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chapters/grid-search.html

@@ -351,7 +351,7 @@ <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-line-number="true" data-line-number-punc=":" data-alg-title="Algorithm">
+<div class="pseudocode-container" data-pseudocode-index="1" data-alg-title="Algorithm" data-line-number="true" data-line-number-punc=":">
 <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}
 </div>
@@ -497,7 +497,7 @@ <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-line-number="true" data-line-number-punc=":" data-alg-title="Algorithm">
+<div id="alg-race" class="pseudocode-container" data-pseudocode-index="2" data-alg-title="Algorithm" data-line-number="true" data-line-number-punc=":">
 <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 +555,7 @@ <h2 data-number="11.4" class="anchored" data-anchor-id="sec-nested-resampling"><
 <div class="figure-content">
 <div class="cell" data-layout-align="center">
 <div class="cell-output-display">
-<div id="fig-1d-boost" class="quarto-float quarto-figure quarto-figure-center anchored" width="70%" data-fig-align="center">
+<div id="fig-1d-boost" class="quarto-float quarto-figure quarto-figure-center anchored" data-fig-align="center" width="70%">
 <figure class="quarto-float quarto-subfloat-fig figure">
 <div aria-describedby="fig-1d-boost-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <img src="../figures/fig-1d-boost-1.svg" id="fig-1d-boost" class="img-fluid quarto-figure quarto-figure-center anchored figure-img" style="width:70.0%" data-ref-parent="fig-1d-boost">

chapters/news.html

@@ -283,7 +283,7 @@ <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">2024-10-23</h3>
+<h3 class="anchored" data-anchor-id="section">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>

chapters/resampling.html

@@ -396,7 +396,7 @@ <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-comment-delimiter="//" data-indent-size="1.2em" data-alg-title="Algorithm" data-line-number-punc=":" data-line-number="true" data-pseudocode-index="1" data-no-end="false">
+<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 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>

search.json

@@ -104,7 +104,7 @@
     "href": "chapters/news.html#changelog",
     "title": "News",
     "section": "Changelog",
-    "text": "Changelog\n\n2024-10-23\nChapter 4 is now on missing data, bumping subsequent chapter numbers.\nNew chapters on resampling and grid search are now online.\n\n\n2024-06-17\nSeveral new chapters on embeddings, splines/interactions/discretization, and overfitting. Also, shinylive is used for interactive visualizations of concepts.\n\n\n2024-01-02\nFixed various typos.\n\n\n2023-12-21\nTypo fix on main page.\n\n\n2023-12-19\nSmall updates.\nUpdated snapshot.\nThe background color of premade outputs has been changed to the pages background color (#16).\n\n\n2023-12-11\nNo new content.\nUpdated snapshot.\nIncludes new Google Scholar links for articles (#8)\n\n\n2023-11-17\nUpdated renv snapshot.\n\n\n2023-10-09\nFirst committed versions.",
+    "text": "Changelog\n\n2024-09-23\nChapter 4 is now on missing data, bumping subsequent chapter numbers.\nNew chapters on resampling and grid search are now online.\n\n\n2024-06-17\nSeveral new chapters on embeddings, splines/interactions/discretization, and overfitting. Also, shinylive is used for interactive visualizations of concepts.\n\n\n2024-01-02\nFixed various typos.\n\n\n2023-12-21\nTypo fix on main page.\n\n\n2023-12-19\nSmall updates.\nUpdated snapshot.\nThe background color of premade outputs has been changed to the pages background color (#16).\n\n\n2023-12-11\nNo new content.\nUpdated snapshot.\nIncludes new Google Scholar links for articles (#8)\n\n\n2023-11-17\nUpdated renv snapshot.\n\n\n2023-10-09\nFirst committed versions.",
     "crumbs": [
       "News"
     ]

sitemap.xml

@@ -6,7 +6,7 @@
   </url>
   <url>
     <loc>https://aml4td.org/chapters/news.html</loc>
-    <lastmod>2024-09-23T13:00:20.387Z</lastmod>
+    <lastmod>2024-09-23T17:20:48.809Z</lastmod>
   </url>
   <url>
     <loc>https://aml4td.org/chapters/contributing.html</loc>