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Expand Up @@ -193,7 +193,7 @@ By default, the code will run a simple cross-validation only on the first iterat
This behavior can changed with the cross-validation test being performed more frequently by overriding the :code:`should_run_crossvalidation` function.

Another way to detect overfitting is to use :py:func:`pypal.plotting.make_jointplot`function from the plotting subpackage. This function will plot all objectives against each other (with errorbars and different classes indicated with colors) and histograms of the objectives on the diagonal. If the majority of predicted points tend to overlap one another and get discarded by PyPAL, this may suggest that the surrogate model is overfitted.
Another way to detect overfitting is to use :py:func:`pypal.plotting.make_jointplot` function from the plotting subpackage. This function will plot all objectives against each other (with errorbars and different classes indicated with colors) and histograms of the objectives on the diagonal. If the majority of predicted points tend to overlap one another and get discarded by PyPAL, this may suggest that the surrogate model is overfitted.

.. code-block:: python
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============

To explore different use cases of PyPAL, we recommend checking out the `example notebooks <https://github.com/kjappelbaum/pypal/tree/master/examples>`_.
All notebooks can be run without installation on MyBinder.
All notebooks can be run without installation on MyBinder. In the folder, you find the notebooks with the output cells, if you rerun them, the execution
should not take more than a few minutes.


1. One active learning step using GPR models built with :code:`GPy`
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:width: 400px
:align: center

Topic covered
................

- building a pal_coregionalized GPR model using :py:meth:`pypal.pal.models.gpr.build_coregionalized_model`
- using coregionalized models with :py:obj:`pypal.pal.pal_coregionalized.PALCoregionalized`
- attributes of the :code:`PAL` instance
- :py:meth:`pypal.pal.utils.exhaust_loop`


2. Active learning with "measure" function and :code:`sklearn` models
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:width: 400px
:align: center

Topic covered
................

- using :code:`sklearn` models with :py:obj:`pypal.pal.pal_sklearn.PALSklearn`
- selecting an initial set with :py:meth:`pypal.pal.utils.get_maxmin_samples`
- plotting with :py:meth:`pypal.plotting.make_jointplot`



3. Quantile regression
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.. image:: _static/quantile_regression_screenshot.png
:width: 400px
:align: center


- Using LightGBM models with quantile loss with :py:obj:`pypal.pal.pal_gbdt.PALGBDT`
- selecting an initial set with :py:meth:`pypal.pal.utils.get_kmeans_samples`
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</pre></div>
</div>
<p>This behavior can changed with the cross-validation test being performed more frequently by overriding the <code class="code docutils literal notranslate"><span class="pre">should_run_crossvalidation</span></code> function.</p>
<p>Another way to detect overfitting is to use :py:func:<a href="#id1"><span class="problematic" id="id2">`</span></a>pypal.plotting.make_jointplot`function from the plotting subpackage. This function will plot all objectives against each other (with errorbars and different classes indicated with colors) and histograms of the objectives on the diagonal. If the majority of predicted points tend to overlap one another and get discarded by PyPAL, this may suggest that the surrogate model is overfitted.</p>
<p>Another way to detect overfitting is to use <a class="reference internal" href="api.html#pypal.plotting.make_jointplot" title="pypal.plotting.make_jointplot"><code class="xref py py-func docutils literal notranslate"><span class="pre">pypal.plotting.make_jointplot()</span></code></a> function from the plotting subpackage. This function will plot all objectives against each other (with errorbars and different classes indicated with colors) and histograms of the objectives on the diagonal. If the majority of predicted points tend to overlap one another and get discarded by PyPAL, this may suggest that the surrogate model is overfitted.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pypal.plotting</span> <span class="kn">import</span> <span class="n">make_jointplot</span>

<span class="c1"># palinstance is a instance of a PAL class after</span>
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</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="tutorials.html#one-active-learning-step-using-gpr-models-built-with-gpy">1. One active learning step using GPR models built with <code class="code docutils literal notranslate"><span class="pre">GPy</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="tutorials.html#active-learning-with-measure-function-and-sklearn-models">2. Active learning with “measure” function and <code class="code docutils literal notranslate"><span class="pre">sklearn</span></code> models</a></li>
<li class="toctree-l2"><a class="reference internal" href="tutorials.html#one-active-learning-step-using-gpr-models-built-with-gpy">1. One active learning step using GPR models built with <code class="code docutils literal notranslate"><span class="pre">GPy</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="tutorials.html#topic-covered">Topic covered</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="tutorials.html#active-learning-with-measure-function-and-sklearn-models">2. Active learning with “measure” function and <code class="code docutils literal notranslate"><span class="pre">sklearn</span></code> models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="tutorials.html#id1">Topic covered</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="tutorials.html#quantile-regression">3. Quantile regression</a></li>
</ul>
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