA, or ARIMAX model.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Exogenous data: Time series of additional independent variables that can be used in an ARIMAX model.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series model: The ARIMA model fitted to input time series.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Forecast: The forecast time series.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Fitted values: The values that the model was actually fitted to, equals to \", _jsx(_components.em, {\n          children: \"original values - residuals\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residuals: The errors the model made at each step.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using this widget, you can model the time series with ARIMA model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/arima-model-stamped.png\",\n      alt: \"\",\n      width: \"276\",\n      height: \"357\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model's name. 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+<p><a href="var.md">VAR Model</a>, <a href="/widget-catalog/time-series/model_evaluation_w">Model Evaluation</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"ARIMA Model\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Model the time series using \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model\",\n        children: \"ARMA\"\n      }), \", ARIMA, or ARIMAX model.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Exogenous data: Time series of additional independent variables that can be used in an ARIMAX model.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series model: The ARIMA model fitted to input time series.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Forecast: The forecast time series.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Fitted values: The values that the model was actually fitted to, equals to \", _jsx(_components.em, {\n          children: \"original values - residuals\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residuals: The errors the model made at each step.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using this widget, you can model the time series with ARIMA model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/arima-model-stamped.png\",\n      alt: \"\",\n      width: \"276\",\n      height: \"357\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model's name. 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 <p>Reinterpret a <strong>Table</strong> object as a Timeseries object.</p>
 <p><strong>Inputs</strong></p>
@@ -170,4 +170,4 @@
 </ol>
 <h2>Example</h2>
 <p>The input to this widget comes from any data-emitting widget, e.g. the <strong>File</strong> widget. Note, whenever you do some processing with Orange core widgets, like the <strong>Select Columns</strong> widget, you need to re-apply the conversion into time series.</p>
-<a href="/widget-catalog/time-series/images/__optimized-images__/as-timeseries-ex1.png" data-gallery="true"><img alt="" loading="lazy" width="379" height="94" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/as-timeseries-ex1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"As Timeseries\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Reinterpret a \", _jsx(_components.strong, {\n        children: \"Table\"\n      }), \" object as a Timeseries object.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: Any data table.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: Data table reinterpreted as time series.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget reinterprets any data table as a time series, so it can be used with the rest of the widgets in this add-on. 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 <p>Visualize variables&#x27; auto-correlation.</p>
 <p><strong>Inputs</strong></p>
@@ -168,4 +168,4 @@ <h2>Example</h2>
 <p>Here is a simple example on how to use the Periodogram widget. We have passed the <a href="/widget-catalog/time-series/yahoo_finance">Yahoo Finance</a> data to the widget and plotted the autocorrelation of Amazon stocks for the past 6 years.</p>
 <a href="/widget-catalog/time-series/images/__optimized-images__/Correlogram-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1652" height="1204" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/Correlogram-Example.png"/></a>
 <h4>See also</h4>
-<p><a href="/widget-catalog/time-series/periodogram_w">Periodogram</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Correlogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize variables' auto-correlation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this widget, you can visualize the autocorrelation coefficients for the selected time series.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/correlogram.png\",\n      alt: \"\",\n      width: \"898\",\n      height: \"503\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the series to calculate autocorrelation for.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Choose to calculate the coefficients using partial autocorrelation function (PACF) instead. 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+<p><a href="/widget-catalog/time-series/periodogram_w">Periodogram</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Correlogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize variables' auto-correlation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this widget, you can visualize the autocorrelation coefficients for the selected time series.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/correlogram.png\",\n      alt: \"\",\n      width: \"898\",\n      height: \"503\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the series to calculate autocorrelation for.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Choose to calculate the coefficients using partial autocorrelation function (PACF) instead. Choose to plot the 95% significance interval (dotted horizontal line). Coefficients that are outside of this interval might be significant.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is a simple example on how to use the Periodogram widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/yahoo_finance/">Yahoo Finance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/as_timeseries/">As Timeseries</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/moving_transform_w/">Moving Transform</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/line_chart/">Line Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/periodogram_w/">Periodogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/correlogram/">Correlogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/spiralogram/">Spiralogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/granger_causality/">Granger Causality</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/arima/">ARIMA Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/var/">VAR Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/model_evaluation_w/">Model Evaluation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/time_slice/">Time Slice</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/time-series/difference/">Difference</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/seasonal_adjustment/">Seasonal Adjustment</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Difference</h1>
 <p>Make the time series stationary by replacing it with 1st or 2nd order discrete difference along its values.</p>
 <p><strong>Inputs</strong></p>
@@ -171,4 +171,4 @@
 <p>To integrate the differences back into the original series (e.g. the forecasts), use the <a href="/widget-catalog/time-series/moving_transform_w">Moving Transform</a> widget.</p>
 <h2>Example</h2>
 <p>In this example, we are using the <a href="yahoo_finance.md">Yahoo Finance</a> data for Amazon stocks for the past 6 years. We pass the data to <strong>Difference</strong> to compute the daily change in the high value of stocks. We observe the change in the <a href="/widget-catalog/time-series/line_chart">Line Chart</a> widget.</p>
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+<a href="/widget-catalog/time-series/images/__optimized-images__/Difference-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2546" height="1320" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/Difference-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Difference\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Make the time series stationary by replacing it with 1st or 2nd order discrete difference along its values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: a dataset, often as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: differences of input time series.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/difference.png\",\n      alt: \"\",\n      width: \"322\",\n      height: \"578\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the series to difference.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Order of differencing. 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 <p>Test if one time series Granger-causes (i.e. can be an indicator of) another time series.</p>
 <p><strong>Inputs</strong></p>
@@ -168,4 +168,4 @@
 <li>The causing (antecedent) series.</li>
 <li>The effect (consequent) series.</li>
 </ol>
-<p>The time series that Granger-cause the series you are interested in are good candidates to have in the same <a href="/widget-catalog/time-series/var">VAR</a> model. But careful, even if one series is said to Granger-cause another, this doesn&#x27;t mean there really exists a causal relationship. Mind your conclusions.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Granger Causality\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Test if one time series Granger-causes (i.e. can be an indicator of) another time series.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widgets performs a series of statistical tests to determine the series that cause other series so we can use the former to forecast the latter.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/granger-causality-stamped.png\",\n      alt: \"\",\n      width: \"478\",\n      height: \"222\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Desired level of confidence.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Maximum lag to test to.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Runs the test.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Denotes the minimum lag at which one series can be said to cause another. 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+<p>The time series that Granger-cause the series you are interested in are good candidates to have in the same <a href="/widget-catalog/time-series/var">VAR</a> model. But careful, even if one series is said to Granger-cause another, this doesn&#x27;t mean there really exists a causal relationship. Mind your conclusions.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Granger Causality\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Test if one time series Granger-causes (i.e. can be an indicator of) another time series.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widgets performs a series of statistical tests to determine the series that cause other series so we can use the former to forecast the latter.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/granger-causality-stamped.png\",\n      alt: \"\",\n      width: \"478\",\n      height: \"222\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Desired level of confidence.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Maximum lag to test to.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Runs the test.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Denotes the minimum lag at which one series can be said to cause another. 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 <p>Induce missing values in the time series by interpolation.</p>
 <p><strong>Inputs</strong></p>
@@ -185,4 +185,4 @@
 <p>Missing values on the series&#x27; end points (head and tail) are always interpolated using <em>nearest</em> method. Unless the interpolation method is set to <em>nearest</em>, discrete time series (i.e. sequences) are always imputed with the series&#x27; <em>mode</em> (most frequent value).</p>
 <h2>Example</h2>
 <p>Pass a time series with missing values in, get interpolated time series out.</p>
-<a href="/widget-catalog/time-series/images/__optimized-images__/interpolate-ex1.png" data-gallery="true"><img alt="" loading="lazy" width="878" height="855" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/interpolate-ex1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Interpolate\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Induce missing values in the time series by interpolation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.strong, {\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: The input time series with the chosen default interpolation method for when the algorithms require interpolated time series (without missing values).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Interpolated time series: The input time series with any missing values interpolated according to the chosen interpolation method.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Most time series algorithms assume, you don't have any missing values in\\nyour data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/yahoo_finance/">Yahoo Finance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/as_timeseries/">As Timeseries</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/moving_transform_w/">Moving Transform</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/time-series/line_chart/">Line Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/periodogram_w/">Periodogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/correlogram/">Correlogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/spiralogram/">Spiralogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/granger_causality/">Granger Causality</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/arima/">ARIMA Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/var/">VAR Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/model_evaluation_w/">Model Evaluation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/time_slice/">Time Slice</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/difference/">Difference</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/seasonal_adjustment/">Seasonal Adjustment</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Line Chart</h1>
 <p>Visualize time series&#x27; sequence and progression in the most basic time series visualization imaginable.</p>
 <p><strong>Inputs</strong></p>
@@ -171,4 +171,4 @@
 <h2>Example</h2>
 <p>The example uses <a href="/widget-catalog/time-series/yahoo_finance">Yahoo Finance</a> data for the previous year. We can observe the data in the <strong>Line Chart</strong>.</p>
 <p>To see the forecast, we have used the <a href="/widget-catalog/time-series/var">VAR Model</a> for training the model. Then, we have attached the model&#x27;s forecast to the <em>Forecast</em> input signal of the Line Chart. The forecast is drawn with a dotted line and the confidence intervals as an ranged area.</p>
-<a href="/widget-catalog/time-series/images/__optimized-images__/LineChart-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2160" height="1822" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/LineChart-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Line Chart\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize time series' sequence and progression in the most basic time series visualization imaginable.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: a dataset, often as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: list of attributes\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Forecast: time series forecast as output by one of the models (like \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/var\",\n          children: \"VAR\"\n        }), \" or \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/arima\",\n          children: \"ARIMA\"\n        }), \").\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can visualize the time series in this widget. Note that the \", _jsx(_components.em, {\n        children: \"line\"\n      }), \" option will display the data as a connected line. In case of non-existent dates, the widget will show them, then the line will be drawn over to connect the last known value with the next one. In case of missing values, the widget will not draw the line in the given place, making the chart disconnected. To better see the missing values, we recommend using the \", _jsx(_components.em, {\n        children: \"column\"\n      }), \" option.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/LineChart.png\",\n      alt: \"\",\n      width: \"965\",\n      height: \"458\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Add a new line chart below the current charts. 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+<a href="/widget-catalog/time-series/images/__optimized-images__/LineChart-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2160" height="1822" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/LineChart-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Line Chart\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize time series' sequence and progression in the most basic time series visualization imaginable.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: a dataset, often as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: list of attributes\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Forecast: time series forecast as output by one of the models (like \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/var\",\n          children: \"VAR\"\n        }), \" or \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/arima\",\n          children: \"ARIMA\"\n        }), \").\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can visualize the time series in this widget. 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 <p>Evaluate different time series&#x27; models.</p>
 <p><strong>Inputs</strong></p>
@@ -169,4 +169,4 @@
 <p><a href="https://image.slidesharecdn.com/granada-140207061551-phpapp01/95/automatic-time-series-forecasting-71-638.jpg?cb=1392426574">This slide</a> (<a href="http://www.slideshare.net/hyndman/automatic-time-series-forecasting">source</a>) shows how cross validation on time series is performed. In this case, the number of folds (1) is 10 and the number of forecast steps in each fold (2) is 1.</p>
 <p>In-sample errors are the errors calculated on the training data itself. A stable model is one where in-sample errors and out-of-sample errors don&#x27;t differ significantly.</p>
 <p>####See also</p>
-<p><a href="/widget-catalog/time-series/arima">ARIMA Model</a>, <a href="/widget-catalog/time-series/var">VAR Model</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a 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dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Model Evaluation\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Evaluate different time series' models.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      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+<p><a href="/widget-catalog/time-series/arima">ARIMA Model</a>, <a href="/widget-catalog/time-series/var">VAR Model</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a 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dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Model Evaluation\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Evaluate different time series' models.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      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@@ -218,4 +218,4 @@ <h2>Examples</h2>
 <p>Another way of using Moving Transform is to aggregated the data by a given period. Let&#x27;s say we wish to observe the yearly averages for the <em>Cyber security breaches</em> data from the <a href="https://orangedatamining.com/widget-catalog/data/datasets/">Datasets</a> widget. We set <em>breach_start</em> as our time variable in <a href="/widget-catalog/time-series/as_timeseries">As Timeseries</a>. In Moving Transform, we aggregated by years, and used mode for <em>US State</em> and <em>Type of Breach</em> variables and sum for the <em>Individuals Affected</em> variable. In the <a href="https://orangedatamining.com/widget-catalog/data/datatable/">Data Table</a> we see the aggregated data. There are 14 instances, each representing a single year.</p>
 <a href="/widget-catalog/time-series/images/__optimized-images__/Moving-Transform-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2856" height="1636" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/Moving-Transform-Example2.png"/></a>
 <h4>See also</h4>
-<p><a href="/widget-catalog/time-series/seasonal_adjustment">Seasonal Adjustment</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Moving Transform\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute aggregations over a sliding window, consecutive blocks or time periods of time series.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series (e.g. one provided by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget).\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: Data with computed aggregations\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/moving-transform.png\",\n      alt: \"\",\n      width: \"1268\",\n      height: \"888\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Define the method for forming blocks of data:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Sliding window\"\n            }), \": use a sliding window of the specified width. 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+<p><a href="/widget-catalog/time-series/seasonal_adjustment">Seasonal Adjustment</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Moving Transform\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute aggregations over a sliding window, consecutive blocks or time periods of time series.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series (e.g. one provided by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget).\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: Data with computed aggregations\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/moving-transform.png\",\n      alt: \"\",\n      width: \"1268\",\n      height: \"888\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Define the method for forming blocks of data:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Sliding window\"\n            }), \": use a sliding window of the specified width. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/yahoo_finance/">Yahoo Finance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/as_timeseries/">As Timeseries</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/moving_transform_w/">Moving Transform</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/line_chart/">Line Chart</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/time-series/periodogram_w/">Periodogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/correlogram/">Correlogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/spiralogram/">Spiralogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/granger_causality/">Granger Causality</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/arima/">ARIMA Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/var/">VAR Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/model_evaluation_w/">Model Evaluation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/time_slice/">Time Slice</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/difference/">Difference</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/seasonal_adjustment/">Seasonal Adjustment</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Periodogram</h1>
 <p>Visualize time series&#x27; cycles, seasonality, periodicity, and most significant periods.</p>
 <p><strong>Inputs</strong></p>
@@ -168,4 +168,4 @@ <h2>Example</h2>
 <p>Here is a simple example on how to use the Periodogram widget. We have passed the <a href="/widget-catalog/time-series/yahoo_finance">Yahoo Finance</a> data to the widget and plotted the periodicity of Amazon stocks for the past 6 years.</p>
 <a href="/widget-catalog/time-series/images/__optimized-images__/Periodogram-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1604" height="1196" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/Periodogram-Example.png"/></a>
 <h4>See also</h4>
-<p><a href="/widget-catalog/time-series/correlogram">Correlogram</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Periodogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize time series' cycles, seasonality, periodicity, and most significant periods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series from the File or as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this widget, you can visualize the most significant periods of the time series.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/periodogram.png\",\n      alt: \"\",\n      width: \"859\",\n      height: \"488\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the series to calculate the periodogram for.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Periodogram for non-equispaced series is calculated using Lomb-Scargle method.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is a simple example on how to use the Periodogram widget. 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+<p><a href="/widget-catalog/time-series/correlogram">Correlogram</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Periodogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize time series' cycles, seasonality, periodicity, and most significant periods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series from the File or as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In this widget, you can visualize the most significant periods of the time series.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/periodogram.png\",\n      alt: \"\",\n      width: \"859\",\n      height: \"488\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the series to calculate the periodogram for.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Periodogram for non-equispaced series is calculated using Lomb-Scargle method.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is a simple example on how to use the Periodogram widget. 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 <p>Decompose the time series into seasonal, trend, and residual components.</p>
 <p><strong>Inputs</strong></p>
@@ -171,4 +171,4 @@
 <h2>Example</h2>
 <a href="/widget-catalog/time-series/images/__optimized-images__/seasonal-adjustment-ex1.png" data-gallery="true"><img alt="" loading="lazy" width="774" height="872" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/seasonal-adjustment-ex1.png"/></a>
 <h4>See also</h4>
-<p><a href="/widget-catalog/time-series/moving_transform">Moving Transform</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a 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dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Seasonal Adjustment\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Decompose the time series into seasonal, trend, and residual components.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: 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\ No newline at end of file
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dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Seasonal Adjustment\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Decompose the time series into seasonal, trend, and residual components.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/yahoo_finance/">Yahoo Finance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/as_timeseries/">As Timeseries</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/interpolate/">Interpolate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/moving_transform_w/">Moving Transform</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/line_chart/">Line Chart</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/periodogram_w/">Periodogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/correlogram/">Correlogram</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/time-series/spiralogram/">Spiralogram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/granger_causality/">Granger Causality</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/arima/">ARIMA Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/var/">VAR Model</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/model_evaluation_w/">Model Evaluation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/time_slice/">Time Slice</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/difference/">Difference</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/time-series/seasonal_adjustment/">Seasonal Adjustment</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Spiralogram</h1>
 <p>Visualize time series&#x27; periodicity in a spiral heatmap.</p>
 <p><strong>Inputs</strong></p>
@@ -177,4 +177,4 @@ <h2>Example</h2>
 <p>We have also selected the sites that were registered in November and were already cleaned. We passed the data to the Data Table, where we can inspect individual dumpsites.</p>
 <a href="/widget-catalog/time-series/images/__optimized-images__/Spiralogram-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2586" height="1506" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/Spiralogram-Example.png"/></a>
 <h4>See also</h4>
-<p><a href="/widget-catalog/time-series/moving_transform_w">Moving Transform</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Spiralogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize time series' periodicity in a spiral heatmap.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series from File or as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Statistics: data table with statistics as shown in the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Spiralogram is intended for visualizing time series and for comparing attribute values by categorical variables. 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+<p><a href="/widget-catalog/time-series/moving_transform_w">Moving Transform</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Spiralogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize time series' periodicity in a spiral heatmap.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series from File or as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Statistics: data table with statistics as shown in the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Spiralogram is intended for visualizing time series and for comparing attribute values by categorical variables. 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The data record when the dumpsite was registered, what kind of waste was deposited, whether the site was cleaned, and so on.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We will use \", _jsx(_components.a, {\n        href: \"/widget-catalog/time-series/as_timeseries\",\n        children: \"As Timeseries\"\n      }), \" for set \", _jsx(_components.em, {\n        children: \"Entry creation date\"\n      }), \" as our time series variable. Then we pass the data to \", _jsx(_components.strong, {\n        children: \"Spiralogram\"\n      }), \". The widget offers many option, so we will show here a slightly more complex set-up.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We split the circle into months of the year. Next, we split the vertical axis by an attribute \", _jsx(_components.em, {\n        children: \"Was cleaned\"\n      }), \", meaning the outer circle shows cleaned dumpsites and the inner circle the ones with the trash. 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 <p>Select a slice of measurements on a time interval.</p>
 <p><strong>Inputs</strong></p>
@@ -172,4 +172,4 @@
 </ol>
 <h2>Example</h2>
 <p>This simple example uses <a href="yahoo_finance.md">Yahoo Finance</a> widget to retrieve financial data from Yahoo, namely the AMNZ stock index from 2015 to 2020. Next, we will use <strong>Time Slice</strong> to observe how the data changed through time. We can observe the output of Time Slice in <a href="/widget-catalog/time-series/line_chart">Line Chart</a>. Press <em>Play</em> in Time Slice and see how Line Chart changes interactively.</p>
-<a href="/widget-catalog/time-series/images/__optimized-images__/time-slice-example.png" data-gallery="true"><img alt="" loading="lazy" width="2672" height="1402" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/time-slice-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Time Slice\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Select a slice of measurements on a time interval.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Data: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Subset: Selected time slice from the time series.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Time slice is a subset selection widget designed specifically for time series and for interactive visualizations. 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 <p>Model the time series using <a href="https://en.wikipedia.org/wiki/Vector_autoregression">vector autoregression (VAR) model</a>.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@
 <h2>Example</h2>
 <a href="/widget-catalog/time-series/images/__optimized-images__/LineChart-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2160" height="1822" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/LineChart-Example.png"/></a>
 <h4>See also</h4>
-<p><a href="arima.md">ARIMA Model</a>, <a href="/widget-catalog/time-series/model_evaluation_w">Model Evaluation</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"VAR Model\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Model the time series using \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Vector_autoregression\",\n        children: \"vector autoregression (VAR) model\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series model: The VAR model fitted to input time series.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Forecast: The forecast time series.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Fitted values: The values that the model was actually fitted to, equals to \", _jsx(_components.em, {\n          children: \"original values - residuals\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residuals: The errors the model made at each step.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using this widget, you can model the time series using VAR model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/var-model-stamped.png\",\n      alt: \"\",\n      width: \"233\",\n      height: \"581\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model's name. 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+<p><a href="arima.md">ARIMA Model</a>, <a href="/widget-catalog/time-series/model_evaluation_w">Model Evaluation</a></p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"VAR Model\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Model the time series using \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Vector_autoregression\",\n        children: \"vector autoregression (VAR) model\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Time series: Time series as output by \", _jsx(_components.a, {\n          href: \"/widget-catalog/time-series/as_timeseries\",\n          children: \"As Timeseries\"\n        }), \" widget.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series model: The VAR model fitted to input time series.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Forecast: The forecast time series.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Fitted values: The values that the model was actually fitted to, equals to \", _jsx(_components.em, {\n          children: \"original values - residuals\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Residuals: The errors the model made at each step.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Using this widget, you can model the time series using VAR model.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/var-model-stamped.png\",\n      alt: \"\",\n      width: \"233\",\n      height: \"581\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Model's name. 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 </ol>
 <h2>Example</h2>
 <p>Since the output data type is inherently a <strong>Table</strong>, you can connect it to wherever a data table is expected. Naturally, you can use it to test some functions in the Timeseries add-on.</p>
-<a href="/widget-catalog/time-series/images/__optimized-images__/yahoo-finance-ex1.png" data-gallery="true"><img alt="" loading="lazy" width="692" height="605" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/yahoo-finance-ex1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Yahoo Finance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generate time series from Yahoo Finance stock market data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: Time series table of open, high, low, close (OHLC) prices, volume and adjusted close price.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget fetches historical stock market data from Yahoo Finance and outputs it as a time series data table.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/yahoo-finance-stamped.png\",\n      alt: \"\",\n      width: \"265\",\n      height: \"180\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Stock (e.g. GOOG) or index (e.g. DJI) symbol you are interested in.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Date range you are interested in.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Desired resolution of the time series. 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+<a href="/widget-catalog/time-series/images/__optimized-images__/yahoo-finance-ex1.png" data-gallery="true"><img alt="" loading="lazy" width="692" height="605" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/time-series/images/__optimized-images__/yahoo-finance-ex1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Yahoo Finance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Generate time series from Yahoo Finance stock market data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Time series: Time series table of open, high, low, close (OHLC) prices, volume and adjusted close price.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This widget fetches historical stock market data from Yahoo Finance and outputs it as a time series data table.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/time-series/images/__optimized-images__/yahoo-finance-stamped.png\",\n      alt: \"\",\n      width: \"265\",\n      height: \"180\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Stock (e.g. GOOG) or index (e.g. DJI) symbol you are interested in.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Date range you are interested in.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Desired resolution of the time series. 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 <p>Compute a sum, max, min ... of selected columns.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@
