From 84ea42c1edecf3bec629f2ee5dfd6845eb601df9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=98topepo=E2=80=99?= <‘mxkuhn@gmail.com’> Date: Mon, 23 Sep 2024 09:01:03 -0400 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- chapters/embeddings.html | 2 +- chapters/grid-search.html | 4 ++-- chapters/news.html | 33 +++++++++++++++++++-------------- chapters/resampling.html | 2 +- search.json | 2 +- sitemap.xml | 2 +- 7 files changed, 26 insertions(+), 21 deletions(-) diff --git a/.nojekyll b/.nojekyll index c839299..d81fbb4 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -f03203f1 \ No newline at end of file +ce8411eb \ No newline at end of file diff --git a/chapters/embeddings.html b/chapters/embeddings.html index 0d167a0..cf910a9 100644 --- a/chapters/embeddings.html +++ b/chapters/embeddings.html @@ -807,7 +807,7 @@

New features are added to the model based on the distance between \(\boldsymbol{x}_0\) and \(\boldsymbol{\bar{x}}^*_{c}\). The amount of shrinkage is best optimized using the tuning methods described in later chapters. There are several variations of this specific procedure. Wang and Zhu (2007) describe several different approaches and Efron (2009) demonstrates the connection to Bayesian methods.

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Nearest centroids diff --git a/chapters/grid-search.html b/chapters/grid-search.html index 31cff8f..094b6b5 100644 --- a/chapters/grid-search.html +++ b/chapters/grid-search.html @@ -351,7 +351,7 @@

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\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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\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}
diff --git a/chapters/news.html b/chapters/news.html index 46199a7..2066d85 100644 --- a/chapters/news.html +++ b/chapters/news.html @@ -282,36 +282,41 @@

Errata

Changelog

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2024-06-17

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Several new chapters on embeddings, splines/interactions/discretization, and overfitting. Also, shinylive is used for interactive visualizations of concepts.

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2024-10-23

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Chapter 4 is now on missing data, bumping subsequent chapter numbers.

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New chapters on resampling and grid search are now online.

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2024-01-02

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Fixed various typos.

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2024-06-17

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Several new chapters on embeddings, splines/interactions/discretization, and overfitting. Also, shinylive is used for interactive visualizations of concepts.

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2023-12-21

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Typo fix on main page.

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2024-01-02

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Fixed various typos.

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2023-12-19

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2023-12-21

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Typo fix on main page.

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2023-12-19

Small updates.

Updated snapshot.

The background color of premade outputs has been changed to the pages background color (#16).

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2023-12-11

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2023-12-11

No new content.

Updated snapshot.

Includes new Google Scholar links for articles (#8)

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2023-11-17

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2023-11-17

Updated renv snapshot.

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2023-10-09

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2023-10-09

First committed versions.

diff --git a/chapters/resampling.html b/chapters/resampling.html index 24d125e..b88bbd9 100644 --- a/chapters/resampling.html +++ b/chapters/resampling.html @@ -396,7 +396,7 @@

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\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}
diff --git a/search.json b/search.json index c5394d3..a2afae4 100644 --- a/search.json +++ b/search.json @@ -104,7 +104,7 @@ "href": "chapters/news.html#changelog", "title": "News", "section": "Changelog", - "text": "Changelog\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\n2024-10-23\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.", "crumbs": [ "News" ] diff --git a/sitemap.xml b/sitemap.xml index c2f9c06..6821c1b 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -6,7 +6,7 @@ https://aml4td.org/chapters/news.html - 2024-08-16T22:47:09.571Z + 2024-09-23T13:00:20.387Z https://aml4td.org/chapters/contributing.html