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chapters/categorical-predictors.html

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chapters/contributing.html

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chapters/embeddings.html

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@@ -807,7 +816,7 @@ <h2 data-number="7.4" class="anchored" data-anchor-id="sec-centroids"><span clas
 <p>New features are added to the model based on the distance between <span class="math inline">\(\boldsymbol{x}_0\)</span> and <span class="math inline">\(\boldsymbol{\bar{x}}^*_{c}\)</span>. The amount of shrinkage is best optimized using the tuning methods described in later chapters. There are several variations of this specific procedure. <span class="citation" data-cites="wangImprovedCentroids">Wang and Zhu (<a href="#ref-wangImprovedCentroids" role="doc-biblioref">2007</a>)</span> describe several different approaches and <span class="citation" data-cites="efron2009empirical">Efron (<a href="#ref-efron2009empirical" role="doc-biblioref">2009</a>)</span> demonstrates the connection to Bayesian methods.</p>
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
-<div id="fig-nsc" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Nearest centroids" data-fig-align="center">
+<div id="fig-nsc" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-align="center" alt="Nearest centroids">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-nsc-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <img src="../figures/fig-nsc-1.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:80.0%" alt="Nearest centroids">

chapters/grid-search.html

@@ -87,6 +87,15 @@
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@@ -351,7 +360,7 @@ <h1 class="title d-none d-lg-block"><span id="sec-grid-search" class="quarto-sec
 <div class="column" style="width:10%;">
 
 </div><div class="column" style="width:80%;">
-<div class="pseudocode-container" data-pseudocode-index="1" data-alg-title="Algorithm" data-line-number="true" data-line-number-punc=":">
+<div class="pseudocode-container" data-line-number-punc=":" data-pseudocode-index="1" data-line-number="true" data-alg-title="Algorithm">
 <div class="pseudocode">
 \begin{algorithm} \begin{algorithmic} \State $\mathfrak{D}^{tr}$: training set of predictors $X$ and outcome $y$ \State $B$: number of resamples \State $M(\mathfrak{D}^{tr}, B)$: a mapping function to split $\mathfrak{D}^{tr}$ for each of $B$ iterations. \State $f()$: model pipeline \State $\Theta$: Parameter set ($s \times m$) with candidates $\theta_j$ \For{$j=1$ \To $s$} \State Generate $\hat{Q}_{j} =$ \Call{Resample}{$\mathfrak{D}^{tr}, f(\cdot;\theta_j), M(\mathfrak{D}^{tr}, B)$} corresponding to candidate $\theta_j$. \EndFor \State Determine $\hat{\theta}_{opt}$ that optimizes $\hat{Q}_{j}$. \end{algorithmic} \end{algorithm}
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@@ -497,7 +506,7 @@ <h3 data-number="11.3.3" class="anchored" data-anchor-id="sec-racing"><span clas
 <div class="column" style="width:10%;">
 