 <h2>Example</h2>
 <p>We will use iris data from the <a href="/widget-catalog/transform/../data/file">File</a> widget for this example and connect it to <strong>Aggregate Columns</strong>.</p>
 <p>Say we wish to compute a sum of <em>sepal_length</em> and <em>sepal_width</em> attributes. We select the two attributes from the list.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/AggregateColumns-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2170" height="1218" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/AggregateColumns-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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@@ -173,4 +173,4 @@ <h2>Example</h2>
 <p>We will transform the data with <a href="/widget-catalog/transform/../unsupervised/PCA">PCA</a> and select the first two components, which explain 96% of variance. Now, we would like to apply the same preprocessing on the &#x27;new&#x27; data, that is the remaining 50 iris virginicas. Send the unused data from <strong>Select Rows</strong> to <strong>Apply Domain</strong>. Make sure to use the <em>Unmatched Data</em> output from <strong>Select Rows</strong> widget. Then add the <em>Transformed data</em> output from <strong>PCA</strong>.</p>
 <p><strong>Apply Domain</strong> will apply the preprocessor to the new data and output it. To add the new data to the old data, use <a href="/widget-catalog/transform/../data/concatenate">Concatenate</a>. Use <em>Transformed Data</em> output from <strong>PCA</strong> as <em>Primary Data</em> and <em>Transformed Data</em> from <strong>Apply Domain</strong> as <em>Additional Data</em>.</p>
 <p>Observe the results in a <a href="../data/datatable.md">Data Table</a> or in a <a href="/widget-catalog/transform/../visualize/scatterplot">Scatter Plot</a> to see the new data in relation to the old one.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/ApplyDomain-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2838" height="1932" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/ApplyDomain-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    h2: \"h2\",\n    a: \"a\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Apply Domain\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Given dataset and template transforms the dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Template Data: template for transforming the dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Transformed Data: transformed dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Apply Domain\"\n      }), \" maps new data into a transformed space. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Concatenate</h1>
 <p>Concatenates data from multiple sources.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@
 <h2>Example</h2>
 <p>As shown below, the widget can be used for merging data from two separate files. Let&#x27;s say we have two data sets with the same attributes, one containing instances from the first experiment and the other instances from the second experiment and we wish to join the two data tables together. We use the <strong>Concatenate</strong> widget to merge the data sets by attributes (appending new rows under existing attributes).</p>
 <p>Below, we used a modified <em>Zoo</em> data set. In the <a href="http://file.biolab.si/datasets/zoo-first.tab">first</a> <a href="../data/file.md">File</a> widget, we loaded only the animals beginning with the letters A and B and in the <a href="http://file.biolab.si/datasets/zoo-second.tab">second</a> one only the animals beginning with the letter C. Upon concatenation, we observe the new data in the <a href="/widget-catalog/transform/../data/datatable">Data Table</a> widget, where we see the complete table with animals from A to C.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Concatenate-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1910" height="1944" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Concatenate-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Concatenate\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Concatenates data from multiple sources.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Primary Data: data set that defines the attribute set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Additional Data: additional data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: concatenated data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget concatenates multiple sets of instances (data sets). 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 <p>Turns discrete variables (attributes) into numeric (&quot;continuous&quot;) dummy variables.</p>
 <p><strong>Inputs</strong></p>
@@ -226,4 +226,4 @@ <h2>Examples</h2>
 <p>First, let&#x27;s see what is the output of the <strong>Continuize</strong> widget. We feed the original data (the <em>Heart disease</em> data set) into the <a href="/widget-catalog/transform/../data/datatable">Data Table</a> and see how they look like. Then we continuize the discrete values using various options and observe them in another <a href="/widget-catalog/transform/../data/datatable">Data Table</a>.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/Continuize-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="2980" height="1868" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Continuize-Example1.png"/></a>
 <p>In the second example, we show a typical use of this widget - in order to properly plot the linear projection of the data, discrete attributes need to be converted to continuous ones and that is why we put the data through the <strong>Continuize</strong> widget before drawing it. Gender, for instance, is transformed into two attributes &quot;<em>gender=female</em>&quot; and <em>gender=male</em>.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Continuize-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1205" height="852" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Continuize-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Continuize\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Turns discrete variables (attributes) 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+<a href="/widget-catalog/transform/images/__optimized-images__/Continuize-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1205" height="852" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Continuize-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Continuize\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Turns discrete variables (attributes) 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 <p>Create class attribute from a string attribute.</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@ <h2>Example</h2>
 <p>Here is a simple example with the <em>auto-mpg</em> dataset. Pass the data to <strong>Create Class</strong>. Select <em>car_name</em> as a column to create the new class from. Here, we wish to create new values that match the car brand. First, we type <em>ford</em> as the new value for the matching strings. Then we define the substring that will match the data instances. This means that all instances containing <em>ford</em> in their <em>car_name</em>, will now have a value <em>ford</em> in the new class column. Next, we define the same for <em>honda</em> and <em>fiat</em>. The widget will tell us how many instance are yet unmatched (remaining instances). We will name them <em>other</em>, but you can continue creating new values by adding a condition with &#x27;+&#x27;.</p>
 <p>We named our new class column <em>car_brand</em> and we matched at the beginning of the string.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/CreateClass-example.png" data-gallery="true"><img alt="" loading="lazy" width="2236" height="1326" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/CreateClass-example.png"/></a>
-<p>Finally, we can observe the new column in a <a href="../data/datatable.md">Data Table</a> or use the value as color in the <a href="/widget-catalog/transform/../visualize/scatterplot">Scatter Plot</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Create Class\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Create class attribute from a string attribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with a new class variable\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Create Class\"\n      }), \" creates a new class attribute from an existing discrete or string attribute. 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\ No newline at end of file
+<p>Finally, we can observe the new column in a <a href="../data/datatable.md">Data Table</a> or use the value as color in the <a href="/widget-catalog/transform/../visualize/scatterplot">Scatter Plot</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Create Class\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Create class attribute from a string attribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with a new class variable\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Create Class\"\n      }), \" creates a new class attribute from an existing discrete or string attribute. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Create Instance</h1>
 <p>Interactively creates an instance from a sample dataset.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@ <h2>Example</h2>
 A <a href="/widget-catalog/transform/../data/selectcolumns">Select Column</a> widget is inserted to omit the actual target value.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/CreateInstance-example.png" data-gallery="true"><img alt="" loading="lazy" width="1398" height="774" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/CreateInstance-example.png"/></a>
 <p>The next example shows how to check whether the created instance is some kind of outlier. The creates instance is feed to <a href="../unsupervised/PCA.md">PCA</a> whose first and second componens are then examined in a <a href="/widget-catalog/transform/../visualize/scatterplot">Scatter Plot</a>. The created instance is colored red in the plot and it could be considered as an outlier if it appears far from the original data (blue).</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/CreateInstance-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1447" height="901" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/CreateInstance-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Create Instance\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Interactively creates an instance from a sample dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference: refrence dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset appended the created instance\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Create Instance\"\n      }), \" widget creates a new instance, based on the input data. 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 <p>Selects a subset of data instances from an input dataset.</p>
 <p><strong>Inputs</strong></p>
@@ -191,4 +191,4 @@ <h2>Over/Undersampling</h2>
 <p><strong>Data Sampler</strong> can also be used to oversample a minority class or undersample majority class in the data. Let us show an example for oversampling. First, separate the minority class using a <a href="../data/selectrows.md">Select Rows</a> widget. We are using the <em>iris</em> data from the <a href="/widget-catalog/transform/../data/file">File</a> widget. The data set has 150 data instances, 50 of each class. Let us oversample, say, <em>iris-setosa</em>.</p>
 <p>In <strong>Select Rows</strong>, set the condition to <em>iris is iris-setosa</em>. This will output 50 instances of the <em>iris-setosa</em> class. Now, connect <em>Matching Data</em> into the <strong>Data Sampler</strong>, select <em>Fixed sample size</em>, set it to, say, 100 and select <em>Sample with replacement</em>. Upon pressing <em>Sample Data</em>, the widget will output 100 instances of <em>iris-setosa</em> class, some of which will be duplicated (because we used <em>Sample with replacement</em>).</p>
 <p>Finally, use <a href="../data/concatenate">Concatenate</a> to join the oversampled instances and the <em>Unmatched Data</em> output of the <strong>Select Rows</strong> widget. This outputs a data set with 200 instances. We can observe the final results in the <a href="/widget-catalog/transform/../visualize/distributions">Distributions</a>.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/DataSampler-Example-OverUnderSampling.png" data-gallery="true"><img alt="" loading="lazy" width="2754" height="1544" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/DataSampler-Example-OverUnderSampling.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Data Sampler\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Selects a subset of data instances from an input dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data Sample: sampled data instances\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Remaining Data: out-of-sample data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Data Sampler\"\n      }), \" widget implements several data sampling methods. 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+<a href="/widget-catalog/transform/images/__optimized-images__/DataSampler-Example-OverUnderSampling.png" data-gallery="true"><img alt="" loading="lazy" width="2754" height="1544" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/DataSampler-Example-OverUnderSampling.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Data Sampler\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Selects a subset of data instances from an input dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data Sample: sampled data instances\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Remaining Data: out-of-sample data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Data Sampler\"\n      }), \" widget implements several data sampling methods. It outputs a sampled and a complementary dataset (with instances from the input set that are not included in the sampled dataset). The output is processed after the input dataset is provided and \", _jsx(_components.em, {\n        children: \"Sample Data\"\n      }), \" is pressed.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/DataSampler-stamped.png\",\n      alt: \"\",\n      width: \"530\",\n      height: \"1090\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the input and output dataset.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The desired sampling method:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Fixed proportion of data\"\n            }), \" returns a selected percentage of the entire data (e.g. 70% of all the data)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Fixed sample size\"\n            }), \" returns a selected number of data instances with a chance to set \", _jsx(_components.em, {\n              children: \"Sample with replacement\"\n            }), \", which always samples from the entire dataset (does not subtract instances already in the subset). 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Discretize</h1>
 <p>Converts numeric attributes to categorical.</p>
 <p><strong>Inputs</strong></p>
@@ -198,4 +198,4 @@ <h2>Example</h2>
 <li>removed <em>Cholesterol</em>,</li>
 <li>and used <em>entropy-mdl</em> for the remaining variables, which resulted in removing <em>rest SBP</em> and in two intervals for <em>ST by exercise</em> and <em>major vessels colored</em>.</li>
 </ul>
-<a href="/widget-catalog/transform/images/__optimized-images__/Discretize-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2222" height="1492" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Discretize-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Discretize\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Converts numeric attributes to categorical.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with discretized values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Discretize\"\n      }), \" widget \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Discretization\",\n        children: \"discretizes\"\n      }), \" numeric variables.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Discretize.png\",\n      alt: \"\",\n      width: \"768\",\n      height: \"610\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Set default method for discretization.\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Select variables to set specific discretization methods for each. 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 <p>Add new features to your dataset.</p>
 <p><strong>Inputs</strong></p>
@@ -201,4 +201,4 @@ <h2>Hints</h2>
 <li><code>!=</code> not equal</li>
 <li>if-else: <em>value</em> <code>if</code> <em>condition</em> else <em>other-value</em> (see the above example</li>
 </ul>
-<p>See more <a href="http://www.tutorialspoint.com/python/python_basic_operators.htm">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    code: \"code\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Formula\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Add new features to your dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with additional features\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Formula\"\n      }), \" allows computing new columns by combining the existing ones with a user-defined expression. 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 <p>Groups data by selected variables and aggregate columns with selected aggregations.</p>
 <p><strong>Inputs</strong></p>
@@ -177,4 +177,4 @@ <h2>Examples</h2>
 <p>In the table on the right-hand side of the widget, we set that we want to compute <strong>mean</strong> and  <strong>median</strong> for values of <strong>rest SBP</strong> variable in each group, <strong>median</strong> for values of <strong>cholesterol</strong> variable, and <strong>mean</strong> for <strong>major vessels colored</strong>.</p>
 <p>In the <strong>Data Table</strong> widget, we can see that both females and males have lower average values for <strong>rest SBP</strong> when <strong>diameter narrowing</strong> is 0. The difference is greater for females. The median of <strong>rest SBP</strong> is different only for females, while for males is the same.</p>
 <p>You can also observe differences between median <strong>cholesterol</strong> level and mean value of <strong>major vessel colored</strong> between groups.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Impute</h1>
 <p>Replaces unknown values in the data.</p>
 <p><strong>Inputs</strong></p>
@@ -195,4 +195,4 @@
 </ol>
 <h2>Example</h2>
 <p>To demonstrate how the <strong>Impute</strong> widget works, we played around with the <em>Iris</em> dataset and deleted some of the data. We used the <strong>Impute</strong> widget and selected the <em>Model-based imputer</em> to impute the missing values. In another <a href="/widget-catalog/transform/../data/datatable">Data Table</a>, we see how the question marks turned into distinct values (&quot;Iris-setosa, &quot;Iris-versicolor&quot;).</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Impute-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1390" height="916" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Impute-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Impute\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Replaces unknown values in the data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Learner: learning algorithm for imputation\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with imputed values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Some Orange's algorithms and visualizations cannot handle unknown values in the data. 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 <p>Transform <a href="https://en.wikipedia.org/wiki/Wide_and_narrow_data">wide data to narrow</a>.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@ <h2>Example</h2>
 <p>An interesting immediate use for this is to pass this data to Distributions and see what are the most and the least common features of animals.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/Melt-Workflow.png" data-gallery="true"><img alt="" loading="lazy" width="1544" height="1654" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Melt-Workflow.png"/></a>
 <p>In the next example we show how shuffling class values influences model performance on the same dataset as above.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Melt-Distribution.png" data-gallery="true"><img alt="" loading="lazy" width="1137" height="668" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Melt-Distribution.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Melt\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Transform \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Wide_and_narrow_data\",\n        children: \"wide data to narrow\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: wide data table\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: narrow data table\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Melt\"\n      }), \" widget receives a dataset in the more common wide format and outputs a table of (row_id, variable, value) triplets.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Melt-Default-stamped.png\",\n      alt: \"\",\n      width: \"452\",\n      height: \"650\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the variable used as id. 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@@ -216,4 +216,4 @@ <h2>Examples</h2>
 <p>The case where we want to include all instances in the output, even those where no match by attribute <em>names</em> was found, is shown in the following workflow.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/MergeData-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1008" height="854" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/MergeData-Example2.png"/></a>
 <p>The third type of merging is shown in the next workflow. The output consists of both inputs, with unknown values assigned where no match was found.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/MergeData-Example3.png" data-gallery="true"><img alt="" loading="lazy" width="1163" height="957" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/MergeData-Example3.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Merge Data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Merges two datasets, based on values of selected attributes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Extra Data: additional dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with features added from extra data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Merge Data\"\n      }), \" widget is used to horizontally merge two datasets, based on the values of selected attributes (columns). In the input, two datasets are required, data and extra data. Rows from the two data sets are matched by the values of pairs of attributes, chosen by the user. The widget produces one output. It corresponds to the instances from the input data to which attributes (columns) from input extra data are appended.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To match by a combination of features click on the plus icon to add the features to merge on.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Depending upon the merge types, selected features may be required to have unique values (that is, no duplicates) in the data. When merging by multiple features, this pertains to a combinations of their values.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Merge-Data-stamped.png\",\n      alt: \"\",\n      width: \"398\",\n      height: \"368\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on main data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on data to append.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Merging type:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Append columns from Extra Data\"\n            }), \" outputs all rows from the Data, augmented by the columns in the Extra Data. Rows without matches are retained, even where the data in the extra columns are missing.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Find matching pairs of rows\"\n            }), \" outputs rows from the Data, augmented by the columns in the Extra Data. Rows without matches are removed from the output.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Concatenate tables\"\n            }), \" treats both data sources symmetrically. The output is similar to the first option, except that non-matched values from Extra Data are appended at the end.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"List of attributes from Data input.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"List of attributes from Extra Data input.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Merging Types\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Append Columns from Extra Data (left join)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Columns from the Extra Data are added to the Data. Instances with no matching rows will have missing values added.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For example, the first table may contain city names and the second would be a list of cities and their coordinates. Columns with coordinates would then be appended to the data with city names. Where city names cannot be matched, missing values will appear.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In our example, the first Data input contained 6 cities, but the Extra Data did not provide Lat and Lon values for Bratislava, so the fields will be empty.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this type of merge, the values on the left (e.g. cities) may repeat (e.g. the same city appear multiple times), while the \", _jsx(_components.em, {\n        children: \"used\"\n      }), \" value on the right must not. For example, let the right-hand table contain multiple Springfields. If Springfield does not appear on the left, the widget will show a warning but still merge the data. If Springfield does appear on the left as well, the widget will show an error. This can be resolved if the both table also include the data on the state (e.g. Illinois, Missouri, Oregon, Ohio) and this feature is added to the combination being matched.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData_Append.png\",\n      alt: \"\",\n      width: \"443\",\n      height: \"251\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Find matching pairs of rows (inner join)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Only those rows that are matched will be present on the output, with the Extra Data columns appended. Rows without matches are removed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In our example, Bratislava from the Data input did not have Lat and Lon values, while Belgrade from the Extra Data could not be found in the City column we were merging on. Hence both instances are removed - only the intersection of instances is sent to the output.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For this type of merge, combinations of features on the left and on the right must be unique.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData_Intersection.png\",\n      alt: \"\",\n      width: \"443\",\n      height: \"251\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Concatenate tables (outer join)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The rows from both the Data and the Extra Data will be present on the output. Where rows cannot be matched, missing values will appear.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In our example, both Bratislava and Belgrade are now present. Bratislava will have missing Lat and Lon values, while Belgrade will have a missing Population value.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For this type of merge, combinations of features on the left and on the right must be unique.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData_Concatenate.png\",\n      alt: \"\",\n      width: \"441\",\n      height: \"275\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Row index\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Data will be merged in the same order as they appear in the table. Row number 1 from the Data input will be joined with row number 1 from the Extra Data input. Row numbers are assigned by Orange based on the original order of the data instances.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Instance ID\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is a more complex option. Sometimes, data in transformed in the analysis and the domain is no longer the same. Nevertheless, the original row indices are still present in the background (Orange remembers them). In this case one can merge on instance ID. For example if you transformed the data with PCA, visualized it in the Scatter Plot, selected some data instances and now you wish to see the original information of the selected subset. Connect the output of Scatter Plot to Merge Data, add the original data set as Extra Data and merge by Instance ID.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-InstanceID.png\",\n      alt: \"\",\n      width: \"530\",\n      height: \"175\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Merge by two or more attributes\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Sometimes our data instances are unique with respect to a combination of columns, not a single column. To merge by more than a single column, add the \", _jsx(_components.em, {\n        children: \"Row matching\"\n      }), \" condition by pressing plus next to the matching condition. To remove it, press the x.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the below example, we are merging by \", _jsx(_components.em, {\n        children: \"student\"\n      }), \" column and \", _jsx(_components.em, {\n        children: \"class\"\n      }), \" column.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-multiple.png\",\n      alt: \"\",\n      width: \"456\",\n      height: \"406\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Say we have two data sets with student names and the class they're in. The first data set has students' grades and the second on the elective course they have chosen. Unfortunately, there are two Jacks in our data, one from class A and the other from class B. 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In the latter, the \", _jsx(_components.strong, {\n        children: \"Merged Data\"\n      }), \" channels are shown, where image attributes are added to the original data.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-Example.png\",\n      alt: \"\",\n      width: \"1000\",\n      height: \"844\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The case where we want to include all instances in the output, even those where no match by attribute \", _jsx(_components.em, {\n        children: \"names\"\n      }), \" was found, is shown in the following workflow.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-Example2.png\",\n      alt: \"\",\n      width: \"1008\",\n      height: \"854\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The third type of merging is shown in the next workflow. The output consists of both inputs, with unknown values assigned where no match was found.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-Example3.png\",\n      alt: \"\",\n      width: \"1163\",\n      height: \"957\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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+<a href="/widget-catalog/transform/images/__optimized-images__/MergeData-Example3.png" data-gallery="true"><img alt="" loading="lazy" width="1163" height="957" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/MergeData-Example3.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Merge Data\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Merges two datasets, based on values of selected attributes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Extra Data: additional dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with features added from extra data\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Merge Data\"\n      }), \" widget is used to horizontally merge two datasets, based on the values of selected attributes (columns). In the input, two datasets are required, data and extra data. Rows from the two data sets are matched by the values of pairs of attributes, chosen by the user. The widget produces one output. It corresponds to the instances from the input data to which attributes (columns) from input extra data are appended.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To match by a combination of features click on the plus icon to add the features to merge on.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Depending upon the merge types, selected features may be required to have unique values (that is, no duplicates) in the data. When merging by multiple features, this pertains to a combinations of their values.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Merge-Data-stamped.png\",\n      alt: \"\",\n      width: \"398\",\n      height: \"368\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on main data.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information on data to append.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Merging type:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Append columns from Extra Data\"\n            }), \" outputs all rows from the Data, augmented by the columns in the Extra Data. Rows without matches are retained, even where the data in the extra columns are missing.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Find matching pairs of rows\"\n            }), \" outputs rows from the Data, augmented by the columns in the Extra Data. Rows without matches are removed from the output.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Concatenate tables\"\n            }), \" treats both data sources symmetrically. The output is similar to the first option, except that non-matched values from Extra Data are appended at the end.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"List of attributes from Data input.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"List of attributes from Extra Data input.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Merging Types\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Append Columns from Extra Data (left join)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Columns from the Extra Data are added to the Data. Instances with no matching rows will have missing values added.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For example, the first table may contain city names and the second would be a list of cities and their coordinates. Columns with coordinates would then be appended to the data with city names. Where city names cannot be matched, missing values will appear.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In our example, the first Data input contained 6 cities, but the Extra Data did not provide Lat and Lon values for Bratislava, so the fields will be empty.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For this type of merge, the values on the left (e.g. cities) may repeat (e.g. the same city appear multiple times), while the \", _jsx(_components.em, {\n        children: \"used\"\n      }), \" value on the right must not. For example, let the right-hand table contain multiple Springfields. If Springfield does not appear on the left, the widget will show a warning but still merge the data. If Springfield does appear on the left as well, the widget will show an error. This can be resolved if the both table also include the data on the state (e.g. Illinois, Missouri, Oregon, Ohio) and this feature is added to the combination being matched.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData_Append.png\",\n      alt: \"\",\n      width: \"443\",\n      height: \"251\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Find matching pairs of rows (inner join)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Only those rows that are matched will be present on the output, with the Extra Data columns appended. Rows without matches are removed.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In our example, Bratislava from the Data input did not have Lat and Lon values, while Belgrade from the Extra Data could not be found in the City column we were merging on. Hence both instances are removed - only the intersection of instances is sent to the output.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For this type of merge, combinations of features on the left and on the right must be unique.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData_Intersection.png\",\n      alt: \"\",\n      width: \"443\",\n      height: \"251\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Concatenate tables (outer join)\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The rows from both the Data and the Extra Data will be present on the output. 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Row numbers are assigned by Orange based on the original order of the data instances.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"#####Instance ID\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"This is a more complex option. Sometimes, data in transformed in the analysis and the domain is no longer the same. Nevertheless, the original row indices are still present in the background (Orange remembers them). In this case one can merge on instance ID. For example if you transformed the data with PCA, visualized it in the Scatter Plot, selected some data instances and now you wish to see the original information of the selected subset. 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To remove it, press the x.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the below example, we are merging by \", _jsx(_components.em, {\n        children: \"student\"\n      }), \" column and \", _jsx(_components.em, {\n        children: \"class\"\n      }), \" column.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-multiple.png\",\n      alt: \"\",\n      width: \"456\",\n      height: \"406\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Say we have two data sets with student names and the class they're in. The first data set has students' grades and the second on the elective course they have chosen. Unfortunately, there are two Jacks in our data, one from class A and the other from class B. 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In the latter, the \", _jsx(_components.strong, {\n        children: \"Merged Data\"\n      }), \" channels are shown, where image attributes are added to the original data.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-Example.png\",\n      alt: \"\",\n      width: \"1000\",\n      height: \"844\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The case where we want to include all instances in the output, even those where no match by attribute \", _jsx(_components.em, {\n        children: \"names\"\n      }), \" was found, is shown in the following workflow.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-Example2.png\",\n      alt: \"\",\n      width: \"1008\",\n      height: \"854\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The third type of merging is shown in the next workflow. The output consists of both inputs, with unknown values assigned where no match was found.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/MergeData-Example3.png\",\n      alt: \"\",\n      width: \"1163\",\n      height: \"957\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Pivot Table</h1>
 <p>Reshape data table based on column values.</p>
 <p><strong>Inputs</strong></p>
@@ -211,4 +211,4 @@ <h2>Example</h2>
 <p>We are using <em>Forest Fires</em> for this example. The data is loaded in the <a href="/widget-catalog/transform/../data/datasets">Datasets</a> widget and passed to <strong>Pivot Table</strong>. <em>Forest Fires</em> datasets reports forest fires by the month and day they happened. We can aggregate all occurrences of forest fires by selecting <em>Count</em> as aggregation method and using <em>month</em> as row and <em>day</em> as column values. Since we are using <em>Count</em>, <em>Values</em> variable will have no effect.</p>
 <p>We can plot the counts in <a href="../visualize/lineplot.md">Line Plot</a>. But first, let us organize our data a bit. With <a href="../data/editdomain.md">Edit Domain</a>, we will reorder rows values so that months will appear in the correct order, namely from January to December. To do the same for columns, we will use <a href="/widget-catalog/transform/../data/selectcolumns">Select Columns</a> and reorder day to go from Monday to Sunday.</p>
 <p>Finally, our data is ready. Let us pass it to <strong>Line Plot</strong>. We can see that forest fires are most common in August and September, while their frequency is higher during the weekend than during weekdays.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Pivot-example.png" data-gallery="true"><img alt="" loading="lazy" width="2978" height="1858" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Pivot-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pivot Table\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Reshape data table based on column values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Pivot Table: contingency matrix as shown in the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Filtered Data: subset selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Grouped Data: aggregates over groups defined by row values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Pivot Table\"\n      }), \" summarizes the data of a more extensive table into a table of statistics. The statistics can include sums, averages, counts, etc. The widget also allows selecting a subset from the table and grouping by row values, which have to be a discrete variable. Data with only numeric variables cannot be displayed in the table.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Pivot-stamped.png\",\n      alt: \"\",\n      width: \"646\",\n      height: \"525\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Discrete or numeric variable used for row values. Numeric variables are considered as integers.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Discrete variable used for column values. Variable values will appear as columns in the table.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Values used for aggregation. Aggregated values will appear as cells in the table.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Aggregation methods:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [\"For any variable type:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Count\"\n                }), \": number of instances with the given row and column value.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Count defined\"\n                }), \": number of instances where the aggregation value is defined.\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"For numeric variables:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Sum\"\n                }), \": sum of values.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Mean\"\n                }), \": average of values.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Mode\"\n                }), \": most frequent value of the subset.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Min\"\n                }), \": smallest value.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Max\"\n                }), \": highest value.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Median\"\n                }), \": middle value.\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Var\"\n                }), \": variance of the subset.\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"For discrete variables:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"Majority\"\n                }), \": most frequent value of the subset.\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Tick the box on the left to automatically output any changes. Alternatively, press \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \" .\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Discrete variables\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Pivot-discrete.png\",\n      alt: \"\",\n      width: \"835\",\n      height: \"525\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Example of a pivot table with only discrete variables selected. We are using \", _jsx(_components.em, {\n        children: \"heart-disease\"\n      }), \" data set for this example. Rows correspond to values of \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \" variable. Our columns are values of \", _jsx(_components.em, {\n        children: \"gender\"\n      }), \", namely female and male. We are using \", _jsx(_components.em, {\n        children: \"thal\"\n      }), \" as values in our cells.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We have selected \", _jsx(_components.em, {\n        children: \"Count\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Majority\"\n      }), \" as aggregation methods. In the pivot table, we can see the number of instances that do not have diameter narrowing and are female. There are 72 such patients. Concurrently, there are 92 male patients that don't have diameter narrowing. Thal values don't have any effect here, we are just counting occurrences in the data.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The second row shows majority. This means most female patients that don't have diameter narrowing have normal thal results. Conversely, female patients that have diameter narrowing most often have reversable defect.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Numeric variables\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Pivot-continuous.png\",\n      alt: \"\",\n      width: \"689\",\n      height: \"525\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Example of a pivot table with numeric variables. We are using \", _jsx(_components.em, {\n        children: \"heart-disease\"\n      }), \" data set for this example. Rows correspond to values of \", _jsx(_components.em, {\n        children: \"diameter narrowing\"\n      }), \" variable. Our columns are values of \", _jsx(_components.em, {\n        children: \"gender\"\n      }), \", namely female and male. We are using \", _jsx(_components.em, {\n        children: \"rest SBP\"\n      }), \" as values in our cells.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We have selected \", _jsx(_components.em, {\n        children: \"Count\"\n      }), \", \", _jsx(_components.em, {\n        children: \"Sum\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Median\"\n      }), \" as aggregation methods. Under \", _jsx(_components.em, {\n        children: \"Count\"\n      }), \", we see there are 72 female patients that don't have diameter narrowing, same as before for discrete values. What is different are the sum and median aggregations. We see that the sum of resting systolic blood pressure for female patients that don't have diameter narrowing is 9269 and the median value is 130.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We are using \", _jsx(_components.em, {\n        children: \"Forest Fires\"\n      }), \" for this example. The data is loaded in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../data/datasets\",\n        children: \"Datasets\"\n      }), \" widget and passed to \", _jsx(_components.strong, {\n        children: \"Pivot Table\"\n      }), \". \", _jsx(_components.em, {\n        children: \"Forest Fires\"\n      }), \" datasets reports forest fires by the month and day they happened. 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We can see that forest fires are most common in August and September, while their frequency is higher during the weekend than during weekdays.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Pivot-example.png\",\n      alt: \"\",\n      width: \"2978\",\n      height: \"1858\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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+<a href="/widget-catalog/transform/images/__optimized-images__/Pivot-example.png" data-gallery="true"><img alt="" loading="lazy" width="2978" height="1858" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Pivot-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pivot Table\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Reshape data table based on column values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input data set\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Pivot Table: contingency matrix as shown in the widget\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Filtered Data: subset selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Grouped Data: aggregates over groups defined by row values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Pivot Table\"\n      }), \" summarizes the data of a more extensive table into a table of statistics. 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--- a/widget-catalog/transform/preprocess/index.html
+++ b/widget-catalog/transform/preprocess/index.html
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Preprocess</h1>
 <p>Preprocesses data with selected methods.</p>
 <p><strong>Inputs</strong></p>
@@ -236,4 +236,4 @@ <h2>Examples</h2>
 <p>This time we are using the <em>heart_disease.tab</em> data from the <a href="/widget-catalog/transform/../data/file">File</a> widget. You can access the data in the dropdown menu. This is a dataset with 303 patients that came to the doctor suffering from a chest pain. After the tests were done, some patients were found to have diameter narrowing and others did not (this is our class variable).</p>
 <p>Some values are missing in our data set, so we would like to impute missing values before evaluating the model. We do this by passing a preprocessor directly to <a href="/widget-catalog/transform/../evaluate/testandscore">Test and Score</a>. In <strong>Preprocess</strong>, we set the correct preprocessing pipeline (in our example only a single preprocessor with <em>Impute missing values</em>), then connect it to the Preprocessor input of Test and Score.</p>
 <p>We also pass the data and the learner (in this case, a <a href="/widget-catalog/transform/../model/logisticregression">Logistic Regression</a>). This is the correct way to pass a preprocessor to cross-validation as each fold will independently get preprocessed in the training phase. This is particularly important for feature selection.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Preprocess-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2614" height="1426" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Preprocess-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Preprocess\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Preprocesses data with selected methods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessed Data: data preprocessed with selected methods\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Preprocessing is crucial for achieving better-quality analysis results. 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If discrete values are categories, each category will be assigned a number as they appear in the data.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Divide by number of values\"\n            }), \" is similar to treat as ordinal, but the final values will be divided by the total number of values and hence the range of the new continuous variable will be [0, 1].\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Impute missing values:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Average/Most frequent\"\n            }), \" replaces missing values (NaN) with the average (for continuous) or most frequent (for discrete) value.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Replace with random value\"\n            }), \" replaces missing values with random ones within the range of each variable.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Remove rows with missing values\"\n            }), \".\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select relevant features:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [\"Similar to \", _jsx(_components.a, {\n              href: \"../data/rank.md\",\n              children: \"Rank\"\n            }), \", this preprocessor outputs only the most informative features. Score can be determined by information gain, \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Information_gain_ratio\",\n              children: \"gain ratio\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Gini_coefficient\",\n              children: \"gini index\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Relief_(feature_selection)\",\n              children: \"ReliefF\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://www.aaai.org/Papers/ICML/2003/ICML03-111.pdf\",\n              children: \"fast correlation based filter\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\",\n              children: \"ANOVA\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Chi-squared_distribution\",\n              children: \"Chi2\"\n            }), \", \", _jsx(_components.a, {\n              href: \"http://lkm.fri.uni-lj.si/rmarko/papers/robnik03-mlj.pdf\",\n              children: \"RReliefF\"\n            }), \", and \", _jsx(_components.a, {\n              href: \"http://scikit-learn.org/stable/modules/feature_selection.html#feature-selection-using-selectfrommodel\",\n              children: \"Univariate Linear Regression\"\n            }), \".\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Strategy\"\n            }), \" refers to how many variables should be on the output. \", _jsx(_components.em, {\n              children: \"Fixed\"\n            }), \" returns a fixed number of top scored variables, while \", _jsx(_components.em, {\n              children: \"Percentile\"\n            }), \" return the selected top percent of the features.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Select random features\"\n        }), \" outputs either a fixed number of features from the original data or a percentage. This is mainly used for advanced testing and educational purposes.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess2.png\",\n      alt: \"\",\n      width: \"1006\",\n      height: \"882\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Normalize adjusts values to a common scale. Center values by mean or median or omit centering altogether. Similar for scaling, one can scale by SD (standard deviation), by span or not at all.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Randomize instances. Randomize classes shuffles class values and destroys connection between instances and class. Similarly, one can randomize features or meta data. If replicable shuffling is on, randomization results can be shared and repeated with a saved workflow. This is mainly used for advanced testing and educational purposes.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Remove sparse features\"\n        }), \" retains features that have more than a number/percentage of non-zero/missing values. The rest are discarded.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Principal component analysis outputs results of a PCA transformation. Similar to the \", _jsx(_components.a, {\n          href: \"/widget-catalog/transform/../unsupervised/PCA\",\n          children: \"PCA\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/CUR_matrix_approximation\",\n          children: \"CUR matrix decomposition\"\n        }), \" is a dimensionality reduction method, similar to SVD.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing for predictive modeling\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When building predictive models, one has to be careful about how to do preprocessing. There are two possible ways to do it in Orange, each slightly different:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Connect \", _jsx(_components.strong, {\n            children: \"Preprocess\"\n          }), \" to the learner. This will override the default preprocessing pipeline for the learner and apply only custom preprocessing pipeline (default preprocessing steps are described in each learner's documentation).\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Models1.png\",\n          alt: \"\",\n          width: \"514\",\n          height: \"200\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Connect \", _jsx(_components.strong, {\n            children: \"Preprocess\"\n          }), \" to Test and Score. This will apply the preprocessors to each batch within cross-validation. Then the learner's preprocessors will be applied to the preprocessed subset.\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Models2.png\",\n          alt: \"\",\n          width: \"516\",\n          height: \"226\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, there's a wrong way to do it. Connecting \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" directly to the original data and outputting preprocessed data set will likely overfit the model. Don't do it.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Models3.png\",\n      alt: \"\",\n      width: \"458\",\n      height: \"246\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example, we have used the \", _jsx(_components.em, {\n        children: \"heart_disease.tab\"\n      }), \" dataset available in the dropdown menu of the \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../data/file\",\n        children: \"File\"\n      }), \" widget. then we used \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" to impute missing values and normalize features. We can observe the changes in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../data/datatable\",\n        children: \"Data Table\"\n      }), \" and compare it to the non-processed data.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Example1.png\",\n      alt: \"\",\n      width: \"2842\",\n      height: \"1520\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we show how to use \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" for predictive modeling.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This time we are using the \", _jsx(_components.em, {\n        children: \"heart_disease.tab\"\n      }), \" data from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../data/file\",\n        children: \"File\"\n      }), \" widget. You can access the data in the dropdown menu. This is a dataset with 303 patients that came to the doctor suffering from a chest pain. After the tests were done, some patients were found to have diameter narrowing and others did not (this is our class variable).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Some values are missing in our data set, so we would like to impute missing values before evaluating the model. We do this by passing a preprocessor directly to \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../evaluate/testandscore\",\n        children: \"Test and Score\"\n      }), \". In \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \", we set the correct preprocessing pipeline (in our example only a single preprocessor with \", _jsx(_components.em, {\n        children: \"Impute missing values\"\n      }), \"), then connect it to the Preprocessor input of Test and Score.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We also pass the data and the learner (in this case, a \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../model/logisticregression\",\n        children: \"Logistic Regression\"\n      }), \"). This is the correct way to pass a preprocessor to cross-validation as each fold will independently get preprocessed in the training phase. This is particularly important for feature selection.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Example2.png\",\n      alt: \"\",\n      width: \"2614\",\n      height: \"1426\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File Import.png","url":"csvfileimport"},{"title":"Datasets","category":"Data","keywords":["datasets","online","data","sets"],"background":"#FFD39F","icon":"widget-icons/Data-Datasets.png","url":"datasets"},{"title":"SQL Table","category":"Data","keywords":["sql table","load"],"background":"#FFD39F","icon":"widget-icons/Data-SQL Table.png","url":"sqltable"},{"title":"Data Table","category":"Data","keywords":["data table","view"],"background":"#FFD39F","icon":"widget-icons/Data-Data Table.png","url":"datatable"},{"title":"Paint Data","category":"Data","keywords":["paint data","create","draw"],"background":"#FFD39F","icon":"widget-icons/Data-Paint Data.png","url":"paintdata"},{"title":"Data Info","category":"Data","keywords":["data info","information","inspect"],"background":"#FFD39F","icon":"widget-icons/Data-Data Info.png","url":"datainfo"},{"title":"Rank","category":"Data","keywords":["rank","filter"],"background":"#FFD39F","icon":"widget-icons/Data-Rank.png","url":"rank"},{"title":"Edit Domain","category":"Data","keywords":["edit domain","rename","drop","reorder","order"],"background":"#FFD39F","icon":"widget-icons/Data-Edit Domain.png","url":"editdomain"},{"title":"Color","category":"Data","keywords":[],"background":"#FFD39F","icon":"widget-icons/Data-Color.png","url":"color"},{"title":"Feature Statistics","category":"Data","keywords":[],"background":"#FFD39F","icon":"widget-icons/Data-Feature Statistics.png","url":"featurestatistics"},{"title":"Save Data","category":"Data","keywords":["save data","export"],"background":"#FFD39F","icon":"widget-icons/Data-Save Data.png","url":"save"}]],["Transform",[{"title":"Data Sampler","category":"Transform","keywords":["data sampler","random"],"background":"#FF9D5E","icon":"widget-icons/Transform-Data Sampler.png","url":"datasampler"},{"title":"Select Columns","category":"Transform","keywords":["select columns","filter","attributes","target","variable"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Columns.png","url":"selectcolumns"},{"title":"Select Rows","category":"Transform","keywords":["select rows","filter"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Rows.png","url":"selectrows"},{"title":"Transpose","category":"Transform","keywords":["transpose"],"background":"#FF9D5E","icon":"widget-icons/Transform-Transpose.png","url":"transpose"},{"title":"Merge Data","category":"Transform","keywords":["merge data","join"],"background":"#FF9D5E","icon":"widget-icons/Transform-Merge Data.png","url":"mergedata"},{"title":"Concatenate","category":"Transform","keywords":["concatenate","append","join","extend"],"background":"#FF9D5E","icon":"widget-icons/Transform-Concatenate.png","url":"concatenate"},{"title":"Select by Data Index","category":"Transform","keywords":[],"background":"#FF9D5E","icon":"widget-icons/Transform-Select by Data Index.png","url":"select-by-data-index"},{"title":"Unique","category":"Transform","keywords":[],"background":"#FF9D5E","icon":"widget-icons/Transform-Unique.png","url":"unique"},{"title":"Aggregate Columns","category":"Transform","keywords":["aggregate columns","aggregate","sum","product","max","min","mean","median","variance"],"background":"#FF9D5E","icon":"widget-icons/Transform-Aggregate Columns.png","url":"aggregatecolumns"},{"title":"Group by","category":"Transform","keywords":["aggregate","group by"],"background":"#FF9D5E","icon":"widget-icons/Transform-Group by.png","url":"groupby"},{"title":"Pivot Table","category":"Transform","keywords":["pivot table","pivot","group","aggregate"],"background":"#FF9D5E","icon":"widget-icons/Transform-Pivot Table.png","url":"pivot"},{"title":"Apply Domain","category":"Transform","keywords":["apply domain","transform"],"background":"#FF9D5E","icon":"widget-icons/Transform-Apply Domain.png","url":"applydomain"},{"title":"Preprocess","category":"Transform","keywords":["preprocess","process"],"background":"#FF9D5E","icon":"widget-icons/Transform-Preprocess.png","url":"preprocess"},{"title":"Impute","category":"Transform","keywords":["impute","substitute","missing"],"background":"#FF9D5E","icon":"widget-icons/Transform-Impute.png","url":"impute"},{"title":"Continuize","category":"Transform","keywords":["continuize","encode","dummy","numeric","one-hot","binary","treatment","contrast"],"background":"#FF9D5E","icon":"widget-icons/Transform-Continuize.png","url":"continuize"},{"title":"Discretize","category":"Transform","keywords":["discretize","bin","categorical","nominal","ordinal"],"background":"#FF9D5E","icon":"widget-icons/Transform-Discretize.png","url":"discretize"},{"title":"Randomize","category":"Transform","keywords":["randomize","random"],"background":"#FF9D5E","icon":"widget-icons/Transform-Randomize.png","url":"randomize"},{"title":"Purge Domain","category":"Transform","keywords":["remove","delete","unused"],"background":"#FF9D5E","icon":"widget-icons/Transform-Purge Domain.png","url":"purgedomain"},{"title":"Melt","category":"Transform","keywords":["melt","shopping list","wide","narrow"],"background":"#FF9D5E","icon":"widget-icons/Transform-Melt.png","url":"melt"},{"title":"Formula","category":"Transform","keywords":["feature constructor","function","lambda","calculation"],"background":"#FF9D5E","icon":"widget-icons/Transform-Formula.png","url":"formula"},{"title":"Create Class","category":"Transform","keywords":["create","class"],"background":"#FF9D5E","icon":"widget-icons/Transform-Create Class.png","url":"createclass"},{"title":"Create Instance","category":"Transform","keywords":["create instance","simulator"],"background":"#FF9D5E","icon":"widget-icons/Transform-Create Instance.png","url":"createinstance"},{"title":"Python Script","category":"Transform","keywords":["program","function"],"background":"#FF9D5E","icon":"widget-icons/Transform-Python Script.png","url":"pythonscript"}]],["Visualize",[{"title":"Tree Viewer","category":"Visualize","keywords":["tree","viewer"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Tree Viewer.png","url":"treeviewer"},{"title":"Box Plot","category":"Visualize","keywords":["box plot","whisker"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Box Plot.png","url":"boxplot"},{"title":"Violin Plot","category":"Visualize","keywords":["violin plot","kernel","density"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Violin Plot.png","url":"violinplot"},{"title":"Distributions","category":"Visualize","keywords":["distributions","histogram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Distributions.png","url":"distributions"},{"title":"Scatter Plot","category":"Visualize","keywords":["scatter","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Scatter Plot.png","url":"scatterplot"},{"title":"Line Plot","category":"Visualize","keywords":["line","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Line Plot.png","url":"lineplot"},{"title":"Bar Plot","category":"Visualize","keywords":["bar plot","chart"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Bar Plot.png","url":"barplot"},{"title":"Sieve Diagram","category":"Visualize","keywords":["sieve","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Sieve Diagram.png","url":"sievediagram"},{"title":"Mosaic Display","category":"Visualize","keywords":["mosaic","display"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Mosaic Display.png","url":"mosaicdisplay"},{"title":"FreeViz","category":"Visualize","keywords":["freeviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-FreeViz.png","url":"freeviz"},{"title":"Linear Projection","category":"Visualize","keywords":["linear","projection"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Linear Projection.png","url":"linearprojection"},{"title":"Radviz","category":"Visualize","keywords":["radviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Radviz.png","url":"radviz"},{"title":"Heat Map","category":"Visualize","keywords":["heat","map"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Heat Map.png","url":"heatmap"},{"title":"Venn Diagram","category":"Visualize","keywords":["venn","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Venn Diagram.png","url":"venndiagram"},{"title":"Silhouette Plot","category":"Visualize","keywords":["silhouette","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Silhouette Plot.png","url":"silhouetteplot"},{"title":"Pythagorean Tree","category":"Visualize","keywords":["pythagorean tree","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean Tree.png","url":"pythagoreantree"},{"title":"Pythagorean Forest","category":"Visualize","keywords":["pythagorean forest","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean Forest.png","url":"pythagoreanforest"},{"title":"CN2 Rule Viewer","category":"Visualize","keywords":["cn2","rule","viewer"],"background":"#FFB7B1","icon":"widget-icons/Visualize-CN2 Rule Viewer.png","url":"cn2ruleviewer"},{"title":"Nomogram","category":"Visualize","keywords":["nomogram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Nomogram.png","url":"nomogram"}]],["Model",[{"title":"Constant","category":"Model","keywords":["constant","majority","mean"],"background":"#FAC1D9","icon":"widget-icons/Model-Constant.png","url":"constant"},{"title":"CN2 Rule Induction","category":"Model","keywords":["cn2","rule","induction"],"background":"#FAC1D9","icon":"widget-icons/Model-CN2 Rule Induction.png","url":"cn2ruleinduction"},{"title":"Calibrated Learner","category":"Model","keywords":["calibrated learner","calibration","threshold"],"background":"#FAC1D9","icon":"widget-icons/Model-Calibrated Learner.png","url":"calibratedlearner"},{"title":"kNN","category":"Model","keywords":["knn","k nearest","knearest","neighbor","neighbour"],"background":"#FAC1D9","icon":"widget-icons/Model-kNN.png","url":"knn"},{"title":"Tree","category":"Model","keywords":["tree","classification tree"],"background":"#FAC1D9","icon":"widget-icons/Model-Tree.png","url":"tree"},{"title":"Random Forest","category":"Model","keywords":["random","forest"],"background":"#FAC1D9","icon":"widget-icons/Model-Random Forest.png","url":"randomforest"},{"title":"Gradient Boosting","category":"Model","keywords":["gradient boosting","catboost","gradient","boost","tree","forest","xgb","gb","extreme"],"background":"#FAC1D9","icon":"widget-icons/Model-Gradient Boosting.png","url":"gradientboosting"},{"title":"SVM","category":"Model","keywords":["svm","support vector machines"],"background":"#FAC1D9","icon":"widget-icons/Model-SVM.png","url":"svm"},{"title":"Linear Regression","category":"Model","keywords":["linear regression","ridge","lasso","elastic net"],"background":"#FAC1D9","icon":"widget-icons/Model-Linear Regression.png","url":"linearregression"},{"title":"Logistic Regression","category":"Model","keywords":["logistic","regression"],"background":"#FAC1D9","icon":"widget-icons/Model-Logistic Regression.png","url":"logisticregression"},{"title":"Naive Bayes","category":"Model","keywords":["naive","bayes"],"background":"#FAC1D9","icon":"widget-icons/Model-Naive Bayes.png","url":"naivebayes"},{"title":"AdaBoost","category":"Model","keywords":["adaboost","boost"],"background":"#FAC1D9","icon":"widget-icons/Model-AdaBoost.png","url":"adaboost"},{"title":"Curve Fit","category":"Model","keywords":["curve fit","function"],"background":"#FAC1D9","icon":"widget-icons/Model-Curve Fit.png","url":"curvefit"},{"title":"Neural Network","category":"Model","keywords":["neural network","mlp"],"background":"#FAC1D9","icon":"widget-icons/Model-Neural Network.png","url":"neuralnetwork"},{"title":"Stochastic Gradient Descent","category":"Model","keywords":["stochastic gradient descent","sgd"],"background":"#FAC1D9","icon":"widget-icons/Model-Stochastic Gradient Descent.png","url":"stochasticgradient"},{"title":"Stacking","category":"Model","keywords":[],"background":"#FAC1D9","icon":"widget-icons/Model-Stacking.png","url":"stacking"},{"title":"Save Model","category":"Model","keywords":["save model","save"],"background":"#FAC1D9","icon":"widget-icons/Model-Save Model.png","url":"savemodel"},{"title":"Load Model","category":"Model","keywords":["load model","file","open","model"],"background":"#FAC1D9","icon":"widget-icons/Model-Load Model.png","url":"loadmodel"}]],["Evaluate",[{"title":"Test and Score","category":"Evaluate","keywords":["test and score","cross validation","cv"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Test and Score.png","url":"testandscore"},{"title":"Predictions","category":"Evaluate","keywords":["predictions"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Predictions.png","url":"predictions"},{"title":"Confusion Matrix","category":"Evaluate","keywords":["confusion","matrix"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Confusion Matrix.png","url":"confusionmatrix"},{"title":"ROC Analysis","category":"Evaluate","keywords":["roc analysis","analyse"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-ROC Analysis.png","url":"rocanalysis"},{"title":"Performance Curve","category":"Evaluate","keywords":["performance curve","lift","cumulative gain","precision","recall","curve"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Performance Curve.png","url":"performancecurve"},{"title":"Calibration 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+<a href="/widget-catalog/transform/images/__optimized-images__/Preprocess-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2614" height="1426" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Preprocess-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Preprocess\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Preprocesses data with selected methods.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Preprocessor: preprocessing method\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Preprocessed Data: data preprocessed with selected methods\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Preprocessing is crucial for achieving better-quality analysis results. 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Score can be determined by information gain, \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Information_gain_ratio\",\n              children: \"gain ratio\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Gini_coefficient\",\n              children: \"gini index\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Relief_(feature_selection)\",\n              children: \"ReliefF\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://www.aaai.org/Papers/ICML/2003/ICML03-111.pdf\",\n              children: \"fast correlation based filter\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\",\n              children: \"ANOVA\"\n            }), \", \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Chi-squared_distribution\",\n              children: \"Chi2\"\n            }), \", \", _jsx(_components.a, {\n              href: \"http://lkm.fri.uni-lj.si/rmarko/papers/robnik03-mlj.pdf\",\n              children: \"RReliefF\"\n            }), \", and \", _jsx(_components.a, {\n              href: \"http://scikit-learn.org/stable/modules/feature_selection.html#feature-selection-using-selectfrommodel\",\n              children: \"Univariate Linear Regression\"\n            }), \".\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Strategy\"\n            }), \" refers to how many variables should be on the output. \", _jsx(_components.em, {\n              children: \"Fixed\"\n            }), \" returns a fixed number of top scored variables, while \", _jsx(_components.em, {\n              children: \"Percentile\"\n            }), \" return the selected top percent of the features.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Select random features\"\n        }), \" outputs either a fixed number of features from the original data or a percentage. 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This is mainly used for advanced testing and educational purposes.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Remove sparse features\"\n        }), \" retains features that have more than a number/percentage of non-zero/missing values. The rest are discarded.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Principal component analysis outputs results of a PCA transformation. Similar to the \", _jsx(_components.a, {\n          href: \"/widget-catalog/transform/../unsupervised/PCA\",\n          children: \"PCA\"\n        }), \" widget.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/CUR_matrix_approximation\",\n          children: \"CUR matrix decomposition\"\n        }), \" is a dimensionality reduction method, similar to SVD.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing for predictive modeling\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"When building predictive models, one has to be careful about how to do preprocessing. There are two possible ways to do it in Orange, each slightly different:\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Connect \", _jsx(_components.strong, {\n            children: \"Preprocess\"\n          }), \" to the learner. 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Then the learner's preprocessors will be applied to the preprocessed subset.\"]\n        }), \"\\n\", _jsx(_components.img, {\n          src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Models2.png\",\n          alt: \"\",\n          width: \"516\",\n          height: \"226\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Finally, there's a wrong way to do it. Connecting \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" directly to the original data and outputting preprocessed data set will likely overfit the model. Don't do it.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Models3.png\",\n      alt: \"\",\n      width: \"458\",\n      height: \"246\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the first example, we have used the \", _jsx(_components.em, {\n        children: \"heart_disease.tab\"\n      }), \" dataset available in the dropdown menu of the \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../data/file\",\n        children: \"File\"\n      }), \" widget. then we used \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \" to impute missing values and normalize features. 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This is a dataset with 303 patients that came to the doctor suffering from a chest pain. After the tests were done, some patients were found to have diameter narrowing and others did not (this is our class variable).\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Some values are missing in our data set, so we would like to impute missing values before evaluating the model. We do this by passing a preprocessor directly to \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../evaluate/testandscore\",\n        children: \"Test and Score\"\n      }), \". In \", _jsx(_components.strong, {\n        children: \"Preprocess\"\n      }), \", we set the correct preprocessing pipeline (in our example only a single preprocessor with \", _jsx(_components.em, {\n        children: \"Impute missing values\"\n      }), \"), then connect it to the Preprocessor input of Test and Score.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We also pass the data and the learner (in this case, a \", _jsx(_components.a, {\n        href: \"/widget-catalog/transform/../model/logisticregression\",\n        children: \"Logistic Regression\"\n      }), \"). This is the correct way to pass a preprocessor to cross-validation as each fold will independently get preprocessed in the training phase. This is particularly important for feature selection.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Preprocess-Example2.png\",\n      alt: \"\",\n      width: \"2614\",\n      height: \"1426\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Purge Domain</h1>