 </div><div class="column" style="width:80%;">
-<div id="alg-race" class="pseudocode-container" data-pseudocode-index="2" data-alg-title="Algorithm" data-line-number="true" data-line-number-punc=":">
+<div id="alg-race" class="pseudocode-container" data-line-number-punc=":" data-pseudocode-index="2" data-line-number="true" data-alg-title="Algorithm">
 <div class="pseudocode">
 \begin{algorithm} \begin{algorithmic} \State $\mathfrak{D}^{tr}$: training set of predictors $X$ and outcome $y$ \State $B$: number of resamples \State Initial number of resamples $1 \lt B_{min} \lt B$ executed prior to analysis \State $M(\mathfrak{D}^{tr}, B)$: a mapping function to split $\mathfrak{D}^{tr}$ for each of $B$ iterations. \State $f()$: model pipeline \State $\Theta$: Parameter set ($s \times m$) with candidates $\theta_j$ \For{$j=1$ \To $s$} \For{$b=1$ \To $B_{min}$} \State Generate $\hat{Q}_{jb} =$ \Call{Resample}{$\mathfrak{D}^{tr}, f(\cdot;\theta_j), M_b(\mathfrak{D}^{tr}, B)$} \EndFor \State Compute $\hat{Q}_{j} = 1/B_{min}\sum_b \hat{Q}_{jb}$. \EndFor \State Eliminate candidates to produce $\Theta^b$ ($s_b \times m$) \For{$b = B_{min} + 1$ \To $B$} \For{$j=1$ \To $s$} \State Generate $\hat{Q}_{jb} =$ \Call{Resample}{$\mathfrak{D}^{tr}, f(\cdot;\theta_j), M_b(\mathfrak{D}^{tr}, B)$} \State Update candidate subset $\Theta^b$ by applying the filtering analysis \Endfor \Endfor \State Determine $\hat{\theta}_{opt}$ that optimizes $\hat{Q}_j^k$. \end{algorithmic} \end{algorithm}
 </div>
@@ -555,7 +564,7 @@ <h2 data-number="11.4" class="anchored" data-anchor-id="sec-nested-resampling"><
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-<div id="fig-1d-boost" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-align="center" width="70%">
+<div id="fig-1d-boost" class="quarto-float quarto-figure quarto-figure-center anchored" width="70%" data-fig-align="center">
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 <div aria-describedby="fig-1d-boost-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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chapters/initial-data-splitting.html

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chapters/interactions-nonlinear.html

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@@ -369,7 +378,7 @@ <h1 class="title d-none d-lg-block"><span id="sec-interactions-nonlinear" class=
 <p>In the predictors’ original form, there is a significant overlap between the two classes of samples. However, when the ratio of the predictors is used, the newly derived predictor better discriminates between classes (shown in <a href="#fig-two-class-corr" class="quarto-xref">Figure&nbsp;<span>8.1</span></a>(b)). While not a general rule, the three data characteristics above suggest that the modeler attempts to form ratios from two or more predictors <a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>.</p>
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-<div id="fig-two-class-corr" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-align="center" alt="Panel (a): two highly correlated, right-skewed predictors with two classes. Panel (b): separation of classes using $log(A/B)$.">
+<div id="fig-two-class-corr" class="quarto-float quarto-figure quarto-figure-center anchored" alt="Panel (a): two highly correlated, right-skewed predictors with two classes. Panel (b): separation of classes using $log(A/B)$." data-fig-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-two-class-corr-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <img src="../figures/fig-two-class-corr-1.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:80.0%" alt="Panel (a): two highly correlated, right-skewed predictors with two classes. Panel (b): separation of classes using $log(A/B)$.">

chapters/introduction.html

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chapters/missing-data.html

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chapters/news.html

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@@ -283,40 +292,44 @@ <h2 class="unlisted unnumbered anchored" data-anchor-id="errata">Errata</h2>
 <section id="changelog" class="level2 unlisted unnumbered">
 <h2 class="unlisted unnumbered anchored" data-anchor-id="changelog">Changelog</h2>
 <section id="section" class="level3">
-<h3 class="anchored" data-anchor-id="section">2024-09-23</h3>
+<h3 class="anchored" data-anchor-id="section">2024-10-01</h3>
+<p>Enabled google analytics.</p>
+</section>
+<section id="section-1" class="level3">
+<h3 class="anchored" data-anchor-id="section-1">2024-09-23</h3>
 <p>Chapter 4 is now on missing data, bumping subsequent chapter numbers.</p>
 <p>New chapters on resampling and grid search are now online.</p>
 </section>
-<section id="section-1" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-1">2024-06-17</h3>
+<section id="section-2" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-2">2024-06-17</h3>
 <p>Several new chapters on embeddings, splines/interactions/discretization, and overfitting. Also, shinylive is used for interactive visualizations of concepts.</p>
 </section>
-<section id="section-2" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-2">2024-01-02</h3>
+<section id="section-3" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-3">2024-01-02</h3>
 <p>Fixed various typos.</p>
 </section>
-<section id="section-3" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-3">2023-12-21</h3>
+<section id="section-4" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-4">2023-12-21</h3>
 <p>Typo fix on main page.</p>
 </section>
-<section id="section-4" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-4">2023-12-19</h3>
+<section id="section-5" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-5">2023-12-19</h3>
 <p>Small updates.</p>
 <p>Updated snapshot.</p>
 <p>The background color of premade outputs has been changed to the pages background color (<a href="https://github.com/aml4td/website/pull/16">#16</a>).</p>
 </section>
-<section id="section-5" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-5">2023-12-11</h3>
+<section id="section-6" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-6">2023-12-11</h3>
 <p>No new content.</p>
 <p>Updated snapshot.</p>
 <p>Includes new Google Scholar links for articles (<a href="https://github.com/aml4td/website/pull/8">#8</a>)</p>
 </section>
-<section id="section-6" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-6">2023-11-17</h3>
+<section id="section-7" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-7">2023-11-17</h3>
 <p>Updated renv snapshot.</p>
 </section>
-<section id="section-7" class="level3 unlisted unnumbered">
-<h3 class="unlisted unnumbered anchored" data-anchor-id="section-7">2023-10-09</h3>
+<section id="section-8" class="level3 unlisted unnumbered">
+<h3 class="unlisted unnumbered anchored" data-anchor-id="section-8">2023-10-09</h3>
 <p>First committed versions.</p>
 