 <p>Removes unused attribute values and useless attributes, sorts the remaining values.</p>
 <p><strong>Inputs</strong></p>
@@ -180,4 +180,4 @@
 <h2>Example</h2>
 <p>The <strong>Purge Domain</strong> widget would typically appear after data filtering, for instance when selecting a subset of visualized examples.</p>
 <p>In the above schema, we play with the <em>adult.tab</em> dataset: we visualize it and select a portion of the data, which contains only four out of the five original classes. To get rid of the empty class, we put the data through <strong>Purge Domain</strong> before going on to the <a href="/widget-catalog/transform/../visualize/boxplot">Box Plot</a> widget. The latter shows only the four classes which are in the <strong>Purge Data</strong> output. To see the effect of data purification, uncheck <em>Remove unused class variable values</em> and observe the effect this has on <a href="/widget-catalog/transform/../visualize/boxplot">Box Plot</a>.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/PurgeDomain-example.png" data-gallery="true"><img alt="" loading="lazy" width="1667" height="1070" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/PurgeDomain-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Purge Domain\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Removes unused attribute values and useless attributes, sorts the remaining values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: filtered dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Definitions of nominal attributes sometimes contain values which don’t appear in the data. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 <p>Extends functionalities through Python scripting.</p>
 <p><strong>Inputs</strong></p>
@@ -221,4 +221,4 @@ <h2>Examples</h2>
 out_object.store_tokens(tokens)
 </code></pre>
 <p>You can add a lot of other preprocessing steps to further adjust the output. The output of <strong>Python Script</strong> can be used with any widget that accepts the type of output your script produces. In this case, connection is green, which signalizes the right type of input for Word Cloud widget.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/PythonScript-Example3.png" data-gallery="true"><img alt="" loading="lazy" width="1550" height="809" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/PythonScript-Example3.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    code: \"code\",\n    pre: \"pre\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Python Script\"\n    }), \"\\n\", _jsx(_components.p, {\n      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Pressing \\\"+\\\" will add a new entry and open it in the \", _jsx(_components.em, {\n          children: \"Python script\"\n        }), \" editor. When the script is modified, its entry in the \", _jsx(_components.em, {\n          children: \"Library\"\n        }), \" will change to indicate it has unsaved changes. Pressing \", _jsx(_components.em, {\n          children: \"Update\"\n        }), \" will save the script (keyboard shortcut \\\"Ctrl+S\\\"). A script can be removed by selecting it and pressing the \\\"-\\\" button.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Pressing \", _jsx(_components.em, {\n          children: \"Execute\"\n        }), \" in the \", _jsx(_components.em, {\n          children: \"Run\"\n        }), \" box executes the script (keyboard shortcut \\\"Ctrl+R\\\"). Any script output (from \", _jsx(_components.code, {\n          children: \"print\"\n        }), \") is captured and displayed in the \", _jsx(_components.em, {\n          children: \"Console\"\n        }), \" below the script.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The \", _jsx(_components.em, {\n          children: \"Python script\"\n        }), \" editor on the left can be used to edit a script (it supports some rudimentary syntax highlighting).\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Console displays the output of the script.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Python Script widget is intended to extend functionalities for advanced users. Classes from Orange library are described in the \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/#reference\",\n        children: \"documentation\"\n      }), \". To find further information about orange Table class see \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/reference/data.table.html\",\n        children: \"Table\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/reference/data.domain.html\",\n        children: \"Domain\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/reference/data.variable.html\",\n        children: \"Variable\"\n      }), \" documentation.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One can, for example, do batch filtering by attributes. We used zoo.tab for the example and we filtered out all the attributes that have more than 5 discrete values. This in our case removed only 'leg' attribute, but imagine an example where one would have many such attributes.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"from Orange.data import Domain, Table\\ndomain = Domain([attr for attr in in_data.domain.attributes\\n                 if attr.is_continuous or len(attr.values) \u003c= 5],\\n                in_data.domain.class_vars)\\nout_data = Table(domain, in_data)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-filtering.png\",\n      alt: \"\",\n      width: \"832\",\n      height: \"714\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The second example shows how to round all the values in a few lines of code. This time we used wine.tab and rounded all the values to whole numbers.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"import numpy as np\\nout_data = in_data.copy()\\n#copy, otherwise input data will be overwritten\\nnp.round(out_data.X, 0, out_data.X)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-round.png\",\n      alt: \"\",\n      width: \"834\",\n      height: \"702\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The third example introduces some Gaussian noise to the data. Again we make a copy of the input data, then walk through all the values with a double for loop and add random noise.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"import random\\nfrom Orange.data import Domain, Table\\nnew_data = in_data.copy()\\nfor inst in new_data:\\n  for f in inst.domain.attributes:\\n    inst[f] += random.gauss(0, 0.02)\\nout_data = new_data\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-gauss.png\",\n      alt: \"\",\n      width: \"729\",\n      height: \"627\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The final example uses Orange3-Text add-on. \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" is very useful for custom preprocessing in text mining, extracting new features from strings, or utilizing advanced \", _jsx(_components.em, {\n        children: \"nltk\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"gensim\"\n      }), \" functions. Below, we simply tokenized our input data from \", _jsx(_components.em, {\n        children: \"deerwester.tab\"\n      }), \" by splitting them by whitespace.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"print('Running Preprocessing ...')\\ntokens = [doc.split(' ') for doc in in_data.documents]\\nprint('Tokens:', tokens)\\nout_object = in_data\\nout_object.store_tokens(tokens)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can add a lot of other preprocessing steps to further adjust the output. The output of \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" can be used with any widget that accepts the type of output your script produces. In this case, connection is green, which signalizes the right type of input for Word Cloud widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-Example3.png\",\n      alt: \"\",\n      width: \"1550\",\n      height: \"809\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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Index","category":"Transform","keywords":[],"background":"#FF9D5E","icon":"widget-icons/Transform-Select by Data Index.png","url":"select-by-data-index"},{"title":"Unique","category":"Transform","keywords":[],"background":"#FF9D5E","icon":"widget-icons/Transform-Unique.png","url":"unique"},{"title":"Aggregate Columns","category":"Transform","keywords":["aggregate columns","aggregate","sum","product","max","min","mean","median","variance"],"background":"#FF9D5E","icon":"widget-icons/Transform-Aggregate Columns.png","url":"aggregatecolumns"},{"title":"Group by","category":"Transform","keywords":["aggregate","group by"],"background":"#FF9D5E","icon":"widget-icons/Transform-Group by.png","url":"groupby"},{"title":"Pivot Table","category":"Transform","keywords":["pivot table","pivot","group","aggregate"],"background":"#FF9D5E","icon":"widget-icons/Transform-Pivot Table.png","url":"pivot"},{"title":"Apply Domain","category":"Transform","keywords":["apply 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Projection","category":"Visualize","keywords":["linear","projection"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Linear Projection.png","url":"linearprojection"},{"title":"Radviz","category":"Visualize","keywords":["radviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Radviz.png","url":"radviz"},{"title":"Heat Map","category":"Visualize","keywords":["heat","map"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Heat Map.png","url":"heatmap"},{"title":"Venn Diagram","category":"Visualize","keywords":["venn","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Venn Diagram.png","url":"venndiagram"},{"title":"Silhouette Plot","category":"Visualize","keywords":["silhouette","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Silhouette Plot.png","url":"silhouetteplot"},{"title":"Pythagorean Tree","category":"Visualize","keywords":["pythagorean tree","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean 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The widget can be further connected to other widgets for visualizing the output.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"For instance the following script would simply pass on all signals it receives:\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"out_data = in_data\\nout_distance = in_distance\\nout_learner = in_learner\\nout_classifier = in_classifier\\nout_object = in_object\\n\"\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Note: You should not modify the input objects in place.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-stamped.png\",\n      alt: \"\",\n      width: \"797\",\n      height: \"595\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Info box contains names of basic operators for Orange Python script.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The \", _jsx(_components.em, {\n          children: \"Library\"\n        }), \" control can be used to manage multiple scripts. Pressing \\\"+\\\" will add a new entry and open it in the \", _jsx(_components.em, {\n          children: \"Python script\"\n        }), \" editor. When the script is modified, its entry in the \", _jsx(_components.em, {\n          children: \"Library\"\n        }), \" will change to indicate it has unsaved changes. Pressing \", _jsx(_components.em, {\n          children: \"Update\"\n        }), \" will save the script (keyboard shortcut \\\"Ctrl+S\\\"). A script can be removed by selecting it and pressing the \\\"-\\\" button.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Pressing \", _jsx(_components.em, {\n          children: \"Execute\"\n        }), \" in the \", _jsx(_components.em, {\n          children: \"Run\"\n        }), \" box executes the script (keyboard shortcut \\\"Ctrl+R\\\"). Any script output (from \", _jsx(_components.code, {\n          children: \"print\"\n        }), \") is captured and displayed in the \", _jsx(_components.em, {\n          children: \"Console\"\n        }), \" below the script.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The \", _jsx(_components.em, {\n          children: \"Python script\"\n        }), \" editor on the left can be used to edit a script (it supports some rudimentary syntax highlighting).\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Console displays the output of the script.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Python Script widget is intended to extend functionalities for advanced users. Classes from Orange library are described in the \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/#reference\",\n        children: \"documentation\"\n      }), \". To find further information about orange Table class see \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/reference/data.table.html\",\n        children: \"Table\"\n      }), \", \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/reference/data.domain.html\",\n        children: \"Domain\"\n      }), \", and \", _jsx(_components.a, {\n        href: \"https://docs.biolab.si/3/data-mining-library/reference/data.variable.html\",\n        children: \"Variable\"\n      }), \" documentation.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"One can, for example, do batch filtering by attributes. We used zoo.tab for the example and we filtered out all the attributes that have more than 5 discrete values. This in our case removed only 'leg' attribute, but imagine an example where one would have many such attributes.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"from Orange.data import Domain, Table\\ndomain = Domain([attr for attr in in_data.domain.attributes\\n                 if attr.is_continuous or len(attr.values) \u003c= 5],\\n                in_data.domain.class_vars)\\nout_data = Table(domain, in_data)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-filtering.png\",\n      alt: \"\",\n      width: \"832\",\n      height: \"714\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The second example shows how to round all the values in a few lines of code. This time we used wine.tab and rounded all the values to whole numbers.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"import numpy as np\\nout_data = in_data.copy()\\n#copy, otherwise input data will be overwritten\\nnp.round(out_data.X, 0, out_data.X)\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-round.png\",\n      alt: \"\",\n      width: \"834\",\n      height: \"702\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The third example introduces some Gaussian noise to the data. Again we make a copy of the input data, then walk through all the values with a double for loop and add random noise.\"\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"import random\\nfrom Orange.data import Domain, Table\\nnew_data = in_data.copy()\\nfor inst in new_data:\\n  for f in inst.domain.attributes:\\n    inst[f] += random.gauss(0, 0.02)\\nout_data = new_data\\n\"\n      })\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-gauss.png\",\n      alt: \"\",\n      width: \"729\",\n      height: \"627\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The final example uses Orange3-Text add-on. \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" is very useful for custom preprocessing in text mining, extracting new features from strings, or utilizing advanced \", _jsx(_components.em, {\n        children: \"nltk\"\n      }), \" or \", _jsx(_components.em, {\n        children: \"gensim\"\n      }), \" functions. Below, we simply tokenized our input data from \", _jsx(_components.em, {\n        children: \"deerwester.tab\"\n      }), \" by splitting them by whitespace.\"]\n    }), \"\\n\", _jsx(_components.pre, {\n      children: _jsx(_components.code, {\n        children: \"print('Running Preprocessing ...')\\ntokens = [doc.split(' ') for doc in in_data.documents]\\nprint('Tokens:', tokens)\\nout_object = in_data\\nout_object.store_tokens(tokens)\\n\"\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"You can add a lot of other preprocessing steps to further adjust the output. The output of \", _jsx(_components.strong, {\n        children: \"Python Script\"\n      }), \" can be used with any widget that accepts the type of output your script produces. In this case, connection is green, which signalizes the right type of input for Word Cloud widget.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/PythonScript-Example3.png\",\n      alt: \"\",\n      width: \"1550\",\n      height: \"809\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File Import.png","url":"csvfileimport"},{"title":"Datasets","category":"Data","keywords":["datasets","online","data","sets"],"background":"#FFD39F","icon":"widget-icons/Data-Datasets.png","url":"datasets"},{"title":"SQL Table","category":"Data","keywords":["sql table","load"],"background":"#FFD39F","icon":"widget-icons/Data-SQL Table.png","url":"sqltable"},{"title":"Data Table","category":"Data","keywords":["data table","view"],"background":"#FFD39F","icon":"widget-icons/Data-Data Table.png","url":"datatable"},{"title":"Paint Data","category":"Data","keywords":["paint data","create","draw"],"background":"#FFD39F","icon":"widget-icons/Data-Paint Data.png","url":"paintdata"},{"title":"Data Info","category":"Data","keywords":["data info","information","inspect"],"background":"#FFD39F","icon":"widget-icons/Data-Data Info.png","url":"datainfo"},{"title":"Rank","category":"Data","keywords":["rank","filter"],"background":"#FFD39F","icon":"widget-icons/Data-Rank.png","url":"rank"},{"title":"Edit Domain","category":"Data","keywords":["edit domain","rename","drop","reorder","order"],"background":"#FFD39F","icon":"widget-icons/Data-Edit Domain.png","url":"editdomain"},{"title":"Color","category":"Data","keywords":[],"background":"#FFD39F","icon":"widget-icons/Data-Color.png","url":"color"},{"title":"Feature Statistics","category":"Data","keywords":[],"background":"#FFD39F","icon":"widget-icons/Data-Feature Statistics.png","url":"featurestatistics"},{"title":"Save Data","category":"Data","keywords":["save data","export"],"background":"#FFD39F","icon":"widget-icons/Data-Save Data.png","url":"save"}]],["Transform",[{"title":"Data Sampler","category":"Transform","keywords":["data sampler","random"],"background":"#FF9D5E","icon":"widget-icons/Transform-Data Sampler.png","url":"datasampler"},{"title":"Select Columns","category":"Transform","keywords":["select columns","filter","attributes","target","variable"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Columns.png","url":"selectcolumns"},{"title":"Select Rows","category":"Transform","keywords":["select rows","filter"],"background":"#FF9D5E","icon":"widget-icons/Transform-Select Rows.png","url":"selectrows"},{"title":"Transpose","category":"Transform","keywords":["transpose"],"background":"#FF9D5E","icon":"widget-icons/Transform-Transpose.png","url":"transpose"},{"title":"Merge Data","category":"Transform","keywords":["merge data","join"],"background":"#FF9D5E","icon":"widget-icons/Transform-Merge Data.png","url":"mergedata"},{"title":"Concatenate","category":"Transform","keywords":["concatenate","append","join","extend"],"background":"#FF9D5E","icon":"widget-icons/Transform-Concatenate.png","url":"concatenate"},{"title":"Select by Data Index","category":"Transform","keywords":[],"background":"#FF9D5E","icon":"widget-icons/Transform-Select by Data Index.png","url":"select-by-data-index"},{"title":"Unique","category":"Transform","keywords":[],"background":"#FF9D5E","icon":"widget-icons/Transform-Unique.png","url":"unique"},{"title":"Aggregate Columns","category":"Transform","keywords":["aggregate columns","aggregate","sum","product","max","min","mean","median","variance"],"background":"#FF9D5E","icon":"widget-icons/Transform-Aggregate Columns.png","url":"aggregatecolumns"},{"title":"Group by","category":"Transform","keywords":["aggregate","group by"],"background":"#FF9D5E","icon":"widget-icons/Transform-Group by.png","url":"groupby"},{"title":"Pivot Table","category":"Transform","keywords":["pivot table","pivot","group","aggregate"],"background":"#FF9D5E","icon":"widget-icons/Transform-Pivot Table.png","url":"pivot"},{"title":"Apply Domain","category":"Transform","keywords":["apply domain","transform"],"background":"#FF9D5E","icon":"widget-icons/Transform-Apply Domain.png","url":"applydomain"},{"title":"Preprocess","category":"Transform","keywords":["preprocess","process"],"background":"#FF9D5E","icon":"widget-icons/Transform-Preprocess.png","url":"preprocess"},{"title":"Impute","category":"Transform","keywords":["impute","substitute","missing"],"background":"#FF9D5E","icon":"widget-icons/Transform-Impute.png","url":"impute"},{"title":"Continuize","category":"Transform","keywords":["continuize","encode","dummy","numeric","one-hot","binary","treatment","contrast"],"background":"#FF9D5E","icon":"widget-icons/Transform-Continuize.png","url":"continuize"},{"title":"Discretize","category":"Transform","keywords":["discretize","bin","categorical","nominal","ordinal"],"background":"#FF9D5E","icon":"widget-icons/Transform-Discretize.png","url":"discretize"},{"title":"Randomize","category":"Transform","keywords":["randomize","random"],"background":"#FF9D5E","icon":"widget-icons/Transform-Randomize.png","url":"randomize"},{"title":"Purge Domain","category":"Transform","keywords":["remove","delete","unused"],"background":"#FF9D5E","icon":"widget-icons/Transform-Purge Domain.png","url":"purgedomain"},{"title":"Melt","category":"Transform","keywords":["melt","shopping list","wide","narrow"],"background":"#FF9D5E","icon":"widget-icons/Transform-Melt.png","url":"melt"},{"title":"Formula","category":"Transform","keywords":["feature constructor","function","lambda","calculation"],"background":"#FF9D5E","icon":"widget-icons/Transform-Formula.png","url":"formula"},{"title":"Create Class","category":"Transform","keywords":["create","class"],"background":"#FF9D5E","icon":"widget-icons/Transform-Create Class.png","url":"createclass"},{"title":"Create Instance","category":"Transform","keywords":["create instance","simulator"],"background":"#FF9D5E","icon":"widget-icons/Transform-Create Instance.png","url":"createinstance"},{"title":"Python Script","category":"Transform","keywords":["program","function"],"background":"#FF9D5E","icon":"widget-icons/Transform-Python Script.png","url":"pythonscript"}]],["Visualize",[{"title":"Tree Viewer","category":"Visualize","keywords":["tree","viewer"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Tree Viewer.png","url":"treeviewer"},{"title":"Box Plot","category":"Visualize","keywords":["box plot","whisker"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Box Plot.png","url":"boxplot"},{"title":"Violin Plot","category":"Visualize","keywords":["violin plot","kernel","density"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Violin Plot.png","url":"violinplot"},{"title":"Distributions","category":"Visualize","keywords":["distributions","histogram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Distributions.png","url":"distributions"},{"title":"Scatter Plot","category":"Visualize","keywords":["scatter","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Scatter Plot.png","url":"scatterplot"},{"title":"Line Plot","category":"Visualize","keywords":["line","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Line Plot.png","url":"lineplot"},{"title":"Bar Plot","category":"Visualize","keywords":["bar plot","chart"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Bar Plot.png","url":"barplot"},{"title":"Sieve Diagram","category":"Visualize","keywords":["sieve","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Sieve Diagram.png","url":"sievediagram"},{"title":"Mosaic Display","category":"Visualize","keywords":["mosaic","display"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Mosaic Display.png","url":"mosaicdisplay"},{"title":"FreeViz","category":"Visualize","keywords":["freeviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-FreeViz.png","url":"freeviz"},{"title":"Linear Projection","category":"Visualize","keywords":["linear","projection"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Linear Projection.png","url":"linearprojection"},{"title":"Radviz","category":"Visualize","keywords":["radviz","viz"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Radviz.png","url":"radviz"},{"title":"Heat Map","category":"Visualize","keywords":["heat","map"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Heat Map.png","url":"heatmap"},{"title":"Venn Diagram","category":"Visualize","keywords":["venn","diagram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Venn Diagram.png","url":"venndiagram"},{"title":"Silhouette Plot","category":"Visualize","keywords":["silhouette","plot"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Silhouette Plot.png","url":"silhouetteplot"},{"title":"Pythagorean Tree","category":"Visualize","keywords":["pythagorean tree","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean Tree.png","url":"pythagoreantree"},{"title":"Pythagorean Forest","category":"Visualize","keywords":["pythagorean forest","fractal"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Pythagorean Forest.png","url":"pythagoreanforest"},{"title":"CN2 Rule Viewer","category":"Visualize","keywords":["cn2","rule","viewer"],"background":"#FFB7B1","icon":"widget-icons/Visualize-CN2 Rule Viewer.png","url":"cn2ruleviewer"},{"title":"Nomogram","category":"Visualize","keywords":["nomogram"],"background":"#FFB7B1","icon":"widget-icons/Visualize-Nomogram.png","url":"nomogram"}]],["Model",[{"title":"Constant","category":"Model","keywords":["constant","majority","mean"],"background":"#FAC1D9","icon":"widget-icons/Model-Constant.png","url":"constant"},{"title":"CN2 Rule Induction","category":"Model","keywords":["cn2","rule","induction"],"background":"#FAC1D9","icon":"widget-icons/Model-CN2 Rule Induction.png","url":"cn2ruleinduction"},{"title":"Calibrated Learner","category":"Model","keywords":["calibrated learner","calibration","threshold"],"background":"#FAC1D9","icon":"widget-icons/Model-Calibrated Learner.png","url":"calibratedlearner"},{"title":"kNN","category":"Model","keywords":["knn","k nearest","knearest","neighbor","neighbour"],"background":"#FAC1D9","icon":"widget-icons/Model-kNN.png","url":"knn"},{"title":"Tree","category":"Model","keywords":["tree","classification tree"],"background":"#FAC1D9","icon":"widget-icons/Model-Tree.png","url":"tree"},{"title":"Random Forest","category":"Model","keywords":["random","forest"],"background":"#FAC1D9","icon":"widget-icons/Model-Random Forest.png","url":"randomforest"},{"title":"Gradient Boosting","category":"Model","keywords":["gradient boosting","catboost","gradient","boost","tree","forest","xgb","gb","extreme"],"background":"#FAC1D9","icon":"widget-icons/Model-Gradient Boosting.png","url":"gradientboosting"},{"title":"SVM","category":"Model","keywords":["svm","support vector machines"],"background":"#FAC1D9","icon":"widget-icons/Model-SVM.png","url":"svm"},{"title":"Linear Regression","category":"Model","keywords":["linear regression","ridge","lasso","elastic net"],"background":"#FAC1D9","icon":"widget-icons/Model-Linear Regression.png","url":"linearregression"},{"title":"Logistic Regression","category":"Model","keywords":["logistic","regression"],"background":"#FAC1D9","icon":"widget-icons/Model-Logistic Regression.png","url":"logisticregression"},{"title":"Naive Bayes","category":"Model","keywords":["naive","bayes"],"background":"#FAC1D9","icon":"widget-icons/Model-Naive Bayes.png","url":"naivebayes"},{"title":"AdaBoost","category":"Model","keywords":["adaboost","boost"],"background":"#FAC1D9","icon":"widget-icons/Model-AdaBoost.png","url":"adaboost"},{"title":"Curve Fit","category":"Model","keywords":["curve fit","function"],"background":"#FAC1D9","icon":"widget-icons/Model-Curve Fit.png","url":"curvefit"},{"title":"Neural Network","category":"Model","keywords":["neural network","mlp"],"background":"#FAC1D9","icon":"widget-icons/Model-Neural Network.png","url":"neuralnetwork"},{"title":"Stochastic Gradient Descent","category":"Model","keywords":["stochastic gradient descent","sgd"],"background":"#FAC1D9","icon":"widget-icons/Model-Stochastic Gradient Descent.png","url":"stochasticgradient"},{"title":"Stacking","category":"Model","keywords":[],"background":"#FAC1D9","icon":"widget-icons/Model-Stacking.png","url":"stacking"},{"title":"Save Model","category":"Model","keywords":["save model","save"],"background":"#FAC1D9","icon":"widget-icons/Model-Save Model.png","url":"savemodel"},{"title":"Load Model","category":"Model","keywords":["load model","file","open","model"],"background":"#FAC1D9","icon":"widget-icons/Model-Load Model.png","url":"loadmodel"}]],["Evaluate",[{"title":"Test and Score","category":"Evaluate","keywords":["test and score","cross validation","cv"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Test and Score.png","url":"testandscore"},{"title":"Predictions","category":"Evaluate","keywords":["predictions"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Predictions.png","url":"predictions"},{"title":"Confusion Matrix","category":"Evaluate","keywords":["confusion","matrix"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Confusion Matrix.png","url":"confusionmatrix"},{"title":"ROC Analysis","category":"Evaluate","keywords":["roc analysis","analyse"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-ROC Analysis.png","url":"rocanalysis"},{"title":"Performance Curve","category":"Evaluate","keywords":["performance curve","lift","cumulative gain","precision","recall","curve"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Performance Curve.png","url":"performancecurve"},{"title":"Calibration Plot","category":"Evaluate","keywords":["calibration","plot"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Calibration Plot.png","url":"calibrationplot"}]],["Unsupervised",[{"title":"Distance File","category":"Unsupervised","keywords":["distance file","load","read","open"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance File.png","url":"distancefile"},{"title":"Distance Matrix","category":"Unsupervised","keywords":["distance","matrix"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Matrix.png","url":"distancematrix"},{"title":"t-SNE","category":"Unsupervised","keywords":["t-sne","tsne"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-t-SNE.png","url":"tsne"},{"title":"Correlations","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correlations.png","url":"correlations"},{"title":"Distance Map","category":"Unsupervised","keywords":["distance","map"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Map.png","url":"distancemap"},{"title":"Hierarchical Clustering","category":"Unsupervised","keywords":["hierarchical","clustering"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Hierarchical Clustering.png","url":"hierarchicalclustering"},{"title":"k-Means","category":"Unsupervised","keywords":["k-means","kmeans","clustering"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-k-Means.png","url":"kmeans"},{"title":"Louvain Clustering","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Louvain Clustering.png","url":"louvainclustering"},{"title":"DBSCAN","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-DBSCAN.png","url":"DBSCAN"},{"title":"Manifold Learning","category":"Unsupervised","keywords":["manifold","learning"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Manifold Learning.png","url":"manifoldlearning"},{"title":"Outliers","category":"Unsupervised","keywords":["outliers","inlier"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Outliers.png","url":"outliers"},{"title":"PCA","category":"Unsupervised","keywords":["pca","principal component analysis","linear transformation"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-PCA.png","url":"PCA"},{"title":"Neighbors","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Neighbors.png","url":"neighbors"},{"title":"Correspondence Analysis","category":"Unsupervised","keywords":["correspondence","analysis"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correspondence Analysis.png","url":"correspondenceanalysis"},{"title":"Distances","category":"Unsupervised","keywords":["distances"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distances.png","url":"distances"},{"title":"Distance Transformation","category":"Unsupervised","keywords":["distance","transformation"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Transformation.png","url":"distancetransformation"},{"title":"MDS","category":"Unsupervised","keywords":["mds","multidimensional scaling","multi dimensional scaling"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-MDS.png","url":"mds"},{"title":"Save Distance Matrix","category":"Unsupervised","keywords":["save distance matrix","distance matrix","save"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Save Distance Matrix.png","url":"savedistancematrix"},{"title":"Self-Organizing Map","category":"Unsupervised","keywords":["self-organizing 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Randomize</h1>
 <p>Shuffles classes, attributes and/or metas of an input dataset.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@ <h2>Example</h2>
 <p>The <strong>Randomize</strong> widget is usually placed right after (e.g. <a href="/widget-catalog/transform/../data/file">File</a> widget. The basic usage is shown in the following workflow, where values of class variable of Iris dataset are randomly shuffled.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/Randomize-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1292" height="906" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Randomize-Example1.png"/></a>
 <p>In the next example we show how shuffling class values influences model performance on the same dataset as above.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Randomize-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1208" height="803" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Randomize-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Randomize\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shuffles classes, attributes and/or metas of an input dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: randomized dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Randomize\"\n      }), \" widget receives a dataset in the input and outputs the same dataset in which the classes, attributes or/and metas are shuffled.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/Randomize-Default.png\",\n      alt: \"\",\n      width: \"254\",\n      height: \"276\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select group of columns of the dataset you want to shuffle.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Select proportion of the dataset you want to shuffle.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce replicable output.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Apply automatically\"\n        }), \" is ticked, changes are committed automatically. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Select by Data Index</h1>
 <p>Match instances by index from data subset.</p>
 <p><strong>Inputs</strong></p>
@@ -175,4 +175,4 @@ <h2>Example</h2>
 <p>A typical use of <strong>Select by Data Index</strong> is to retrieve the original data after a transformation. We will load <em>iris.tab</em> data in the <a href="../data/file.md">File</a> widget. Then we will transform this data with <a href="../unsupervised/PCA.md">PCA</a>. We can project the transformed data in a <a href="/widget-catalog/transform/../visualize/scatterplot">Scatter Plot</a>, where we can only see PCA components and not the original features.</p>
 <p>Now we will select an interesting subset (we could also select the entire data set). If we observe it in a <a href="/widget-catalog/transform/../data/datatable">Data Table</a>, we can see that the data is transformed. If we would like to see this data with the original features, we will have to retrieve them with <strong>Select by Data Index</strong>.</p>
 <p>Connect the original data and the subset from <a href="/widget-catalog/transform/../visualize/scatterplot">Scatter Plot</a> to <strong>Select by Data Index</strong>. The widget will match the indices of the subset with the indices of the reference (original) data and output the matching reference data. A final inspection in another <a href="/widget-catalog/transform/../data/datatable">Data Table</a> confirms the data on the output is from the original data space.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/Select-by-Data-Index-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="2688" height="1884" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/Select-by-Data-Index-Example1.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Select by Data Index\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Match instances by index from data subset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: reference data set\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset to match\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matching data: subset from reference data set that matches indices from subset data\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Unmatched data: subset from reference data set that does not match indices from subset data\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Annotated data: reference data set with an additional column defining matches\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Select by Data Index\"\n      }), \" enables matching the data by indices. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Select Columns</h1>
 <p>Manual selection of data attributes and composition of data domain.</p>
 <p><strong>Inputs</strong></p>
@@ -179,4 +179,4 @@ <h2>Examples</h2>
 <p>In the workflow below, the <em>Iris</em> data from the <a href="../data/file.md">File</a> widget is fed into the <strong>Select Columns</strong> widget, where we select to output only two attributes (namely petal width and petal length). We view both the original dataset and the dataset with selected columns in the <a href="/widget-catalog/transform/../data/datatable">Data Table</a> widget.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/SelectColumns-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1327" height="908" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/SelectColumns-Example1.png"/></a>
 <p>For a more complex use of the widget, we composed a workflow to redefine the classification problem in the <em>heart-disease</em> dataset. Originally, the task was to predict if the patient has a coronary artery diameter narrowing. We changed the problem to that of gender classification, based on age, chest pain and cholesterol level, and informatively kept the diameter narrowing as a meta attribute.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/SelectColumns-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1469" height="945" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/SelectColumns-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Select Columns\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Manual selection of data attributes and composition of data domain.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with columns as set in the widget\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Select Columns\"\n      }), \" widget is used to manually compose your \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Data_domain\",\n        children: \"data domain\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Select Rows</h1>
 <p>Selects data instances based on conditions over data features.</p>
 <p><strong>Inputs</strong></p>
@@ -184,4 +184,4 @@ <h2>Example</h2>
 <p>In the workflow below, we used the <em>Zoo</em> data from the <a href="../data/file.md">File</a> widget and fed it into the <strong>Select Rows</strong> widget. In the widget, we chose to output only two animal types, namely fish and reptiles. We can inspect both the original dataset and the dataset with selected rows in the <a href="/widget-catalog/transform/../data/datatable">Data Table</a> widget.</p>
 <a href="/widget-catalog/transform/images/__optimized-images__/SelectRows-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1494" height="1062" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/SelectRows-Example.png"/></a>
 <p>In the next example, we used the data from the <em>Titanic</em> dataset and similarly fed it into the <a href="/widget-catalog/transform/../visualize/boxplot">Box Plot</a> widget. We first observed the entire dataset based on survival. Then we selected only first class passengers in the <strong>Select Rows</strong> widget and fed it again into the <a href="/widget-catalog/transform/../visualize/boxplot">Box Plot</a>. There we could see all the first class passengers listed by their survival rate and grouped by gender.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/SelectRows-Workflow.png" data-gallery="true"><img alt="" loading="lazy" width="1373" height="835" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/SelectRows-Workflow.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Select Rows\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Selects data instances based on conditions over data features.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Matching Data: instances that match the conditions\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Non-Matching Data: instances that do not match the conditions\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a instance is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget selects a subset from an input dataset, based on user-defined conditions. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Transpose</h1>
 <p>Transposes a data table.</p>
 <p><strong>Inputs</strong></p>
@@ -166,4 +166,4 @@
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 <h2>Example</h2>
 <p>This is a simple workflow showing how to use <strong>Transpose</strong>. Connect the widget to <a href="../data/file.md">File</a> widget. The output of <strong>Transpose</strong> is a transposed data table with rows as columns and columns as rows. You can observe the result in a <a href="/widget-catalog/transform/../data/datatable">Data Table</a>.</p>
-<a href="/widget-catalog/transform/images/__optimized-images__/transpose-example.png" data-gallery="true"><img alt="" loading="lazy" width="928" height="644" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/transform/images/__optimized-images__/transpose-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Transpose\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Transposes a data table.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: transposed dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Transpose\"\n      }), \" widget transposes data table.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/transform/images/__optimized-images__/transpose-stamped.png\",\n      alt: \"\",\n      width: \"163\",\n      height: \"194\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a simple workflow showing how to use \", _jsx(_components.strong, {\n        children: \"Transpose\"\n      }), \". 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/datasampler/">Data Sampler</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectcolumns/">Select Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/selectrows/">Select Rows</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/transpose/">Transpose</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/mergedata/">Merge Data</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/concatenate/">Concatenate</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/select-by-data-index/">Select by Data Index</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/transform/unique/">Unique</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/aggregatecolumns/">Aggregate Columns</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/groupby/">Group by</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pivot/">Pivot Table</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/applydomain/">Apply Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/preprocess/">Preprocess</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/impute/">Impute</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/continuize/">Continuize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/discretize/">Discretize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/randomize/">Randomize</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/purgedomain/">Purge Domain</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/melt/">Melt</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/formula/">Formula</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createclass/">Create Class</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/createinstance/">Create Instance</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/transform/pythonscript/">Python Script</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Unique</h1>
 <p>Remove duplicated data instances.</p>
 <p><strong>Inputs</strong></p>
@@ -170,4 +170,4 @@
 </ol>
 <h2>Example</h2>
 <p>Data set <em>Zoo</em> contains two frogs. This workflow keeps only one by removing instances with the same names.</p>
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>DBSCAN</h1>
 <p>Groups items using the DBSCAN clustering algorithm.</p>
 <p><strong>Inputs</strong></p>
@@ -180,4 +180,4 @@
 left and right you can select the right <strong>Neighborhood distance</strong>.</p>
 <h2>Example</h2>
 <p>In the following example, we connected the <a href="../data/file.md">File</a> widget with the Iris dataset to the DBSCAN widget. In the DBSCAN widget, we set <strong>Core points neighbors</strong> parameter to 5. And select the <strong>Neighborhood distance</strong> to the value in the first &quot;valley&quot; in the graph. We show clusters in the <a href="/widget-catalog/unsupervised/../visualize/scatterplot">Scatter Plot</a> widget.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/DBSCAN-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2852" height="1468" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DBSCAN-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"DBSCAN\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using the DBSCAN clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with cluster label as a meta attribute\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget applies the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/DBSCAN\",\n        children: \"DBSCAN clustering\"\n      }), \" algorithm to the data and outputs a new dataset with cluster labels as a meta attribute. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/DBSCAN-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2852" height="1468" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DBSCAN-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"DBSCAN\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using the DBSCAN clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with cluster label as a meta attribute\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget applies the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/DBSCAN\",\n        children: \"DBSCAN clustering\"\n      }), \" algorithm to the data and outputs a new dataset with cluster labels as a meta attribute. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>PCA</h1>
 <p>PCA linear transformation of input data.</p>
 <p><strong>Inputs</strong></p>
@@ -185,4 +185,4 @@ <h2>Examples</h2>
 <p><strong>PCA</strong> can be used to simplify visualizations of large datasets. Below, we used the <em>Iris</em> dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the <a href="/widget-catalog/unsupervised/../visualize/scatterplot">Scatter Plot</a> show a much clearer distinction between classes than the default settings.</p>
 <a href="/widget-catalog/unsupervised/images/__optimized-images__/PCAExample.png" data-gallery="true"><img alt="" loading="lazy" width="1447" height="1102" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/PCAExample.png"/></a>
 <p>The widget provides two outputs: transformed data and principal components. Transformed data are weights for individual instances in the new coordinate system, while components are the system descriptors (weights for principal components). When fed into the <a href="/widget-catalog/unsupervised/../data/datatable">Data Table</a>, we can see both outputs in numerical form. We used two data tables in order to provide a more clean visualization of the workflow, but you can also choose to edit the links in such a way that you display the data in just one data table. You only need to create two links and connect the <em>Transformed data</em> and <em>Components</em> inputs to the <em>Data</em> output.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/PCAExample2.png" data-gallery="true"><img alt="" loading="lazy" width="1453" height="819" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/PCAExample2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"PCA\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"PCA linear transformation of input data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Transformed Data: PCA transformed data\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Components: \", _jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors\",\n          children: \"Eigenvectors\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Principal_component_analysis\",\n        children: \"Principal Component Analysis\"\n      }), \" (PCA) computes the PCA linear transformation of the input data. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/PCAExample2.png" data-gallery="true"><img alt="" loading="lazy" width="1453" height="819" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/PCAExample2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"PCA\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"PCA linear transformation of input data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Transformed Data: PCA transformed data\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Components: \", _jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors\",\n          children: \"Eigenvectors\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Principal_component_analysis\",\n        children: \"Principal Component Analysis\"\n      }), \" (PCA) computes the PCA linear transformation of the input data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Correlations</h1>
 <p>Compute all pairwise attribute correlations.</p>
 <p><strong>Inputs</strong></p>
@@ -181,4 +181,4 @@ <h2>Example</h2>
 <p>Correlations can be computed only for numeric (continuous) features, so we will use <em>housing</em> as an example data set. Load it in the <a href="/widget-catalog/unsupervised/file">File</a> widget and connect it to <strong>Correlations</strong>. Positively correlated feature pairs will be at the top of the list and negatively correlated will be at the bottom.</p>
 <a href="/widget-catalog/unsupervised/images/__optimized-images__/Correlations-links.png" data-gallery="true"><img alt="" loading="lazy" width="541" height="282" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Correlations-links.png"/></a>
 <p>Go to the most negatively correlated pair, DIS-NOX. Now connect <a href="/widget-catalog/unsupervised/../visualize/scatterplot">Scatter Plot</a> to <strong>Correlations</strong> and set two outputs, Data to Data and Features to Features. Observe how the feature pair is immediately set in the scatter plot. Looks like the two features are indeed negatively correlated.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/Correlations-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2642" height="1466" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Correlations-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Correlations\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute all pairwise attribute correlations.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: selected pair of features\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Correlations: data table with correlation scores\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Correlations\"\n      }), \" computes Pearson or Spearman correlation scores for all pairs of features in a dataset. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/Correlations-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2642" height="1466" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Correlations-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Correlations\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute all pairwise attribute correlations.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: selected pair of features\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Correlations: data table with correlation scores\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Correlations\"\n      }), \" computes Pearson or Spearman correlation scores for all pairs of features in a dataset. 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 <p>Correspondence analysis for categorical multivariate data.</p>
 <p><strong>Inputs</strong></p>
@@ -172,4 +172,4 @@
 </ol>
 <h2>Example</h2>
 <p>Below, is a simple comparison between the <strong>Correspondence Analysis</strong> and <a href="/widget-catalog/unsupervised/../visualize/scatterplot">Scatter Plot</a> widgets on the <em>Titanic</em> dataset. While the <a href="/widget-catalog/unsupervised/../visualize/scatterplot">Scatter Plot</a> shows fairly well which class and sex had a good survival rate and which one didn&#x27;t, <strong>Correspondence Analysis</strong> can plot several variables in a 2-D graph, thus making it easy to see the relations between variable values. It is clear from the graph that &quot;no&quot;, &quot;male&quot; and &quot;crew&quot; are related to each other. The same goes for &quot;yes&quot;, &quot;female&quot; and &quot;first&quot;.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/CorrespondenceAnalysis-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1299" height="969" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/CorrespondenceAnalysis-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Correspondence Analysis\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Correspondence analysis for categorical multivariate data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Coordinates: coordinates of all components\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Correspondence_analysis\",\n        children: \"Correspondence Analysis\"\n      }), \" (CA) computes the CA linear transformation of the input data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Distance File</h1>
 <p>Loads an existing distance file.</p>
 <p><strong>Outputs</strong></p>
@@ -171,4 +171,4 @@
 <p>The simplest way to prepare a distance file is to use Excel. The widget currently processes only single-sheet workbooks. The matrix can be either rectangular, or upper- or lower-triangular, with labels given for columns (immediately above) or rows (immediately to the left) or both. Empty cells are treated as zeros. If the matrix is triangular and only one set of labels is given or both sets are equal, the other half can be filled automatically, making the matrix symmetric.</p>
 <h2>Example</h2>
 <p>When you want to use a custom-set distance file that you&#x27;ve saved before, open the <strong>Distance File</strong> widget and select the desired file with the <em>Browse</em> icon. This widget loads the existing distance file. In the snapshot below, we loaded the transformed <em>Iris</em> distance matrix from the <a href="../unsupervised/savedistancematrix.md">Save Distance Matrix</a> example. We displayed the transformed data matrix in the <a href="/widget-catalog/unsupervised/../unsupervised/distancemap">Distance Map</a> widget. We also decided to display a distance map of the original <em>Iris</em> dataset for comparison.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/DistanceFile-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1105" height="764" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DistanceFile-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distance File\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Loads an existing distance file.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distance File: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/DistanceFile-stamped.png\",\n      alt: \"\",\n      width: \"508\",\n      height: \"216\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Choose from a list of previously saved distance files.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Browse for saved distance files.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reload the selected distance file.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Information about the distance file (number of points,\\nlabelled/unlabelled).\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Browse documentation datasets.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The simplest way to prepare a distance file is to use Excel. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Distance Map</h1>
 <p>Visualizes distances between items.</p>
 <p><strong>Inputs</strong></p>
@@ -194,4 +194,4 @@ <h2>Examples</h2>
 <p>The first workflow shows a very standard use of the <strong>Distance Map</strong> widget. We select 70% of the original <em>Iris</em> data as our sample and view the distances between rows in <strong>Distance Map</strong>.</p>
 <a href="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1021" height="956" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example1.png"/></a>
 <p>In the second example, we use the <em>heart disease</em> data again and select a subset of women only from the <a href="../visualize/scatterplot.md">Scatter Plot</a>. Then, we visualize distances between columns in the <strong>Distance Map</strong>. Since the subset also contains some discrete data, the <a href="/widget-catalog/unsupervised/../unsupervised/distances">Distances</a> widget warns us it will ignore the discrete features, thus we will see only continuous instances/attributes in the map.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1293" height="965" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distance Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualizes distances between items.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: instances selected from the matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: attributes selected from the matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distance Map\"\n      }), \" visualizes distances between objects. 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Also note that the \", _jsx(_components.strong, {\n        children: \"Distances\"\n      }), \" widget ignores discrete values and calculates distances only for continuous data, thus it can only display distance map for discrete data if you \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../data/continuize\",\n        children: \"Continuize\"\n      }), \" them first.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The snapshot shows distances between columns in the \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" data, where smaller distances are represented with light and larger with dark orange. The matrix is symmetric and the diagonal is a light shade of orange - no attribute is different from itself. Symmetricity is always assumed, while the diagonal may also be non-zero.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-stamped.png\",\n      alt: \"\",\n      width: \"733\",\n      height: \"561\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Element sorting\"\n        }), \" arranges elements in the map by\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsx(_components.li, {\n            children: \"None (lists instances as found in the dataset)\"\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Clustering\"\n            }), \" (clusters data by similarity)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Clustering with ordered leaves\"\n            }), \" (maximizes the sum of similarities of adjacent elements)\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Colors\"\n        }), \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Colors\"\n            }), \" (select the color palette for your distance map)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Low\"\n            }), \" and \", _jsx(_components.strong, {\n              children: \"High\"\n            }), \" are thresholds for the color palette (low for instances or attributes with low distances and high for instances or attributes with high distances).\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select \", _jsx(_components.em, {\n          children: \"Annotations\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Send Selected Automatically\"\n        }), \" is on, the data subset is communicated automatically, otherwise you need to press \", _jsx(_components.em, {\n          children: \"Send Selected\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Press \", _jsx(_components.em, {\n          children: \"Save Image\"\n        }), \" if you want to save the created image to your computer.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Normally, a color palette is used to visualize the entire range of distances appearing in the matrix. This can be changed by setting the low and high threshold. In this way we ignore the differences in distances outside this interval and visualize the interesting part of the distribution.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Below, we visualized the most correlated attributes (distances by columns) in the \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" dataset by setting the color threshold for high distances to the minimum. We get a predominantly black square, where attributes with the lowest distance scores are represented by a lighter shade of the selected color schema (in our case: orange). Beside the diagonal line, we see that in our example \", _jsx(_components.em, {\n        children: \"ST by exercise\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"major vessels colored\"\n      }), \" are the two attributes closest together.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Highlighted.png\",\n      alt: \"\",\n      width: \"853\",\n      height: \"618\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The user can select a region in the map with the usual click-and-drag of the cursor. When a part of the map is selected, the widget outputs all items from the selected cells.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The first workflow shows a very standard use of the \", _jsx(_components.strong, {\n        children: \"Distance Map\"\n      }), \" widget. We select 70% of the original \", _jsx(_components.em, {\n        children: \"Iris\"\n      }), \" data as our sample and view the distances between rows in \", _jsx(_components.strong, {\n        children: \"Distance Map\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example1.png\",\n      alt: \"\",\n      width: \"1021\",\n      height: \"956\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we use the \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" data again and select a subset of women only from the \", _jsx(_components.a, {\n        href: \"../visualize/scatterplot.md\",\n        children: \"Scatter Plot\"\n      }), \". Then, we visualize distances between columns in the \", _jsx(_components.strong, {\n        children: \"Distance Map\"\n      }), \". Since the subset also contains some discrete data, the \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/distances\",\n        children: \"Distances\"\n      }), \" widget warns us it will ignore the discrete features, thus we will see only continuous instances/attributes in the map.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example.png\",\n      alt: \"\",\n      width: \"1293\",\n      height: \"965\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1293" height="965" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMap-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distance Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualizes distances between items.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: instances selected from the matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: attributes selected from the matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distance Map\"\n      }), \" visualizes distances between objects. The visualization is the same as if we printed out a table of numbers, except that the numbers are replaced by colored spots.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Distances are most often those between instances (\\\"\", _jsx(_components.em, {\n        children: \"rows\"\n      }), \"\\\" in the \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/distances\",\n        children: \"Distances\"\n      }), \" widget) or attributes (\\\"\", _jsx(_components.em, {\n        children: \"columns\"\n      }), \"\\\" in Distances widget). The only suitable input for \", _jsx(_components.strong, {\n        children: \"Distance Map\"\n      }), \" is the \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/distances\",\n        children: \"Distances\"\n      }), \" widget. For the output, the user can select a region of the map and the widget will output the corresponding instances or attributes. Also note that the \", _jsx(_components.strong, {\n        children: \"Distances\"\n      }), \" widget ignores discrete values and calculates distances only for continuous data, thus it can only display distance map for discrete data if you \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../data/continuize\",\n        children: \"Continuize\"\n      }), \" them first.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The snapshot shows distances between columns in the \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" data, where smaller distances are represented with light and larger with dark orange. The matrix is symmetric and the diagonal is a light shade of orange - no attribute is different from itself. 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This can be changed by setting the low and high threshold. In this way we ignore the differences in distances outside this interval and visualize the interesting part of the distribution.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Below, we visualized the most correlated attributes (distances by columns) in the \", _jsx(_components.em, {\n        children: \"heart disease\"\n      }), \" dataset by setting the color threshold for high distances to the minimum. We get a predominantly black square, where attributes with the lowest distance scores are represented by a lighter shade of the selected color schema (in our case: orange). 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Distance Matrix</h1>
 <p>Visualizes distance measures in a distance matrix.</p>
 <p><strong>Inputs</strong></p>
@@ -174,4 +174,4 @@
 <p>The only two suitable inputs for <strong>Distance Matrix</strong> are the <a href="../unsupervised/distances.md">Distances</a> widget and the <a href="/widget-catalog/unsupervised/../unsupervised/distancetransformation">Distance Transformation</a> widget. The output of the widget is a data table containing the distance matrix. The user can decide how to label the table and the distance matrix (or instances in the distance matrix) can then be visualized or displayed in a separate data table.</p>
 <h2>Example</h2>
 <p>The example below displays a very standard use of the <strong>Distance Matrix</strong> widget. We compute the distances between rows in the sample from the <em>Iris</em> dataset and output them in the <strong>Distance Matrix</strong>. It comes as no surprise that Iris Virginica and Iris Setosa are the furthest apart.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMatrix-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1025" height="692" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DistanceMatrix-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distance Matrix\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualizes distance measures in a distance matrix.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Table: distance measures in a distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distance Matrix\"\n      }), \" widget creates a distance matrix, which is a two-dimensional array containing the distances, taken pairwise, between the elements of a set. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Distances</h1>
 <p>Computes distances between rows/columns in a dataset.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Examples</h2>
 <p>Alternatively, we can compute distance between columns and find how similar our features are.</p>
 <a href="/widget-catalog/unsupervised/images/__optimized-images__/Distances-Example1-columns.png" data-gallery="true"><img alt="" loading="lazy" width="2118" height="1462" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Distances-Example1-columns.png"/></a>
 <p>The second example shows how to visualize the resulting distance matrix. A nice way to observe data similarity is in a <a href="distancemap.md">Distance Map</a> or in <a href="/widget-catalog/unsupervised/mds">MDS</a>.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/Distances-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1966" height="1338" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Distances-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distances\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Computes distances between rows/columns in a dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distances\"\n      }), \" widget computes distances between rows or columns in a dataset. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/Distances-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="1966" height="1338" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Distances-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distances\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Computes distances between rows/columns in a dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distances\"\n      }), \" widget computes distances between rows or columns in a dataset. 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@@ -187,4 +187,4 @@
 </ol>
 <h2>Example</h2>
 <p>In the snapshot below, you can see how transformation affects the distance matrix. We loaded the <em>Iris</em> dataset and calculated the distances between rows with the help of the <a href="../unsupervised/distances.md">Distances</a> widget. In order to demonstrate how <strong>Distance Transformation</strong> affects the <a href="/widget-catalog/unsupervised/../unsupervised/distancematrix">Distance Matrix</a>, we created the workflow below and compared the transformed distance matrix with the &quot;original&quot; one.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/DistanceTransformation-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1206" height="854" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/DistanceTransformation-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distance Transformation\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Transforms distances in a dataset.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: transformed distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distances Transformation\"\n      }), \" widget is used for the normalization and inversion of distance matrices. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Hierarchical Clustering</h1>
 <p>Groups items using a hierarchical clustering algorithm.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h4>Cluster explanation</h4>
 <p>In the second example, we continue the <em>Grades for English and Math</em> data. Say we wish to explain what characterizes the cluster with Maya, George, Lea, and Phill.</p>
 <p>We select the cluster in the dendrogram and pass the entire data set to <a href="/widget-catalog/unsupervised/../visualize/boxplot">Box Plot</a>. Note that the connection here is <em>Data</em>, not <em>Selected Data</em>. To rewire the connection, double-click on it.</p>