 

chapters/numeric-predictors.html

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chapters/overfitting.html

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chapters/resampling.html

@@ -88,6 +88,15 @@
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@@ -396,7 +405,7 @@ <h1 class="title d-none d-lg-block"><span id="sec-resampling" class="quarto-sect
 <div class="column" style="width:5%;">
 
 </div><div class="column" style="width:65%;">
-<div id="alg-resampling" class="pseudocode-container" data-line-number="true" data-no-end="false" data-indent-size="1.2em" data-alg-title="Algorithm" data-pseudocode-index="1" data-line-number-punc=":" data-comment-delimiter="//">
+<div id="alg-resampling" class="pseudocode-container" data-line-number-punc=":" data-comment-delimiter="//" data-pseudocode-index="1" data-indent-size="1.2em" data-alg-title="Algorithm" data-line-number="true" data-no-end="false">
 <div class="pseudocode">
 \begin{algorithm} \begin{algorithmic} \State $\mathfrak{D}^{tr}$: training set of predictors $X$ and outcome $y$ \State $B$: number of resamples \State $M(\mathfrak{D}^{tr}, B)$: a mapping function to split $\mathfrak{D}^{tr}$ for each of $B$ iterations. \State $f()$: model pipeline \Procedure{Resample}{$\mathfrak{D}^{tr}, f, M(\mathfrak{D}^{tr}, B)$} \For{$b =1$ \To $B$} \State Partition $\mathfrak{D}^{tr}$ into $\{\mathfrak{D}_b^{fit}, \mathfrak{D}_b^{pred}\}$ using $M_b(\mathfrak{D}^{tr}, B)$. \State Train model pipeline $f$ on the analysis set to produce $\hat{f}_{b}(\mathfrak{D}_b^{fit})$. \State Generate assessment set predictions $\hat{y}_b$ by applying model $\hat{f}_{b}$ to $\mathfrak{D}_b^{pred}$. \State Estimate performance statistic $\hat{Q}_{b}$. \EndFor \State Compute reampling estimate $\hat{Q} = \sum_{b=1}^B \hat{Q}_{b}$. \Return $\hat{Q}$. \Endprocedure \end{algorithmic} \end{algorithm}
 </div>

chapters/whole-game.html

@@ -88,6 +88,15 @@
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index.html

@@ -8,7 +8,7 @@
 
 <meta name="author" content="Max Kuhn">
 <meta name="author" content="Kjell Johnson">
-<meta name="dcterms.date" content="2024-09-23">
+<meta name="dcterms.date" content="2024-10-01">
 
 <title>Applied Machine Learning for Tabular Data</title>
 <style>
@@ -90,6 +90,15 @@
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@@ -315,7 +324,7 @@ <h1 class="title d-none d-lg-block">Applied Machine Learning for Tabular Data</h
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">2024-09-23</p>
+      <p class="date">2024-10-01</p>
     </div>
   </div>