 <p>In <strong>Box Plot</strong>, we set <em>Selected</em> variable as the Subgroup. This will split the plot into selected data instances (our cluster) and the remaining data. Next, we use <em>Order by relevance to subgroup</em> option, which sorts the variables according to how well they distinguish between subgroups. It turns out, that our cluster contains students who are bad at math (they have low values of the Algebra variable).</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/Hierarchical-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2560" height="1608" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Hierarchical-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Hierarchical Clustering\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using a hierarchical clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether an instance is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget computes \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Hierarchical_clustering\",\n        children: \"hierarchical clustering\"\n      }), \" of arbitrary types of objects from a matrix of distances and shows a corresponding \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Dendrogram\",\n        children: \"dendrogram\"\n      }), \". 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Items to the right of the line are selected.)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Top N\"\n            }), \" (Selects the number of top nodes.)\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Use \", _jsx(_components.em, {\n          children: \"Zoom\"\n        }), \" and scroll to zoom in or out.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"The data can be automatically output on any change (\", _jsx(_components.em, {\n          children: \"Send Automatically\"\n        }), \") or, if the box isn't ticked, by pushing \", _jsx(_components.em, {\n          children: \"Send Selection\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To output the cluster, click on the ruler at the top or the bottom of the visualization. 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We compute it with \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/distances\",\n        children: \"Distances\"\n      }), \", where we use the \", _jsx(_components.em, {\n        children: \"Euclidean\"\n      }), \" distance metric.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Once the data is passed to the hierarchical clustering, the widget displays a dendrogram, a tree-like clustering structure. Each node represents an instance in the data set, in our case a student. Tree nodes are labelled with student names.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To create the clusters, we click on the ruler at the desired threshold. In this case, we chose three clusters. We pass those clusters to \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/mds\",\n        children: \"MDS\"\n      }), \", which shows a 2D projection of data instances, colored by cluster label.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/Hierarchical-Example1.png\",\n      alt: \"\",\n      width: \"2080\",\n      height: \"1644\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Cluster explanation\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we continue the \", _jsx(_components.em, {\n        children: \"Grades for English and Math\"\n      }), \" data. Say we wish to explain what characterizes the cluster with Maya, George, Lea, and Phill.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We select the cluster in the dendrogram and pass the entire data set to \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../visualize/boxplot\",\n        children: \"Box Plot\"\n      }), \". Note that the connection here is \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \", not \", _jsx(_components.em, {\n        children: \"Selected Data\"\n      }), \". To rewire the connection, double-click on it.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \", we set \", _jsx(_components.em, {\n        children: \"Selected\"\n      }), \" variable as the Subgroup. This will split the plot into selected data instances (our cluster) and the remaining data. Next, we use \", _jsx(_components.em, {\n        children: \"Order by relevance to subgroup\"\n      }), \" option, which sorts the variables according to how well they distinguish between subgroups. It turns out, that our cluster contains students who are bad at math (they have low values of the Algebra variable).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/Hierarchical-Example2.png\",\n      alt: \"\",\n      width: \"2560\",\n      height: \"1608\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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This will create a cut-off for the clusters.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Cluster selection and projections\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We start with the \", _jsx(_components.em, {\n        children: \"Grades for English and Math\"\n      }), \" data set from the \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../data/datasets\",\n        children: \"Datasets\"\n      }), \" widget. The data contains two numeric variables, grades for English and for Algebra.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Hierarchical Clustering\"\n      }), \" requires distance matrix on the input. We compute it with \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/distances\",\n        children: \"Distances\"\n      }), \", where we use the \", _jsx(_components.em, {\n        children: \"Euclidean\"\n      }), \" distance metric.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Once the data is passed to the hierarchical clustering, the widget displays a dendrogram, a tree-like clustering structure. Each node represents an instance in the data set, in our case a student. Tree nodes are labelled with student names.\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To create the clusters, we click on the ruler at the desired threshold. In this case, we chose three clusters. We pass those clusters to \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../unsupervised/mds\",\n        children: \"MDS\"\n      }), \", which shows a 2D projection of data instances, colored by cluster label.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/Hierarchical-Example1.png\",\n      alt: \"\",\n      width: \"2080\",\n      height: \"1644\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Cluster explanation\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the second example, we continue the \", _jsx(_components.em, {\n        children: \"Grades for English and Math\"\n      }), \" data. Say we wish to explain what characterizes the cluster with Maya, George, Lea, and Phill.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"We select the cluster in the dendrogram and pass the entire data set to \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../visualize/boxplot\",\n        children: \"Box Plot\"\n      }), \". Note that the connection here is \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \", not \", _jsx(_components.em, {\n        children: \"Selected Data\"\n      }), \". To rewire the connection, double-click on it.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \", we set \", _jsx(_components.em, {\n        children: \"Selected\"\n      }), \" variable as the Subgroup. This will split the plot into selected data instances (our cluster) and the remaining data. Next, we use \", _jsx(_components.em, {\n        children: \"Order by relevance to subgroup\"\n      }), \" option, which sorts the variables according to how well they distinguish between subgroups. It turns out, that our cluster contains students who are bad at math (they have low values of the Algebra variable).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/Hierarchical-Example2.png\",\n      alt: \"\",\n      width: \"2560\",\n      height: \"1608\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file 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 <p>Groups items using the k-Means clustering algorithm.</p>
 <p><strong>Inputs</strong></p>
@@ -199,4 +199,4 @@ <h2>Examples</h2>
 <p>But as we used silhouette score to estimate our cluster quality, we can plot the clusters in the <a href="/widget-catalog/unsupervised/../visualize/silhouetteplot">Silhouette Plot</a> to observe inliers and outliers. Place Silhouette Plot in place of Select Rows.</p>
 <p>Silhouette Plot shows silhouette scores for individual data instances. High, positive scores represent instances that are highly representative of the clusters, while negative scores represent instances that are outliers (don&#x27;t fit well with the cluster). Select negative scores from the green cluster C3 and plot them in a scatter plot as a subset.</p>
 <p>It seems like these are mostly iris versicolors, which are bordering the iris virginica region. Note that the green color of the cluster C3 doesn&#x27;t coincide with the green color of the iris labels - these are two different things.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/kMeans-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2836" height="1812" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/kMeans-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"k-Means\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using the k-Means clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with cluster label as a meta attribute\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Centroids: table with initial centroid coordinates\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget applies the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-means_clustering\",\n        children: \"k-Means clustering\"\n      }), \" algorithm to the data and outputs a new dataset in which the cluster label is added as a meta attribute. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/kMeans-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2836" height="1812" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/kMeans-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"k-Means\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using the k-Means clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with cluster label as a meta attribute\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Centroids: table with initial centroid coordinates\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget applies the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/K-means_clustering\",\n        children: \"k-Means clustering\"\n      }), \" algorithm to the data and outputs a new dataset in which the cluster label is added as a meta attribute. 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 <p>Groups items using the Louvain clustering algorithm.</p>
 <p><strong>Inputs</strong></p>
@@ -196,4 +196,4 @@ <h2>Example</h2>
 <p>We can visualize the graph itself using the <strong>Network Explorer</strong> from the Network addon.</p>
 <h2>References</h2>
 <p>Blondel, Vincent D., et al. &quot;<a href="https://arxiv.org/abs/0803.0476">Fast unfolding of communities in large networks.</a>&quot; Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.</p>
-<p>Lambiotte, Renaud, J-C. Delvenne, and Mauricio Barahona. &quot;Laplacian dynamics and multiscale modular structure in networks.&quot; arXiv preprint, <a href="https://arxiv.org/abs/0812.1770">arXiv:0812</a>.1770 (2008).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Louvain Clustering\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using the Louvain clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with cluster label as a meta attribute\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Graph (with the Network addon): the weighted k-nearest neighbor graph\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget first converts the input data into a k-nearest neighbor graph. 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\ No newline at end of file
+<p>Lambiotte, Renaud, J-C. Delvenne, and Mauricio Barahona. &quot;Laplacian dynamics and multiscale modular structure in networks.&quot; arXiv preprint, <a href="https://arxiv.org/abs/0812.1770">arXiv:0812</a>.1770 (2008).</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Louvain Clustering\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Groups items using the Louvain clustering algorithm.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with cluster label as a meta attribute\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Graph (with the Network addon): the weighted k-nearest neighbor graph\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The widget first converts the input data into a k-nearest neighbor graph. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Manifold Learning</h1>
 <p>Nonlinear dimensionality reduction.</p>
 <p><strong>Inputs</strong></p>
@@ -239,4 +239,4 @@ <h2>Preprocessing</h2>
 <p>To override default preprocessing, preprocess the data beforehand with <a href="/widget-catalog/unsupervised/../data/preprocess">Preprocess</a> widget.</p>
 <h2>Example</h2>
 <p><em>Manifold Learning</em> widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used <em>voting.tab</em> to map 16-dimensional data onto a 2D graph. Then we used <a href="/widget-catalog/unsupervised/../visualize/scatterplot">Scatter Plot</a> to plot the embeddings.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/manifold-learning-example.png" data-gallery="true"><img alt="" loading="lazy" width="1206" height="764" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/manifold-learning-example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Manifold Learning\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nonlinear dimensionality reduction.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Transformed Data: dataset with reduced coordinates\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction\",\n        children: \"Manifold Learning\"\n      }), \" is a technique which finds a non-linear manifold within the higher-dimensional space. 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[_jsx(_components.em, {\n                  children: \"initialization\"\n                }), \": method for initialization of the algorithm (PCA or random)\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Isomap:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [\"number of \", _jsx(_components.em, {\n                  children: \"neighbors\"\n                })]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Locally Linear Embedding:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"method\"\n                }), \":\", \"\\n\", _jsxs(_components.ul, {\n                  children: [\"\\n\", _jsx(_components.li, {\n                    children: \"standard\"\n                  }), \"\\n\", _jsx(_components.li, {\n                    children: \"modified\"\n                  }), \"\\n\", _jsx(_components.li, {\n                    children: _jsx(_components.a, {\n                      href: \"http://scikit-learn.org/stable/modules/manifold.html#hessian-eigenmapping\",\n                      children: \"hessian eigenmap\"\n                    })\n                  }), \"\\n\", _jsx(_components.li, {\n                    children: \"local\"\n                  }), \"\\n\"]\n                }), \"\\n\"]\n              }), \"\\n\", _jsxs(_components.li, {\n                children: [\"number of \", _jsx(_components.em, {\n                  children: \"neighbors\"\n                })]\n              }), \"\\n\", _jsx(_components.li, {\n                children: _jsx(_components.em, {\n                  children: \"max iterations\"\n                })\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [\"Spectral Embedding:\", \"\\n\", _jsxs(_components.ul, {\n              children: [\"\\n\", _jsxs(_components.li, {\n                children: [_jsx(_components.em, {\n                  children: \"affinity\"\n                }), \":\", \"\\n\", _jsxs(_components.ul, {\n                  children: [\"\\n\", _jsx(_components.li, {\n                    children: \"nearest neighbors\"\n                  }), \"\\n\", _jsx(_components.li, {\n                    children: \"RFB kernel\"\n                  }), \"\\n\"]\n                }), \"\\n\"]\n              }), \"\\n\"]\n            }), \"\\n\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Output: the number of reduced features (components).\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Apply automatically\"\n        }), \" is ticked, changes will be propagated automatically. Alternatively, click \", _jsx(_components.em, {\n          children: \"Apply\"\n        }), \".\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Manifold Learning\"\n      }), \" widget produces different embeddings for high-dimensional data.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/collage-manifold.png\",\n      alt: \"\",\n      width: \"777\",\n      height: \"498\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"From left to right, top to bottom: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding.\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"All projections use default preprocessing if necessary. It is executed in the following order:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"continuization of categorical variables (with one feature per value)\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"imputation of missing values with mean values\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To override default preprocessing, preprocess the data beforehand with \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../data/preprocess\",\n        children: \"Preprocess\"\n      }), \" widget.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.em, {\n        children: \"Manifold Learning\"\n      }), \" widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used \", _jsx(_components.em, {\n        children: \"voting.tab\"\n      }), \" to map 16-dimensional data onto a 2D graph. Then we used \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../visualize/scatterplot\",\n        children: \"Scatter Plot\"\n      }), \" to plot the embeddings.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/manifold-learning-example.png\",\n      alt: \"\",\n      width: \"1206\",\n      height: \"764\"\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv 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Plot","category":"Evaluate","keywords":["calibration","plot"],"background":"#C3F3F3","icon":"widget-icons/Evaluate-Calibration Plot.png","url":"calibrationplot"}]],["Unsupervised",[{"title":"Distance File","category":"Unsupervised","keywords":["distance file","load","read","open"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance File.png","url":"distancefile"},{"title":"Distance Matrix","category":"Unsupervised","keywords":["distance","matrix"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Distance Matrix.png","url":"distancematrix"},{"title":"t-SNE","category":"Unsupervised","keywords":["t-sne","tsne"],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-t-SNE.png","url":"tsne"},{"title":"Correlations","category":"Unsupervised","keywords":[],"background":"#CAE1EF","icon":"widget-icons/Unsupervised-Correlations.png","url":"correlations"},{"title":"Distance 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 <p>Multidimensional scaling (MDS) projects items onto a plane fitted to given distances between points.</p>
 <p><strong>Inputs</strong></p>
@@ -216,4 +216,4 @@ <h1>Example</h1>
 <h1>References</h1>
 <p>Wickelmaier, F. (2003). An Introduction to MDS. Sound Quality Research
 Unit, Aalborg University. Available
-<a href="https://homepages.uni-tuebingen.de/florian.wickelmaier/pubs/Wickelmaier2003SQRU.pdf">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"MDS\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Multidimensional scaling (MDS) projects items onto a plane fitted to given distances between points.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Distances: distance matrix\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset with MDS coordinates\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Multidimensional_scaling\",\n        children: \"Multidimensional scaling\"\n      }), \" is a technique which finds a low-dimensional (in our case a two-dimensional) projection of points, where it tries to fit distances between points as well as possible. The perfect fit is typically impossible to obtain since the data is high-dimensional or the distances are not \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Euclidean_distance\",\n        children: \"Euclidean\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In the input, the widget needs either a dataset or a matrix of distances. When visualizing distances between rows, you can also adjust the color of the points, change their shape, mark them, and output them upon selection.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The algorithm iteratively moves the points around in a kind of a simulation of a physical model: if two points are too close to each other (or too far away), there is a force pushing them apart (or together). The change of the point’s position at each time interval corresponds to the sum of forces acting on it.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/MDS-zoo-stamped.png\",\n      alt: \"\",\n      width: \"1220\",\n      height: \"970\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"The widget redraws the projection during optimization. Optimization is run automatically in the beginning and later by pushing \", _jsx(_components.em, {\n          children: \"Start\"\n        }), \".\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Max iterations\"\n            }), \": The optimization stops either when the projection changes only minimally at the last iteration or when a maximum number of iterations has been reached.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Initialization\"\n            }), \": PCA (Torgerson) positions the initial points along principal coordinate axes. \", _jsx(_components.em, {\n              children: \"Random\"\n            }), \" sets the initial points to a random position and then readjusts them.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Refresh\"\n            }), \": Set how often you want to refresh the visualization. It can be at \", _jsx(_components.em, {\n              children: \"Every iteration\"\n            }), \", \", _jsx(_components.em, {\n              children: \"Every 5/10/25/50 steps\"\n            }), \" or never (\", _jsx(_components.em, {\n              children: \"None\"\n            }), \"). Setting a lower refresh interval makes the animation more visually appealing, but can be slow if the number of points is high.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Defines how the points are visualized. These options are available only when visualizing distances between rows (selected in the \", _jsx(_components.a, {\n          href: \"/widget-catalog/unsupervised/../unsupervised/distances\",\n          children: \"Distances\"\n        }), \" widget).\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Color\"\n            }), \": Color of points by attribute (gray for continuous, colored for discrete).\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Shape\"\n            }), \": Shape of points by attribute (only for discrete).\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Size\"\n            }), \": Set the size of points (\", _jsx(_components.em, {\n              children: \"Same size\"\n            }), \" or select an attribute) or let the size depend on the value of the continuous attribute the point represents (Stress).\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Label\"\n            }), \": Discrete attributes can serve as a label.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Symbol size\"\n            }), \": Adjust the size of the dots.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Symbol opacity\"\n            }), \": Adjust the transparency level of the dots.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Show similar pairs\"\n            }), \": Adjust the strength of network lines.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Jitter\"\n            }), \": Set \", _jsx(_components.a, {\n              href: \"https://en.wikipedia.org/wiki/Jitter\",\n              children: \"jittering\"\n            }), \" to prevent the dots from overlapping.\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Adjust the graph with \", _jsx(_components.em, {\n          children: \"Zoom/Select\"\n        }), \". 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The rectangle readjusts the graph proportionally.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Select the desired output:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Original features only\"\n            }), \" (input dataset)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Coordinates only\"\n            }), \" (MDS coordinates)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Coordinates as features\"\n            }), \" (input dataset + MDS coordinates as regular attributes)\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.strong, {\n              children: \"Coordinates as meta attributes\"\n            }), \" (input dataset + MDS coordinates as meta attributes)\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Sending the instances can be automatic if \", _jsx(_components.em, {\n          children: \"Send selected automatically\"\n        }), \" is ticked. Alternatively, click \", _jsx(_components.em, {\n          children: \"Send selected\"\n        }), \".\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.strong, {\n          children: \"Save Image\"\n        }), \" allows you to save the created image either as .svg or .png file to your device.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Produce a report.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The MDS graph performs many of the functions of the Visualizations widget. It is in many respects similar to the \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../visualize/scatterplot\",\n        children: \"Scatter Plot\"\n      }), \" widget, so we recommend reading that widget's description as well.\"]\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Preprocessing\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"When given \", _jsx(_components.em, {\n        children: \"Distances\"\n      }), \" on the input, preprocessing is not applied. When given \", _jsx(_components.em, {\n        children: \"Data\"\n      }), \", MDS uses default preprocessing if necessary. 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Sound Quality Research\\nUnit, Aalborg University. Available\\n\", _jsx(_components.a, {\n        href: \"https://homepages.uni-tuebingen.de/florian.wickelmaier/pubs/Wickelmaier2003SQRU.pdf\",\n        children: \"here\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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@@ -180,4 +180,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/unsupervised/images/__optimized-images__/neighbours-example-multiple.png" data-gallery="true"><img alt="" loading="lazy" width="1749" height="994" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/neighbours-example-multiple.png"/></a>
 <p>Another example requires the installation of Image Analytics add-on. We loaded 15 paintings from famous painters with <strong>Import Images</strong> widget and passed them to <strong>Image Embedding</strong>, where we selected <em>Painters</em> embedder.</p>
 <p>Then the procedure is the same as above. We passed embedded images to <strong>Image Viewer</strong> and selected a painting from Monet to serve as our reference image. We passed the image to <strong>Neighbors</strong>, where we set the distance measure to <em>cosine</em>, ticked off <em>Exclude reference</em> and set the neighbors to 2. This allows us to find the actual closest neighbor to a reference painting and observe them side by side in <strong>Image Viewer (1)</strong>.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/neighbours-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1707" height="943" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/neighbours-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    code: \"code\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Neighbors\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute nearest neighbors in data according to reference.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: An input data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference: A reference data for neighbor computation.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Neighbors: A data table of nearest neighbors according to reference.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Neighbors\"\n      }), \" widget computes nearest neighbors for a given reference and for a given distance measure. 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In \", _jsx(_components.strong, {\n        children: \"Data Table\"\n      }), \", we selected an instance of iris, that will serve as our reference, meaning we wish to retrieve 10 closest examples to the select data instance. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/neighbours-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1707" height="943" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/neighbours-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    code: \"code\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Neighbors\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Compute nearest neighbors in data according to reference.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: An input data set.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Reference: A reference data for neighbor computation.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Neighbors: A data table of nearest neighbors according to reference.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Neighbors\"\n      }), \" widget computes nearest neighbors for a given reference and for a given distance measure. 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 <p>Outlier detection widget.</p>
 <p><strong>Inputs</strong></p>
@@ -210,4 +210,4 @@
 </ol>
 <h2>Example</h2>
 <p>Below is an example of how to use this widget. We used subset (<em>versicolor</em> and <em>virginica</em> instances) of the <em>Iris</em> dataset to detect the outliers. We chose the <em>Local Outlier Factor</em> method, with <em>Euclidean</em> distance. Then we observed the annotated instances in the <a href="../visualize/scatterplot.md">Scatter Plot</a> widget. In the next step we used the <em>setosa</em> instances to demonstrate novelty detection using <a href="/widget-catalog/unsupervised/../data/applydomain">Apply Domain</a> widget. After concatenating both outputs we examined the outliers in the <em>Scatter Plot (1)</em>.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/Outliers-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1248" height="1171" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Outliers-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Outliers\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Outlier detection widget.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Outliers: instances scored as outliers\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Inliers: instances not scored as outliers\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Data: input dataset appended \", _jsx(_components.em, {\n          children: \"Outlier\"\n        }), \" variable\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Outliers\"\n      }), \" widget applies one of the four methods for outlier detection. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Save Distance Matrix</h1>
 <p>Saves a distance matrix.</p>
 <p>If the file is saved to the same directory as the workflow or in the subtree of that directory, the widget remembers the relative path. Otherwise it will store an absolute path, but disable auto save for security reasons.</p>
@@ -166,4 +166,4 @@
 </ol>
 <h2>Example</h2>
 <p>In the snapshot below, we used the <a href="../unsupervised/distancetransformation.md">Distance Transformation</a> widget to transform the distances in the <em>Iris</em> dataset. We then chose to save the transformed version to our computer, so we could use it later on. We decided to output all data instances. You can choose to output just a minor subset of the data matrix. Pairs are marked automatically. If you wish to know what happened to our changed file, see <a href="/widget-catalog/unsupervised/../unsupervised/distancefile">Distance File</a>.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/SaveDistanceMatrix-Example.png" data-gallery="true"><img alt="" loading="lazy" width="866" height="891" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/SaveDistanceMatrix-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Save Distance Matrix\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Saves a distance matrix.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If the file is saved to the same directory as the workflow or in the subtree of that directory, the widget remembers the relative path. 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 <p>Computation of a self-organizing map.</p>
 <p><strong>Inputs</strong></p>
@@ -185,4 +185,4 @@
 </ol>
 <h2>Example</h2>
 <p>Self-organizing maps are low-dimensional projections of the input data. We will use the <em>brown-selected</em> data and display the data instance in a 2-D projection. Seems like the three gene types are well-separated. We can select a subset from the grid and display it in a Data Table.</p>
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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/Self-Organizing_Map_Example.png" data-gallery="true"><img alt="" loading="lazy" width="2710" height="1472" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/Self-Organizing_Map_Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Self-Organizing Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Computation of a self-organizing map.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Self-organizing_map\",\n        children: \"self-organizing map (SOM)\"\n      }), \" is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a two-dimensional, discretized representation of the data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancefile/">Distance File</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancematrix/">Distance Matrix</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/unsupervised/tsne/">t-SNE</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correlations/">Correlations</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancemap/">Distance Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/hierarchicalclustering/">Hierarchical Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/kmeans/">k-Means</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/louvainclustering/">Louvain Clustering</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/DBSCAN/">DBSCAN</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/manifoldlearning/">Manifold Learning</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/outliers/">Outliers</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/PCA/">PCA</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/neighbors/">Neighbors</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/correspondenceanalysis/">Correspondence Analysis</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distances/">Distances</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/distancetransformation/">Distance Transformation</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/mds/">MDS</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/savedistancematrix/">Save Distance Matrix</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/unsupervised/selforganizingmap/">Self-Organizing Map</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>t-SNE</h1>
 <p>Two-dimensional data projection with t-SNE.</p>
 <p><strong>Inputs</strong></p>
@@ -196,4 +196,4 @@ <h2>Examples</h2>
 <p>For the second example, use <a href="https://orangedatamining.com/widget-catalog/single-cell/single_cell_datasets/">Single Cell Datasets</a> widget from the Single Cell add-on to load <em>Bone marrow mononuclear cells with AML (sample)</em> data. Then pass it through <strong>k-Means</strong> and select 2 clusters from Silhouette Scores. Ok, it looks like there might be two distinct clusters here.</p>
 <p>But can we find subpopulations in these cells? Select a few marker genes with the <a href="https://orangedatamining.com/widget-catalog/bioinformatics/marker_genes/">Marker Genes</a> widget, for example natural killer cells (NK cells). Pass the marker genes and k-Means results to <a href="https://orangedatamining.com/widget-catalog/single-cell/score_cells/">Score Cells</a> widget. Finally, add <strong>t-SNE</strong> to visualize the results.</p>
 <p>In <strong>t-SNE</strong>, use <em>Cluster</em> attribute to color the points and <em>Score</em> attribute to set their size. We see that killer cells are nicely clustered together and that t-SNE indeed found subpopulations.</p>
-<a href="/widget-catalog/unsupervised/images/__optimized-images__/tSNE-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="3076" height="1798" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/tSNE-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"t-SNE\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Two-dimensional data projection with 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Select the region, where the gene function is mixed and inspect it in a \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../data/datatable\",\n        children: \"Data Table\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/tSNE-Example1.png\",\n      alt: \"\",\n      width: \"2314\",\n      height: \"1680\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the second example, use \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/single-cell/single_cell_datasets/\",\n        children: \"Single Cell Datasets\"\n      }), \" widget from the Single Cell add-on to load \", _jsx(_components.em, {\n        children: \"Bone marrow mononuclear cells with AML (sample)\"\n      }), \" data. Then pass it through \", _jsx(_components.strong, {\n        children: \"k-Means\"\n      }), \" and select 2 clusters from Silhouette Scores. Ok, it looks like there might be two distinct clusters here.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"But can we find subpopulations in these cells? Select a few marker genes with the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/bioinformatics/marker_genes/\",\n        children: \"Marker Genes\"\n      }), \" widget, for example natural killer cells (NK cells). Pass the marker genes and k-Means results to \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/single-cell/score_cells/\",\n        children: \"Score Cells\"\n      }), \" widget. 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+<a href="/widget-catalog/unsupervised/images/__optimized-images__/tSNE-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="3076" height="1798" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/unsupervised/images/__optimized-images__/tSNE-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a 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to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"t-SNE\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Two-dimensional data projection with 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Select the region, where the gene function is mixed and inspect it in a \", _jsx(_components.a, {\n        href: \"/widget-catalog/unsupervised/../data/datatable\",\n        children: \"Data Table\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/unsupervised/images/__optimized-images__/tSNE-Example1.png\",\n      alt: \"\",\n      width: \"2314\",\n      height: \"1680\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For the second example, use \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/single-cell/single_cell_datasets/\",\n        children: \"Single Cell Datasets\"\n      }), \" widget from the Single Cell add-on to load \", _jsx(_components.em, {\n        children: \"Bone marrow mononuclear cells with AML (sample)\"\n      }), \" data. Then pass it through \", _jsx(_components.strong, {\n        children: \"k-Means\"\n      }), \" and select 2 clusters from Silhouette Scores. Ok, it looks like there might be two distinct clusters here.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"But can we find subpopulations in these cells? Select a few marker genes with the \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/bioinformatics/marker_genes/\",\n        children: \"Marker Genes\"\n      }), \" widget, for example natural killer cells (NK cells). Pass the marker genes and k-Means results to \", _jsx(_components.a, {\n        href: \"https://orangedatamining.com/widget-catalog/single-cell/score_cells/\",\n        children: \"Score Cells\"\n      }), \" widget. 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 <p>Visualizes comparisons among discrete categories.</p>
 <p><strong>Inputs</strong></p>
@@ -176,4 +176,4 @@ <h2>Example</h2>
 <p>The <strong>Bar Plot</strong> widget is most commonly used immediately after the <a href="/widget-catalog/visualize/../data/file">File</a> widget to compare categorical values. In this example, we have used <em>heart-disease</em> data to inspect our variables.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/Bar-Plot-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1424" height="744" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Bar-Plot-Example.png"/></a>
 <p>First, we have observed cholesterol values of patient from our data set. We grouped them by diameter narrowing, which defines patients with a heart disease (1) and those without (0). We use the same variable for coloring the bars.</p>
-<p>Then, we selected patients over 60 years of age with <a href="/widget-catalog/visualize/../data/selectrows">Select Rows</a>. We sent the subset to <strong>Bar Plot</strong> to highlight these patients in the widget. The big outlier with a high cholesterol level is apparently over 60 years old.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Bar Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualizes comparisons among discrete categories.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Bar Plot\"\n      }), \" widget visualizes numeric variables and compares them by a categorical variable. 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+<p>Then, we selected patients over 60 years of age with <a href="/widget-catalog/visualize/../data/selectrows">Select Rows</a>. We sent the subset to <strong>Bar Plot</strong> to highlight these patients in the widget. The big outlier with a high cholesterol level is apparently over 60 years old.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Bar Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualizes comparisons among discrete categories.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Bar Plot\"\n      }), \" widget visualizes numeric variables and compares them by a categorical variable. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Box Plot</h1>
 <p>Shows distribution of attribute values.</p>
 <p><strong>Inputs</strong></p>
@@ -187,4 +187,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/visualize/images/__optimized-images__/BoxPlot-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="3046" height="1452" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/BoxPlot-Example1.png"/></a>
 <p><strong>Box Plot</strong> is also useful for finding the properties of a specific dataset, for instance, a set of instances manually defined in another widget (e.g. <a href="../visualize/scatterplot.md">Scatter Plot</a> or instances belonging to some cluster or a classification tree node. Let us now use <em>zoo</em> data and create a typical clustering workflow with <a href="../unsupervised/distances.md">Distances</a> and <a href="/widget-catalog/visualize/../unsupervised/hierarchicalclustering">Hierarchical Clustering</a>.</p>
 <p>Now define the threshold for cluster selection (click on the ruler at the top). Connect <strong>Box Plot</strong> to <strong>Hierarchical Clustering</strong>, tick <em>Order by relevance</em>, and select <em>Cluster</em> as a subgroup. This will order attributes by how well they define the selected subgroup, in our case, a cluster. It seems like our clusters indeed correspond very well with the animal type!</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/BoxPlot-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2986" height="1934" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/BoxPlot-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Box Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows distribution of attribute values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \" widget shows the distributions of attribute values. 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+<a href="/widget-catalog/visualize/images/__optimized-images__/BoxPlot-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2986" height="1934" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/BoxPlot-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    a: \"a\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Box Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Shows distribution of attribute values.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Box Plot\"\n      }), \" widget shows the distributions of attribute values. 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 <p>CN2 Rule Viewer</p>
 <p><strong>Inputs</strong></p>
@@ -174,4 +174,4 @@
 <h2>Examples</h2>
 <p>In the schema below, the most common use of the widget is presented. First, the data is read and a CN2 rule classifier is trained. We are using <em>titanic</em> dataset for the rule construction. The rules are then viewed using the <a href="/widget-catalog/visualize/../visualize/cn2ruleviewer">Rule Viewer</a>. To explore different CN2 algorithms and understand how adjusting parameters influences the learning process, <strong>Rule Viewer</strong> should be kept open and in sight, while setting the CN2 learning algorithm (the presentation will be updated promptly).</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/CN2-Viewer-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="1249" height="746" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/CN2-Viewer-Example1.png"/></a>
-<p>Selecting a rule outputs filtered data instances. These can be viewed in a <a href="/widget-catalog/visualize/../data/datatable">Data Table</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"CN2 Rule Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"CN2 Rule Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset to filter\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"CN2 Rule Classifier: CN2 Rule Classifier, including a list of induced rules\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Filtered Data: data instances covered by all selected rules\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A widget that displays \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/CN2_algorithm\",\n        children: \"CN2 classification\"\n      }), \" rules. 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\ No newline at end of file
+<p>Selecting a rule outputs filtered data instances. These can be viewed in a <a href="/widget-catalog/visualize/../data/datatable">Data Table</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"CN2 Rule Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"CN2 Rule Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: dataset to filter\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"CN2 Rule Classifier: CN2 Rule Classifier, including a list of induced rules\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Filtered Data: data instances covered by all selected rules\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A widget that displays \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/CN2_algorithm\",\n        children: \"CN2 classification\"\n      }), \" rules. If data is also connected, upon rule selection, one can analyze which instances abide to the conditions.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/CN2RuleViewer-stamped.png\",\n      alt: \"\",\n      width: \"771\",\n      height: \"428\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Original order of induced rules can be restored.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"When rules are many and complex, the view can appear packed. For this reason, \", _jsx(_components.em, {\n          children: \"compact view\"\n        }), \" was implemented, which allows a flat presentation and a cleaner inspection of rules.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Click \", _jsx(_components.em, {\n          children: \"Report\"\n        }), \" to bring up a detailed description of the rule induction algorithm and its parameters, the data domain, and induced rules.\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Additionally, upon selection, rules can be copied to clipboard by pressing the default system shortcut (ctrl+C, cmd+C).\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"In the schema below, the most common use of the widget is presented. First, the data is read and a CN2 rule classifier is trained. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Distributions</h1>
 <p>Displays value distributions for a single attribute.</p>
 <p><strong>Inputs</strong></p>
@@ -185,4 +185,4 @@
 <p>For this example, we used the <em>Iris</em> dataset.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/Distributions-Continuous.png" data-gallery="true"><img alt="" loading="lazy" width="920" height="791" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Distributions-Continuous.png"/></a>
 <p>In class-less domains, the bars are displayed in blue. We used the <em>Housing</em> dataset.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/Distributions-NoClass.png" data-gallery="true"><img alt="" loading="lazy" width="1010" height="791" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Distributions-NoClass.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Distributions\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays value distributions for a single attribute.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether an instance is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Histogram Data: bins and instance counts from the histogram\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Distributions\"\n      }), \" widget displays the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Frequency_distribution\",\n        children: \"value distribution\"\n      }), \" of discrete or continuous attributes. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>FreeViz</h1>
 <p>Displays FreeViz projection.</p>
 <p><strong>Inputs</strong></p>
@@ -193,4 +193,4 @@ <h2>Selection</h2>
 <a href="/widget-catalog/visualize/images/__optimized-images__/FreeViz-selection.png" data-gallery="true"><img alt="" loading="lazy" width="800" height="780" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/FreeViz-selection.png"/></a>
 <h2>Explorative Data Analysis</h2>
 <p>The <strong>FreeViz</strong>, as the rest of Orange widgets, supports zooming-in and out of part of the plot and a manual selection of data instances. These functions are available in the lower left corner of the widget. The default tool is <em>Select</em>, which selects data instances within the chosen rectangular area. <em>Pan</em> enables you to move the plot around the pane. With <em>Zoom</em> you can zoom in and out of the pane with a mouse scroll, while <em>Reset zoom</em> resets the visualization to its optimal size. An example of a simple schema, where we selected data instances from a rectangular region and sent them to the <a href="/widget-catalog/visualize/../data/datatable">Data Table</a> widget, is shown below.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/FreeViz-Example-Explorative.png" data-gallery="true"><img alt="" loading="lazy" width="1622" height="815" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/FreeViz-Example-Explorative.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"FreeViz\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Displays FreeViz projection.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Components: FreeViz vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"FreeViz\"\n      }), \" uses a paradigm borrowed from particle physics: points in the same class attract each other, those from different class repel each other, and the resulting forces are exerted on the anchors of the attributes, that is, on unit vectors of each of the dimensional axis. 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 <p>Plots a heat map for a pair of attributes.</p>
 <p><strong>Inputs</strong></p>
@@ -204,4 +204,4 @@ <h3>Sentiment Analysis</h3>
 <a href="/widget-catalog/visualize/images/__optimized-images__/HeatMap-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2214" height="1732" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/HeatMap-Example2.png"/></a>
 <h2>References</h2>
 <p>Bar-Joseph, Z., Gifford, D.K., Jaakkola, T.S. (2001) Fast optimal leaf ordering for hierarchical clustering, Bioinformatics, 17, 22-29.</p>
-<p>Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares, M., Haussler, D. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences, 1, 262-267.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h3: \"h3\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Heat Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots a heat map for a pair of attributes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Heat_map\",\n        children: \"Heat map\"\n      }), \" is a graphical method for visualizing attribute values in a two-way matrix. It only works on datasets containing numeric variables. The values are represented by color according to the selected color pallette. By combining class variable and attributes on x and y axes, we see where the attribute values are the strongest and where the weakest, thus enabling us to find typical features for each class.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The widget enables row selection with click and drag. One can zoom in with Ctrl++ (Cmd++) and zoom out with Ctrl+- (Cmd+-). Ctrl+0 (Cmd+0) resets zoom to the extended version, while Ctrl+9 (Cmd+9) reset it to the default.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/HeatMap.png\",\n      alt: \"\",\n      width: \"1150\",\n      height: \"1806\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"The color pallette. Choose from linear, diverging, color-blind friendly, or other pallettes. \", _jsx(_components.strong, {\n          children: \"Low\"\n        }), \" and \", _jsx(_components.strong, {\n          children: \"High\"\n        }), \" are thresholds for the color palette (low for attributes with low values and high for attributes with high values). Selecting one of diverging palettes, which have two extreme colors and a neutral (black or white) color at the midpoint, enables an option to set a meaningful mid-point value (default is 0).\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Merge rows. 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Clustering with optimal leaf ordering reorders left and right branches in the dendrogram to minimize the sum of distances between adjacent leaves (Bar-Joseph et al. 2001).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/HeatMap-advanced.png\",\n      alt: \"\",\n      width: \"1516\",\n      height: \"1726\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Gene expresssions\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Heat Map\"\n      }), \" below displays attribute values for the \", _jsx(_components.em, {\n        children: \"brown-selected\"\n      }), \" data set (Brown et al. 2000). Heat maps are particularly appropriate for showing gene expressions and the brown-selected data set contains yeast gene expressions at different conditions.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Heat map shows low expressions in blue and high expressions in yellow and white. For better organization, we added \", _jsx(_components.em, {\n        children: \"Clustering (opt. ordering)\"\n      }), \" to the columns, which puts columns with similar profiles closer together. In this way we can see the conditions that result in low expressions for ribosomal genes in the lower right corner.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Additionally, the plot is enhanced with row color on the right, showing which class the rows belong to.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/HeatMap-Example1.png\",\n      alt: \"\",\n      width: \"2442\",\n      height: \"1732\"\n    }), \"\\n\", _jsx(_components.h3, {\n      children: \"Sentiment Analysis\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Heat maps are great for visualizing any kind of comparable numeric variables, for example sentiment in a collection of documents. We will take \", _jsx(_components.em, {\n        children: \"book-excerpts\"\n      }), \" corpus from the \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget and pass it to the \", _jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \" widget, which computes sentiment scores for each document. The output of sentiment analysis are four columns, positive, negative, and neutral sentiment score, and a compound score that aggregates the previous scores into a single number. Positive compound values (white) represent positive documents, while negative (blue) represent negative documents.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We used row clustering to place similar rows closer together, resulting in clear negative and positive groups. Now we can select negative children's books and explore which are they.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/HeatMap-Example2.png\",\n      alt: \"\",\n      width: \"2214\",\n      height: \"1732\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Bar-Joseph, Z., Gifford, D.K., Jaakkola, T.S. (2001) Fast optimal leaf ordering for hierarchical clustering, Bioinformatics, 17, 22-29.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares, M., Haussler, D. 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+<p>Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares, M., Haussler, D. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences, 1, 262-267.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h3: \"h3\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Heat Map\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots a heat map for a pair of attributes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Heat_map\",\n        children: \"Heat map\"\n      }), \" is a graphical method for visualizing attribute values in a two-way matrix. 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We will take \", _jsx(_components.em, {\n        children: \"book-excerpts\"\n      }), \" corpus from the \", _jsx(_components.strong, {\n        children: \"Corpus\"\n      }), \" widget and pass it to the \", _jsx(_components.strong, {\n        children: \"Sentiment Analysis\"\n      }), \" widget, which computes sentiment scores for each document. The output of sentiment analysis are four columns, positive, negative, and neutral sentiment score, and a compound score that aggregates the previous scores into a single number. Positive compound values (white) represent positive documents, while negative (blue) represent negative documents.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"We used row clustering to place similar rows closer together, resulting in clear negative and positive groups. Now we can select negative children's books and explore which are they.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/HeatMap-Example2.png\",\n      alt: \"\",\n      width: \"2214\",\n      height: \"1732\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"References\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Bar-Joseph, Z., Gifford, D.K., Jaakkola, T.S. (2001) Fast optimal leaf ordering for hierarchical clustering, Bioinformatics, 17, 22-29.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares, M., Haussler, D. 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@@ -203,4 +203,4 @@ <h2>Example</h2>
 <h2>References</h2>
 <p>Koren Y., Carmel L. (2003). Visualization of labeled data using linear transformations. In Proceedings of IEEE Information Visualization 2003, (InfoVis&#x27;03). Available <a href="http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=3DDF0DB68D8AB9949820A19B0344C1F3?doi=10.1.1.13.8657&amp;rep=rep1&amp;type=pdf">here</a>.</p>
 <p>Boulesteix A.-L., Strimmer K. (2006). Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings in Bioinformatics, 8(1), 32-44. Abstract <a href="http://bib.oxfordjournals.org/content/8/1/32.abstract">here</a>.</p>
-<p>Leban G., Zupan B., Vidmar G., Bratko I. (2006). VizRank: Data Visualization Guided by Machine Learning. Data Mining and Knowledge Discovery, 13, 119-136. Available <a href="http://eprints.fri.uni-lj.si/210/2/1._G._Leban%2C_B._Zupan%2C_G._Vidmar%2C_I._Bratko%2C_Data_Mining_and_Knowledge_Discovery_13%2C_119-36_(2006)..pdf">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Linear Projection\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A linear projection method with explorative data analysis.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Projection: custom projection vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Components: projection vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget displays \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Projection_(linear_algebra)\",\n        children: \"linear projections\"\n      }), \" of class-labeled data. 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+<p>Leban G., Zupan B., Vidmar G., Bratko I. (2006). VizRank: Data Visualization Guided by Machine Learning. Data Mining and Knowledge Discovery, 13, 119-136. Available <a href="http://eprints.fri.uni-lj.si/210/2/1._G._Leban%2C_B._Zupan%2C_G._Vidmar%2C_I._Bratko%2C_Data_Mining_and_Knowledge_Discovery_13%2C_119-36_(2006)..pdf">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Linear Projection\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A linear projection method with explorative data analysis.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Projection: custom projection vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Components: projection vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This widget displays \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Projection_(linear_algebra)\",\n        children: \"linear projections\"\n      }), \" of class-labeled data. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Line Plot</h1>
 <p>Visualization of data profiles (e.g., time series).</p>
 <p><strong>Inputs</strong></p>
@@ -183,4 +183,4 @@
 <h2>Example</h2>
 <p><strong>Line Plot</strong> is a standard visualization widget, which displays data profiles, normally of ordered numerical data. In this simple example, we will display the <em>iris</em> data in a line plot, grouped by the iris attribute. The plot shows how petal length nicely separates between class values.</p>
 <p>If we observe this in a <a href="/widget-catalog/visualize/../visualize/scatterplot">Scatter Plot</a>, we can confirm this is indeed so. Petal length is an interesting attribute for separation of classes, especially when enhanced with petal width, which is also nicely separated in the line plot.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/LinePlot-Example.png" data-gallery="true"><img alt="" loading="lazy" width="2734" height="1730" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/LinePlot-Example.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Line Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualization of data profiles (e.g., time series).\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Line_chart\",\n        children: \"Line plot\"\n      }), \" a type of plot which displays the data as a series of points, connected by straight line segments. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Mosaic Display</h1>
 <p>Display data in a mosaic plot.</p>
 <p><strong>Inputs</strong></p>
@@ -174,4 +174,4 @@
 <h2>Example</h2>
 <p>We loaded the <em>titanic</em> dataset and connected it to the <strong>Mosaic Display</strong> widget. We decided to focus on two variables, namely status, sex and survival. We colored the interiors according to Pearson residuals in order to demonstrate the difference between observed and fitted values.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/Mosaic-Display-Example.png" data-gallery="true"><img alt="" loading="lazy" width="920" height="934" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Mosaic-Display-Example.png"/></a>
-<p>We can see that the survival rates for men and women clearly deviate from the fitted value.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Mosaic Display\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Display data in a mosaic plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected data: instances selected from the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Mosaic plot\"\n      }), \" is a graphical representation of a two-way frequency table or a contingency table. 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+<p>We can see that the survival rates for men and women clearly deviate from the fitted value.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Mosaic Display\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Display data in a mosaic plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected data: instances selected from the plot\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Mosaic plot\"\n      }), \" is a graphical representation of a two-way frequency table or a contingency table. 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@@ -182,4 +182,4 @@ <h4>Features output</h4>
 <p>The second example shows how to use the Features output. Let us use <em>heart_disease</em> data for this exercise and load it in the File widget. Now connect File to <a href="../model/naivebayes.md">Naive Bayes</a> (or <a href="../model/logisticregression.md">Logistic Regression</a>) and add Nomogram to Naive Bayes. Finally, connect File to <a href="/widget-catalog/visualize/../data/selectcolumns">Select Columns</a>.</p>
 <p>Select Columns selects a subset of variables, while Nomogram shows the top scoring variables for the trained classifier. To filter the data by the variables selected in the Nomogram, connect Nomogram to Select Columns as shown below. Nomogram will pass a list of selected variables to Select Columns, which will retain only the variables from the list. For this to work, you have to press <em>Use input features</em> in Select Columns (or tick it to always apply it).</p>
 <p>We have selected the top 5 variables in Nomogram and used Select Columns to retain only those variables.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/Nomogram-Features.png" data-gallery="true"><img alt="" loading="lazy" width="2516" height="1594" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Nomogram-Features.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Nomogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nomograms for visualization of Naive Bayes and Logistic Regression classifiers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Classifier: trained classifier\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Features: selected variables, 10 by default\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Nomogram\"\n      }), \" enables some classifier's (more precisely Naive Bayes classifier and Logistic Regression classifier) visual representation. 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It is possible to choose from 'No sorting', 'Name', 'Absolute importance', 'Positive influence' and 'Negative influence' for Naive Bayes representation and from 'No sorting', 'Name' and 'Absolute importance' for Logistic Regression representation.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The probability for the chosen target class is computed by '1-vs-all' principle, which should be taken in consideration when dealing with multiclass data (alternating probabilities do not sum to 1). To avoid this inconvenience, you can choose to normalize probabilities.\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Nomogram-NaiveBayes.png\",\n      alt: \"\",\n      width: \"893\",\n      height: \"534\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Select the target class you want to model the probability for. Select, whether you want to normalize the probabilities or not.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"By default Scale is set to Log odds ration. For easier understanding and interpretation option \", _jsx(_components.em, {\n          children: \"Point scale\"\n        }), \" can be used. The unit is obtained by re-scaling the log odds so that the maximal absolute log odds ratio in the nomogram represents 100 points.\"]\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Display all attributes or only the best ranked ones. Sort them and set the projection type.\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Continuous attributes can be plotted in 2D (only for Logistic Regression).\"\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Nomogram-LogisticRegression.png\",\n      alt: \"logreg\",\n      width: \"893\",\n      height: \"534\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Examples\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Nomogram\"\n      }), \" widget should be used immediately after trained classifier widget (e.g. \", _jsx(_components.a, {\n        href: \"../model/naivebayes.md\",\n        children: \"Naive Bayes\"\n      }), \" or \", _jsx(_components.a, {\n        href: \"../model/logisticregression.md\",\n        children: \"Logistics Regression\"\n      }), \"). It can also be passed a data instance using any widget that enables selection (e.g. \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../data/datatable\",\n        children: \"Data Table\"\n      }), \") as shown in the workflow below.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Nomogram-Example.png\",\n      alt: \"\",\n      width: \"1161\",\n      height: \"912\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Referring to the Titanic dataset once again, 1490 (68%) passengers on Titanic out of 2201 died. To make a prediction, the contribution of each attribute is measured as a point score and the individual point scores are summed to determine the probability. When the value of the attribute is unknown, its contribution is 0 points. Therefore, not knowing anything about the passenger, the total point score is 0 and the corresponding probability equals the unconditional prior. The nomogram in the example shows the case when we know that the passenger is a male adult from the first class. The points sum to -0.36, with a corresponding probability of not surviving of about 53%.\"\n    }), \"\\n\", _jsx(_components.h4, {\n      children: \"Features output\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The second example shows how to use the Features output. Let us use \", _jsx(_components.em, {\n        children: \"heart_disease\"\n      }), \" data for this exercise and load it in the File widget. Now connect File to \", _jsx(_components.a, {\n        href: \"../model/naivebayes.md\",\n        children: \"Naive Bayes\"\n      }), \" (or \", _jsx(_components.a, {\n        href: \"../model/logisticregression.md\",\n        children: \"Logistic Regression\"\n      }), \") and add Nomogram to Naive Bayes. Finally, connect File to \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../data/selectcolumns\",\n        children: \"Select Columns\"\n      }), \".\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Select Columns selects a subset of variables, while Nomogram shows the top scoring variables for the trained classifier. To filter the data by the variables selected in the Nomogram, connect Nomogram to Select Columns as shown below. Nomogram will pass a list of selected variables to Select Columns, which will retain only the variables from the list. 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+<a href="/widget-catalog/visualize/images/__optimized-images__/Nomogram-Features.png" data-gallery="true"><img alt="" loading="lazy" width="2516" height="1594" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Nomogram-Features.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\",\n    a: \"a\",\n    h4: \"h4\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Nomogram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Nomograms for visualization of Naive Bayes and Logistic Regression classifiers.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Classifier: trained classifier\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Features: selected variables, 10 by default\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Nomogram\"\n      }), \" enables some classifier's (more precisely Naive Bayes classifier and Logistic Regression classifier) visual representation. 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 <p>Pythagorean forest for visualizing random forests.</p>
 <p><strong>Inputs</strong></p>
@@ -182,4 +182,4 @@ <h2>Example</h2>
 <p>Then we&#x27;ve selected a tree in the visualization and inspected it further with <a href="/widget-catalog/visualize/../visualize/pythagoreantree">Pythagorean Tree</a> widget.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Forest-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1339" height="1006" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Forest-Example.png"/></a>
 <h2>References</h2>
-<p>Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). Generalized Pythagoras Trees for Visualizing Hierarchies. In IVAPP &#x27;14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pythagorean Forest\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pythagorean forest for visualizing random forests.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Random Forest: tree models from random forest\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Tree: selected tree model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Pythagorean Forest\"\n      }), \" shows all learned decision tree models from \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../model/randomforest\",\n        children: \"Random Forest\"\n      }), \" widget. 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+<p>Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). Generalized Pythagoras Trees for Visualizing Hierarchies. In IVAPP &#x27;14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pythagorean Forest\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pythagorean forest for visualizing random forests.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Random Forest: tree models from random forest\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Tree: selected tree model\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Pythagorean Forest\"\n      }), \" shows all learned decision tree models from \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../model/randomforest\",\n        children: \"Random Forest\"\n      }), \" widget. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Pythagorean Tree</h1>
 <p>Pythagorean tree visualization for classification or regression trees.</p>
 <p><strong>Inputs</strong></p>
@@ -195,4 +195,4 @@ <h2>Example</h2>
 <p>The selected data instances are shown as a subset in the <a href="../visualize/scatterplot.md">Scatter Plot</a>, sent to the <a href="../data/datatable.md">Data Table</a> and examined in the <a href="/widget-catalog/visualize/../visualize/boxplot">Box Plot</a>. We have used brown-selected dataset in this example. The tree and scatter plot are shown below; the selected node in the tree has a black outline.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Tree-scatterplot.png" data-gallery="true"><img alt="" loading="lazy" width="1054" height="717" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Tree-scatterplot.png"/></a>
 <h2>References</h2>
-<p>Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). <a href="http://publications.fbeck.com/ivapp14-pythagoras.pdf">Generalized Pythagoras Trees for Visualizing Hierarchies</a>. In IVAPP &#x27;14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pythagorean Tree\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pythagorean tree visualization for classification or regression trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Tree: tree model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the tree\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Pythagorean Trees\"\n      }), \" are plane fractals that can be used to depict general tree hierarchies as presented in an article by \", _jsx(_components.a, {\n        href: \"http://publications.fbeck.com/ivapp14-pythagoras.pdf\",\n        children: \"Fabian Beck and co-authors\"\n      }), \". In our case, they are used for visualizing and exploring tree models, such as \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../model/tree\",\n        children: \"Tree\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Tree1-stamped.png\",\n      alt: \"\",\n      width: \"746\",\n      height: \"539\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the input tree model.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Visualization parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Depth\"\n            }), \": set the depth of displayed trees.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Target class\"\n            }), \" (for classification trees): the intensity of the color for nodes of the tree will correspond to the probability of the target class. 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They can both visualize \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../model/tree\",\n        children: \"Tree\"\n      }), \", but Pythagorean visualization takes less space and is more compact, even for a small \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Iris_flower_data_set\",\n        children: \"Iris flower\"\n      }), \" dataset. For both visualization widgets, we have hidden the control area on the left by clicking on the splitter between control and visualization area.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Tree-comparison.png\",\n      alt: \"\",\n      width: \"1128\",\n      height: \"761\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pythagorean Tree is interactive: click on any of the nodes (squares) to select training data instances that were associated with that node. 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\ No newline at end of file
+<p>Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). <a href="http://publications.fbeck.com/ivapp14-pythagoras.pdf">Generalized Pythagoras Trees for Visualizing Hierarchies</a>. In IVAPP &#x27;14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Pythagorean Tree\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pythagorean tree visualization for classification or regression trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Tree: tree model\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the tree\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [_jsx(_components.strong, {\n        children: \"Pythagorean Trees\"\n      }), \" are plane fractals that can be used to depict general tree hierarchies as presented in an article by \", _jsx(_components.a, {\n        href: \"http://publications.fbeck.com/ivapp14-pythagoras.pdf\",\n        children: \"Fabian Beck and co-authors\"\n      }), \". In our case, they are used for visualizing and exploring tree models, such as \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../model/tree\",\n        children: \"Tree\"\n      }), \".\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Tree1-stamped.png\",\n      alt: \"\",\n      width: \"746\",\n      height: \"539\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Information on the input tree model.\"\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Visualization parameters:\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Depth\"\n            }), \": set the depth of displayed trees.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Target class\"\n            }), \" (for classification trees): the intensity of the color for nodes of the tree will correspond to the probability of the target class. 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They can both visualize \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/../model/tree\",\n        children: \"Tree\"\n      }), \", but Pythagorean visualization takes less space and is more compact, even for a small \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Iris_flower_data_set\",\n        children: \"Iris flower\"\n      }), \" dataset. For both visualization widgets, we have hidden the control area on the left by clicking on the splitter between control and visualization area.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Pythagorean-Tree-comparison.png\",\n      alt: \"\",\n      width: \"1128\",\n      height: \"761\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Pythagorean Tree is interactive: click on any of the nodes (squares) to select training data instances that were associated with that node. 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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Radviz</h1>
 <p>Radviz vizualization with explorative data analysis and intelligent data
 visualization enhancements.</p>
@@ -176,4 +176,4 @@ <h2>References</h2>
 <p>Hoffman, P. E. et al. (1997) DNA visual and analytic data mining. In the Proceedings of the IEEE Visualization. Phoenix, AZ, pp. 437-441.</p>
 <p>Brown, M. P., W. N. Grundy et al. (2000). &quot;Knowledge-based analysis of microarray gene expression data by using support vector machines.&quot; Proc Natl Acad Sci U S A 97(1): 262-7.</p>
 <p>Leban, G., B. Zupan et al. (2006). &quot;VizRank: Data Visualization Guided by Machine Learning.&quot; Data Mining and Knowledge Discovery 13(2): 119-136.</p>
-<p>Mramor, M., G. Leban, J. Demsar, and B. Zupan. Visualization-based cancer microarray data classification analysis. Bioinformatics 23(16): 2147-2154, 2007.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Radviz\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Radviz vizualization with explorative data analysis and intelligent data\\nvisualization enhancements.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Components: Radviz vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Radviz (Hoffman et al. 1997) is a non-linear multi-dimensional visualization technique that can display data defined by three or more variables in a 2-dimensional projection. 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+<p>Mramor, M., G. Leban, J. Demsar, and B. Zupan. Visualization-based cancer microarray data classification analysis. Bioinformatics 23(16): 2147-2154, 2007.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Radviz\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Radviz vizualization with explorative data analysis and intelligent data\\nvisualization enhancements.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Components: Radviz vectors\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Radviz (Hoffman et al. 1997) is a non-linear multi-dimensional visualization technique that can display data defined by three or more variables in a 2-dimensional projection. 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Series","keywords":[],"background":"#33aaff","icon":"widget-icons/Time Series-Yahoo Finance.png","url":"yahoo_finance"},{"title":"As Timeseries","category":"Time Series","keywords":[],"background":"#33aaff","icon":"widget-icons/Time Series-As Timeseries.png","url":"as_timeseries"},{"title":"Interpolate","category":"Time Series","keywords":[],"background":"#33aaff","icon":"widget-icons/Time Series-Interpolate.png","url":"interpolate"},{"title":"Moving Transform","category":"Time Series","keywords":[],"background":"#33aaff","icon":"widget-icons/Time Series-Moving Transform.png","url":"moving_transform_w"},{"title":"Line Chart","category":"Time Series","keywords":[],"background":"#33aaff","icon":"widget-icons/Time Series-Line Chart.png","url":"line_chart"},{"title":"Periodogram","category":"Time Series","keywords":[],"background":"#33aaff","icon":"widget-icons/Time Series-Periodogram.png","url":"periodogram_w"},{"title":"Correlogram","category":"Time 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Scatter Plot</h1>
 <p>Scatter plot visualization with exploratory analysis and intelligent data visualization enhancements.</p>
 <p><strong>Inputs</strong></p>
@@ -204,4 +204,4 @@ <h2>Example</h2>
 <p>The <strong>Scatter Plot</strong> can be combined with any widget that outputs a list of selected data instances. In the example below, we combine <a href="/widget-catalog/visualize/../model/tree">Tree</a> and <strong>Scatter Plot</strong> to display instances taken from a chosen decision tree node (clicking on any node of the tree will send a set of selected data instances to the scatter plot and mark selected instances with filled symbols).</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/ScatterPlotExample-Classification.png" data-gallery="true"><img alt="" loading="lazy" width="1534" height="1086" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/ScatterPlotExample-Classification.png"/></a>
 <h2>References</h2>
-<p>Gregor Leban and Blaz Zupan and Gaj Vidmar and Ivan Bratko (2006) VizRank: Data Visualization Guided by Machine Learning. Data Mining and Knowledge Discovery, 13 (2). pp. 119-136. Available <a href="http://eprints.fri.uni-lj.si/210/">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    code: \"code\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Scatter Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot visualization with exploratory analysis and intelligent data visualization enhancements.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: list of attributes\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \" widget provides a 2-dimensional scatter plot visualization. The data is displayed as a collection of points, each having the value of the x-axis attribute determining the position on the horizontal axis and the value of the y-axis attribute determining the position on the vertical axis. Various properties of the graph, like color, size and shape of the points, axis titles, maximum point size and jittering can be adjusted on the left side of the widget. A snapshot below shows the scatter plot of the \", _jsx(_components.em, {\n        children: \"Iris\"\n      }), \" dataset with the coloring matching of the class attribute.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Scatterplot-Iris-stamped.png\",\n      alt: \"\",\n      width: \"1790\",\n      height: \"1492\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Select the x and y attribute. Optimize your projection with \", _jsx(_components.strong, {\n          children: \"Find Informative Projections\"\n        }), \". This feature scores attribute pairs by average classification accuracy and returns the top scoring pair with a simultaneous visualization update.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Attributes\"\n        }), \": Set the color of the displayed points (you will get colors for categorical values and blue-green-yellow points for numeric). Set label, shape and size to differentiate between points. \", _jsx(_components.em, {\n          children: \"Label only selected points\"\n        }), \" allows you to select individual data instances and label only those.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"Set symbol size and opacity for all data points. Set \", _jsx(_components.a, {\n          href: \"https://en.wikipedia.org/wiki/Jitter\",\n          children: \"jittering\"\n        }), \" to prevent the dots overlapping. Jittering will randomly scatter point only around categorical values. If \", _jsx(_components.em, {\n          children: \"Jitter numeric values\"\n        }), \" is checked, points are also scattered around their actual numeric values.\", \"\\n\", _jsxs(_components.ul, {\n          children: [\"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show color regions\"\n            }), \" colors the graph by class (see the screenshot below).\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show legend\"\n            }), \" displays a legend on the right. Click and drag the legend to move it.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show gridlines\"\n            }), \" displays the grid behind the plot.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show all data on mouse hover\"\n            }), \" enables information bubbles if the cursor is placed on a dot.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Show regression line\"\n            }), \" draws the regression line for pair of numeric attributes. If a categorical variable is selected for coloring the plot, individual regression lines for each class value will be displayed. The reported r value corresponds to the \", _jsx(_components.code, {\n              children: \"rvalue\"\n            }), \" from \", _jsx(_components.a, {\n              href: \"https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html\",\n              children: \"linear least-squares regression\"\n            }), \", which is equal to the Pearson's correlation coefficient.\"]\n          }), \"\\n\", _jsxs(_components.li, {\n            children: [_jsx(_components.em, {\n              children: \"Treat variables as independent\"\n            }), \" fits regression line to a group of points (minimize distance from points), rather than fitting y as a function of x (minimize vertical distances).\"]\n          }), \"\\n\"]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [_jsx(_components.em, {\n          children: \"Select, zoom, pan and zoom to fit\"\n        }), \" are the options for exploring the graph. The manual selection of data instances works as an angular/square selection tool. Double click to move the projection. Scroll in or out for zoom.\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"If \", _jsx(_components.em, {\n          children: \"Send automatically\"\n        }), \" is ticked, changes are communicated automatically. Alternatively, press \", _jsx(_components.em, {\n          children: \"Send\"\n        }), \".\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Here is an example of the \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \" widget if the \", _jsx(_components.em, {\n        children: \"Show color regions\"\n      }), \" and \", _jsx(_components.em, {\n        children: \"Show regression line\"\n      }), \" boxes are ticked.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/Scatterplot-ClassDensity.png\",\n      alt: \"\",\n      width: \"1790\",\n      height: \"1492\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Intelligent Data Visualization\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If a dataset has many attributes, it is impossible to manually scan through all the pairs to find interesting or useful scatter plots. Orange implements intelligent data visualization with the \", _jsx(_components.strong, {\n        children: \"Find Informative Projections\"\n      }), \" option in the widget.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"If a categorical variable is selected in the Color section, the \", _jsx(_components.a, {\n        href: \"http://eprints.fri.uni-lj.si/210/\",\n        children: \"score\"\n      }), \" is computed as follows. For each data instance, the method finds 10 nearest neighbors in the projected 2D space, that is, on the combination of attribute pairs. It then checks how many of them have the same color. The total score of the projection is then the average number of same-colored neighbors.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Computation for numeric colors is similar, except that the \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Coefficient_of_determination\",\n        children: \"coefficient of determination\"\n      }), \" is used for measuring the local homogeneity of the projection.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"To use this method, go to the \", _jsx(_components.em, {\n        children: \"Find Informative Projections\"\n      }), \" option in the widget, open the subwindow and press \", _jsx(_components.em, {\n        children: \"Start Evaluation\"\n      }), \". The feature will return a list of attribute pairs by average classification accuracy score.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Below, there is an example demonstrating the utility of ranking. 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Notice that the scatter plot doesn't show all 52 data instances, because some data instances overlap (they have the same values for both attributes used).\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/ScatterPlotExample-Explorative.png\",\n      alt: \"\",\n      width: \"1428\",\n      height: \"747\"\n    }), \"\\n\", _jsx(_components.h2, {\n      children: \"Example\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \" can be combined with any widget that outputs a list of selected data instances. 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Available \", _jsx(_components.a, {\n        href: \"http://eprints.fri.uni-lj.si/210/\",\n        children: \"here\"\n      }), \".\"]\n    })]\n  });\n}\nfunction MDXContent(props = {}) {\n  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);\n  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {\n    children: _jsx(_createMdxContent, props)\n  })) : _createMdxContent(props);\n}\nreturn {\n  default: MDXContent\n};\n","frontmatter":{},"scope":{}},"widgetCatalog":[["Data",[{"title":"File","category":"Data","keywords":["file","load","read","open"],"background":"#FFD39F","icon":"widget-icons/Data-File.png","url":"file"},{"title":"CSV File Import","category":"Data","keywords":["csv file import","file","load","read","open","csv"],"background":"#FFD39F","icon":"widget-icons/Data-CSV File 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+<p>Gregor Leban and Blaz Zupan and Gaj Vidmar and Ivan Bratko (2006) VizRank: Data Visualization Guided by Machine Learning. Data Mining and Knowledge Discovery, 13 (2). pp. 119-136. Available <a href="http://eprints.fri.uni-lj.si/210/">here</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    em: \"em\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    code: \"code\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Scatter Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Scatter plot visualization with exploratory analysis and intelligent data visualization enhancements.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data Subset: subset of instances\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Features: list of attributes\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Scatter Plot\"\n      }), \" widget provides a 2-dimensional scatter plot visualization. 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@@ -177,4 +177,4 @@ <h2>Example</h2>
 <p>The <strong>Sieve Diagram</strong> also features the <em>Score Combinations</em> option, which makes the ranking of attributes even easier.</p>
 <a href="/widget-catalog/visualize/images/SieveDiagram-Example1.PNG" data-gallery="true"><img alt="" loading="lazy" width="1045" height="979" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/SieveDiagram-Example1.PNG"/></a>
 <h2>References</h2>
-<p>Riedwyl, H., and Schüpbach, M. (1994). Parquet diagram to plot contingency tables. In Softstat &#x27;93: Advances in Statistical Software, F. Faulbaum (Ed.). New York: Gustav Fischer, 293-299.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Sieve Diagram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots a sieve diagram for a pair of attributes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.strong, {\n        children: \"Sieve Diagram\"\n      }), \" is a graphical method for visualizing frequencies in a two-way contingency table and comparing them to \", _jsx(_components.a, {\n        href: \"http://cnx.org/contents/d396c4ad-2fd7-47cd-be84-152b44880feb@2/What-is-an-expected-frequency\",\n        children: \"expected frequencies\"\n      }), \" under assumption of independence. It was proposed by Riedwyl and Schüpbach in a technical report in 1983 and later called a parquet diagram (Riedwyl and Schüpbach 1994). In this display, the area of each rectangle is proportional to the expected frequency, while the observed frequency is shown by the number of squares in each rectangle. 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+<p>Riedwyl, H., and Schüpbach, M. (1994). Parquet diagram to plot contingency tables. In Softstat &#x27;93: Advances in Statistical Software, F. Faulbaum (Ed.). New York: Gustav Fischer, 293-299.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Sieve Diagram\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Plots a sieve diagram for a pair of attributes.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"A \", _jsx(_components.strong, {\n        children: \"Sieve Diagram\"\n      }), \" is a graphical method for visualizing frequencies in a two-way contingency table and comparing them to \", _jsx(_components.a, {\n        href: \"http://cnx.org/contents/d396c4ad-2fd7-47cd-be84-152b44880feb@2/What-is-an-expected-frequency\",\n        children: \"expected frequencies\"\n      }), \" under assumption of independence. It was proposed by Riedwyl and Schüpbach in a technical report in 1983 and later called a parquet diagram (Riedwyl and Schüpbach 1994). In this display, the area of each rectangle is proportional to the expected frequency, while the observed frequency is shown by the number of squares in each rectangle. 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+<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><meta name="description" content="Orange Data Mining Toolbox"/><meta property="og:description" content="Orange Data Mining Toolbox"/><meta property="og:url" content="https://orangedatamining.com"/><meta property="og:type" content="website"/><meta property="og:image" content="/_next/static/media/orange-cover.6d606323.png"/><meta property="og:image:width" content="1033"/><meta property="og:image:height" content="626"/><meta property="og:site_name" content="Orange Data Mining"/><meta name="author" content="Bioinformatics Laboratory, University of Ljubljana"/><title>Orange Data Mining - undefined</title><meta name="robots" content="index,follow"/><meta property="og:title" content="Orange Data Mining - undefined"/><meta name="next-head-count" content="14"/><link rel="preload" href="/_next/static/media/a9b61b60c2d733b4-s.p.woff2" as="font" type="font/woff2" crossorigin="anonymous" data-next-font="size-adjust"/><link rel="preload" href="/_next/static/css/984cb0988e66ab60.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/984cb0988e66ab60.css" crossorigin="" data-n-g=""/><link rel="preload" href="/_next/static/css/4ac7ed34d61ef456.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/4ac7ed34d61ef456.css" crossorigin="" data-n-p=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-21c828b96ad33382.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-0c7baedefba6b077.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-61a1de8ce4711ed8.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-1340827672ec5913.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/690-eebe7c6b2c5b12a7.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/widget-catalog/%5Bcategory%5D/%5Bwidget%5D-43b4b1cc219f30f4.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/94QTGfoo8u2d9JarndNd4/_ssgManifest.js" defer="" crossorigin=""></script><style data-styled="" data-styled-version="5.3.6">.eVggRJ{max-width:1296px;margin-right:auto;margin-left:auto;padding-left:72px;padding-right:72px;height:100%;box-sizing:content-box;}/*!sc*/
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 </style></head><body><div id="__next"><div class="sc-b34fb00f-0 eWBvvS __className_fa886a"><nav class="sc-a026f25c-0 dAXQuQ"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-a026f25c-1 gUFsBX"><a href="/"><img alt="Orange Logo" loading="lazy" width="120" height="35" decoding="async" data-nimg="1" class="img-logo" style="color:transparent" src="/_next/static/media/logo-orange.faff1861.svg"/></a><div><button aria-label="Toggle navigation" class="sc-46d6b9d9-0 eWdALT"><div></div><div></div><div></div></button><div class="sc-a026f25c-2 bcvGIl"><ul class="sc-a026f25c-3 jjIqRA"><li><a href="/screenshots/">Screenshots</a></li><li><a href="/examples/">Examples</a></li><li><a href="/download/">Download</a></li><li><a href="/blog/">Blog</a></li><li><a href="/docs/">Docs</a></li><li><a href="/workshops/">Workshops</a></li></ul><form class="sc-a026f25c-4 eLPewT"><div class="sc-a026f25c-5 ipGgXV">Search</div><button class="sc-a026f25c-6 iFVURz"><img alt="Icon for search" loading="lazy" width="18" height="17" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/icon-search.459b2665.svg"/><span class="sc-cdea7863-0 jTcDmc">Search through page</span></button></form></div></div></div></div></nav><main><div class="sc-61db3aea-0 hRMVSr"><div class="sc-509753a1-4 jsKdTl"><nav class="sc-509753a1-6 kosXkm"><h3 class="sc-509753a1-0 fBwvec">Widgets</h3><h4 class="sc-509753a1-1 elaUVR">Data</h4><h4 class="sc-509753a1-1 elaUVR">Transform</h4><h4 class="sc-509753a1-1 elaUVR">Visualize</h4><ul class="sc-509753a1-2 kpckfe"><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/treeviewer/">Tree Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/boxplot/">Box Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/violinplot/">Violin Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/distributions/">Distributions</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/scatterplot/">Scatter Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/lineplot/">Line Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/barplot/">Bar Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/sievediagram/">Sieve Diagram</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/mosaicdisplay/">Mosaic Display</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/freeviz/">FreeViz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/linearprojection/">Linear Projection</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/radviz/">Radviz</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/heatmap/">Heat Map</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/venndiagram/">Venn Diagram</a></li><li><a class="sc-509753a1-3 eAVfkB" href="/widget-catalog/visualize/silhouetteplot/">Silhouette Plot</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreantree/">Pythagorean Tree</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/pythagoreanforest/">Pythagorean Forest</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/cn2ruleviewer/">CN2 Rule Viewer</a></li><li><a class="sc-509753a1-3 kpmImk" href="/widget-catalog/visualize/nomogram/">Nomogram</a></li></ul><h4 class="sc-509753a1-1 elaUVR">Model</h4><h4 class="sc-509753a1-1 elaUVR">Evaluate</h4><h4 class="sc-509753a1-1 elaUVR">Unsupervised</h4><h4 class="sc-509753a1-1 elaUVR">Spectroscopy</h4><h4 class="sc-509753a1-1 elaUVR">Text Mining</h4><h4 class="sc-509753a1-1 elaUVR">Survival Analysis</h4><h4 class="sc-509753a1-1 elaUVR">Bioinformatics</h4><h4 class="sc-509753a1-1 elaUVR">Single Cell</h4><h4 class="sc-509753a1-1 elaUVR">Image Analytics</h4><h4 class="sc-509753a1-1 elaUVR">Networks</h4><h4 class="sc-509753a1-1 elaUVR">Geo</h4><h4 class="sc-509753a1-1 elaUVR">Educational</h4><h4 class="sc-509753a1-1 elaUVR">Time Series</h4><h4 class="sc-509753a1-1 elaUVR">Associate</h4><h4 class="sc-509753a1-1 elaUVR">Explain</h4></nav><div class="sc-509753a1-5 laBPrS"><div class="sc-8f0614a2-0 dpPsin"><div class="lg-react-element "><h1>Silhouette Plot</h1>
 <p>A graphical representation of consistency within clusters of data.</p>
 <p><strong>Inputs</strong></p>
@@ -188,4 +188,4 @@
 <h2>Example</h2>
 <p>In the snapshot below, we have decided to use the <strong>Silhouette Plot</strong> on the <em>iris</em> dataset. We selected data instances with low silhouette scores and passed them on as a subset to the <a href="/widget-catalog/visualize/../visualize/scatterplot">Scatter Plot</a> widget. This visualization only confirms the accuracy of the <strong>Silhouette Plot</strong> widget, as you can clearly see that the subset lies in the border between two clusters.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/SilhouettePlot-Example.png" data-gallery="true"><img alt="" loading="lazy" width="1271" height="846" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/SilhouettePlot-Example.png"/></a>
-<p>If you are interested in other uses of the <strong>Silhouette Plot</strong> widget, feel free to explore our <a href="http://blog.biolab.si/2016/03/23/all-i-see-is-silhouette/">blog post</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Silhouette Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A graphical representation of consistency within clusters of data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Silhouette Plot\"\n      }), \" widget offers a graphical representation of consistency within clusters of data and provides the user with the means to visually assess cluster quality. 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\ No newline at end of file
+<p>If you are interested in other uses of the <strong>Silhouette Plot</strong> widget, feel free to explore our <a href="http://blog.biolab.si/2016/03/23/all-i-see-is-silhouette/">blog post</a>.</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    a: \"a\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Silhouette Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A graphical representation of consistency within clusters of data.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Silhouette Plot\"\n      }), \" widget offers a graphical representation of consistency within clusters of data and provides the user with the means to visually assess cluster quality. The silhouette score is a measure of how similar an object is to its own cluster in comparison to other clusters and is crucial in the creation of a silhouette plot. The silhouette score close to 1 indicates that the data instance is close to the center of the cluster and instances possessing the silhouette scores close to 0 are on the border between two clusters.\"]\n    }), \"\\n\", _jsx(_components.img, {\n      src: \"/widget-catalog/visualize/images/__optimized-images__/SilhouettePlot.png\",\n      alt: \"\",\n      width: \"1342\",\n      height: \"1328\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"Choose the distance metric. 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@@ -198,4 +198,4 @@ <h2>Examples</h2>
 <p><strong>Tree Viewer</strong> can also export labeled data. Connect <a href="/widget-catalog/visualize/../data/datatable">Data Table</a> to <strong>Tree Viewer</strong> and set the link between widgets to <em>Data</em> instead of <em>Selected Data</em>. This will send the entire data to <strong>Data Table</strong> with an additional meta column labeling selected data instances (<em>Yes</em> for selected and <em>No</em> for the remaining).</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/TreeViewer-selection.png" data-gallery="true"><img alt="" loading="lazy" width="2864" height="1480" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/TreeViewer-selection.png"/></a>
 <p>Finally, <strong>Tree Viewer</strong> can be used also for visualizing regression trees. Connect <a href="../model/randomforest.md">Random Forest</a> to <a href="/widget-catalog/visualize/../data/file">File</a> widget using <em>housing.tab</em> dataset. Then connect <a href="../visualize/pythagoreanforest.md">Pythagorean Forest</a> to <strong>Random Forest</strong>. In <strong>Pythagorean Forest</strong> select a regression tree you wish to further analyze and pass it to the <strong>Tree Viewer</strong>. The widget will display the constructed tree. For visualizing larger trees, especially for regression, <a href="/widget-catalog/visualize/../visualize/pythagoreantree">Pythagorean Tree</a> could be a better option.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/TreeViewer-regression.png" data-gallery="true"><img alt="" loading="lazy" width="2196" height="1578" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/TreeViewer-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tree Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A visualization of classification and regression trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Tree: decision tree\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the tree node\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a versatile widget with 2-D visualization of \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Decision_tree_learning\",\n        children: \"classification and regression trees\"\n      }), \". 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+<a href="/widget-catalog/visualize/images/__optimized-images__/TreeViewer-regression.png" data-gallery="true"><img alt="" loading="lazy" width="2196" height="1578" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/TreeViewer-regression.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Tree Viewer\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"A visualization of classification and regression trees.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Tree: decision tree\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the tree node\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a versatile widget with 2-D visualization of \", _jsx(_components.a, {\n        href: \"https://en.wikipedia.org/wiki/Decision_tree_learning\",\n        children: \"classification and regression trees\"\n      }), \". 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@@ -181,4 +181,4 @@ <h2>Examples</h2>
 <a href="/widget-catalog/visualize/images/__optimized-images__/VennDiagram-Example1.png" data-gallery="true"><img alt="" loading="lazy" width="2366" height="1844" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/VennDiagram-Example1.png"/></a>
 <p>The <strong>Venn Diagram</strong> widget can be also used for exploring different prediction models. In the following example, we analysed 3 prediction methods, namely <a href="../model/naivebayes.md">Naive Bayes</a>, <a href="../model/svm.md">SVM</a> and <a href="/widget-catalog/visualize/../model/randomforest">Random Forest</a>, according to their misclassified instances.</p>
 <p>By selecting misclassifications in the three <a href="../evaluate/confusionmatrix.md">Confusion Matrix</a> widgets and sending them to Venn diagram, we can see all the misclassification instances visualized per method used. Then we open <strong>Venn Diagram</strong> and select, for example, the misclassified instances that were identified by all three methods. This is represented as an intersection of all three circles. Click on the intersection to see this two instances marked in the <a href="/widget-catalog/visualize/../visualize/scatterplot">Scatter Plot</a> widget. Try selecting different diagram sections to see how the scatter plot visualization changes.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/VennDiagram-Example2.png" data-gallery="true"><img alt="" loading="lazy" width="2706" height="1952" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/VennDiagram-Example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    a: \"a\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    img: \"img\",\n    ol: \"ol\",\n    h2: \"h2\",\n    em: \"em\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Venn Diagram\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Plots a \", _jsx(_components.a, {\n        href: \"http://en.wikipedia.org/wiki/Venn_diagram\",\n        children: \"Venn diagram\"\n      }), \" for two or more data subsets.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: entire data with a column indicating whether an instance was selected or not\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Venn Diagram\"\n      }), \" widget displays logical relations between datasets by showing the number of common data instances (rows) or the number of shared features (columns). 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@@ -189,4 +189,4 @@ <h2>Examples</h2>
 <p>The <strong>Violin Plot</strong> widget is most commonly used immediately after the <a href="/widget-catalog/visualize/../data/file">File</a> widget to observe the statistical properties of a dataset. In the first example, we have used <em>heart-disease</em> data to inspect our variables.</p>
 <a href="/widget-catalog/visualize/images/__optimized-images__/ViolinPlot-example1.png" data-gallery="true"><img alt="" loading="lazy" width="1712" height="1314" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/ViolinPlot-example1.png"/></a>
 <p>The <strong>Violin Plot</strong> could also be used for <em>outlier detection</em>. In the next example we eliminate the outliers by selecting only instances that fall inside the <a href="https://en.wikipedia.org/wiki/Interquartile_range">Q1 − 1.5  and Q3 + 1.5 IQR</a>.</p>
-<a href="/widget-catalog/visualize/images/__optimized-images__/ViolinPlot-example2.png" data-gallery="true"><img alt="" loading="lazy" width="1716" height="1532" decoding="async" data-nimg="1" style="color:transparent" src="/widget-catalog/visualize/images/__optimized-images__/ViolinPlot-example2.png"/></a></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h1: \"h1\",\n    p: \"p\",\n    strong: \"strong\",\n    ul: \"ul\",\n    li: \"li\",\n    a: \"a\",\n    img: \"img\",\n    ol: \"ol\",\n    em: \"em\",\n    h2: \"h2\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h1, {\n      children: \"Violin Plot\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Visualize the distribution of feature values in a violin plot.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Inputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Data: input dataset\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: _jsx(_components.strong, {\n        children: \"Outputs\"\n      })\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Selected Data: instances selected from the plot\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"Data: data with an additional column showing whether a point is selected\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"The \", _jsx(_components.strong, {\n        children: \"Violin Plot\"\n      }), \" widget plays a similar role as a \", _jsx(_components.a, {\n        href: \"/widget-catalog/visualize/boxplot\",\n        children: \"Box Plot\"\n      }), \". 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@@ -237,4 +237,4 @@ <h3>Requirements</h3>
 <ul>
 <li>No prior knowledge on data science, machine learning, or statistics is required.</li>
 <li>Bring your own laptop.</li>
-</ul></div></div></div><a href="/training-inquiry" class="sc-b6ea565a-0 jyxOJT">Get in touch</a></div><img alt="Data Mining for Business" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="sc-3e325082-8 dZaYgE" style="color:transparent" src="/workshops/Data-Mining-for-Business.jpg"/></div><div class="sc-3e325082-2 iLHXSV"><p>“Playing with data is fun. It&#x27;s like a detective story, where data gives you clues and you dig ever deeper into the mystery until finding the hidden treasure, the cunning murderer, or the mischievous gene.”</p><div class="sc-3e325082-1 TfGkw"><img alt="Janez Demšar" loading="lazy" width="150" height="150" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/janez_demsar.61d43983.png"/><div><p><b>Janez Demšar, prof. dr.</b></p><p>Lecturer</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Data Mining for Business","image":{"width":800,"height":450,"src":"/workshops/Data-Mining-for-Business.jpg"},"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h3, {\n      children: \"Why should you attend?\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"You will learn about latest data science and machine learning approaches that specifically address business problems.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"After completing the course, you will be able to predict churn, segment customers, detect brand sentiment and recommend shopping carts.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"We will emphasize on intuition. 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+</ul></div></div></div><a href="/training-inquiry" class="sc-b6ea565a-0 jyxOJT">Get in touch</a></div><img alt="Data Mining for Business" loading="lazy" width="800" height="450" decoding="async" data-nimg="1" class="sc-3e325082-8 dZaYgE" style="color:transparent" src="/workshops/Data-Mining-for-Business.jpg"/></div><div class="sc-3e325082-2 iLHXSV"><p>“Playing with data is fun. It&#x27;s like a detective story, where data gives you clues and you dig ever deeper into the mystery until finding the hidden treasure, the cunning murderer, or the mischievous gene.”</p><div class="sc-3e325082-1 TfGkw"><img alt="Janez Demšar" loading="lazy" width="150" height="150" decoding="async" data-nimg="1" style="color:transparent" src="/_next/static/media/janez_demsar.61d43983.png"/><div><p><b>Janez Demšar, prof. dr.</b></p><p>Lecturer</p></div></div></div></div></div></main><footer class="sc-62144270-0 bhDsrC"><div class="sc-61db3aea-0 eVggRJ"><div class="sc-62144270-1 cLlhlS"><div><h3>Orange</h3><ul><li><a href="/faq/">FAQ</a></li><li><a href="/license/">License</a></li><li><a href="/privacy/">Privacy</a></li><li><a href="/citation/">Citation</a></li><li><a href="/contact/">Contact</a></li></ul></div><div><h3>Download</h3><ul><li><a href="/download/#win">Windows</a></li><li><a href="/download/#mac">Mac OS</a></li></ul></div><div><h3>Community</h3><ul><li><a href="https://twitter.com/OrangeDataMiner">Twitter</a></li><li><a href="https://www.facebook.com/orangedatamining">Facebook</a></li><li><a href="https://datascience.stackexchange.com/questions/tagged/orange">Stack Exchange</a></li><li><a href="https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g">YouTube</a></li><li><a href="https://discord.com/invite/FWrfeXV">Discord</a></li></ul></div><div><h3>Documentation</h3><ul><li><a href="/getting-started/">Get started</a></li><li><a href="/widget-catalog/">Widgets</a></li><li><a href="https://orange3.readthedocs.io/projects/orange-data-mining-library/en/latest/">Scripting</a></li></ul></div><div><h3>Developers</h3><ul><li><a href="https://github.com/biolab/orange3">GitHub</a></li><li><a href="http://docs.biolab.si/3/development/">Getting started</a></li></ul></div><div><a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&amp;hosted_button_id=A76TAX87ZVR3J" target="_blank" rel="noreferrer" class="sc-b6ea565a-0 jyxOJT">Donate to Orange</a></div></div><p>Copyright © University of Ljubljana</p></div></footer><div class="sc-7e1621ad-1 kkwPib"><div><p>This site uses cookies to improve your experience.</p><div class="sc-7e1621ad-0 kOMJoa"><button class="sc-7e1621ad-2 fkWelF">Details</button><button class="sc-7e1621ad-2 dsztgy">Understand</button></div></div></div></div></div><script id="__NEXT_DATA__" type="application/json" crossorigin="">{"props":{"pageProps":{"sections":[{"title":"Data Mining for Business","image":{"width":800,"height":450,"src":"/workshops/Data-Mining-for-Business.jpg"},"mdxSource":{"compiledSource":"/*@jsxRuntime automatic @jsxImportSource react*/\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = Object.assign({\n    h3: \"h3\",\n    ul: \"ul\",\n    li: \"li\"\n  }, _provideComponents(), props.components);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.h3, {\n      children: \"Why should you attend?\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"You will learn about latest data science and machine learning approaches that specifically address business problems.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"After completing the course, you will be able to predict churn, segment customers, detect brand sentiment and recommend shopping carts.\"\n      }), \"\\n\", _jsx(_components.li, {\n        children: \"We will emphasize on intuition. 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