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Morten Hjorth-Jensen
2022-09-19 07:14:19 +02:00
parent 970ebe0921
commit b90ac9320c
8 changed files with 164 additions and 164 deletions
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doc/pub/week38/html/._week38-bs004.html
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@@ -239,7 +239,7 @@ MathJax.Hub.Config({
<ul>
<li> Define a range of interest for the penalty parameter.</li>
<li> Divide the data set into training and test set comprising samples \( \{1, \ldots, n\} \setminus i \) and \( \{ i \} \), respectively.</li>
<li> Fit the linear regression model by means of ridge estimation for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
<li> Fit the linear regression model by means of for example Ridge or Lasso regression for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
</ul>
$$
\begin{align*}
doc/pub/week38/html/._week38-bs005.html
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@@ -243,10 +243,10 @@ MathJax.Hub.Config({
<li> Split the dataset into \( k \) groups.</li>
<li> For each unique group:
<ol type="a"></li>
<li> Decide which group to use as set for test data</li>
<li> Take the remaining groups as a training data set</li>
<li> Fit a model on the training set and evaluate it on the test set</li>
<li> Retain the evaluation score and discard the model</li>
<li> Decide which group to use as set for test data</li>
<li> Take the remaining groups as a training data set</li>
<li> Fit a model on the training set and evaluate it on the test set</li>
<li> Retain the evaluation score and discard the model</li>
</ol>
<li> Summarize the model using the sample of model evaluation scores</li>
</ol>
doc/pub/week38/html/week38-reveal.html
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@@ -307,7 +307,7 @@ cross-validation (LOOCV).
<ul>
<p><li> Define a range of interest for the penalty parameter.</li>
<p><li> Divide the data set into training and test set comprising samples \( \{1, \ldots, n\} \setminus i \) and \( \{ i \} \), respectively.</li>
<p><li> Fit the linear regression model by means of ridge estimation for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
<p><li> Fit the linear regression model by means of for example Ridge or Lasso regression for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
</ul>
<p>
<p>&nbsp;<br>
@@ -338,10 +338,10 @@ $$
<p><li> Split the dataset into \( k \) groups.</li>
<p><li> For each unique group:
<ol type="a"></li>
<p><li> Decide which group to use as set for test data</li>
<p><li> Take the remaining groups as a training data set</li>
<p><li> Fit a model on the training set and evaluate it on the test set</li>
<p><li> Retain the evaluation score and discard the model</li>
<p><li> Decide which group to use as set for test data</li>
<p><li> Take the remaining groups as a training data set</li>
<p><li> Fit a model on the training set and evaluate it on the test set</li>
<p><li> Retain the evaluation score and discard the model</li>
</ol>
<p>
<p><li> Summarize the model using the sample of model evaluation scores</li>
doc/pub/week38/html/week38-solarized.html
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@@ -284,7 +284,7 @@ cross-validation (LOOCV).
<ul>
<li> Define a range of interest for the penalty parameter.</li>
<li> Divide the data set into training and test set comprising samples \( \{1, \ldots, n\} \setminus i \) and \( \{ i \} \), respectively.</li>
<li> Fit the linear regression model by means of ridge estimation for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
<li> Fit the linear regression model by means of for example Ridge or Lasso regression for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
</ul>
$$
\begin{align*}
@@ -310,10 +310,10 @@ $$
<li> Split the dataset into \( k \) groups.</li>
<li> For each unique group:
<ol type="a"></li>
<li> Decide which group to use as set for test data</li>
<li> Take the remaining groups as a training data set</li>
<li> Fit a model on the training set and evaluate it on the test set</li>
<li> Retain the evaluation score and discard the model</li>
<li> Decide which group to use as set for test data</li>
<li> Take the remaining groups as a training data set</li>
<li> Fit a model on the training set and evaluate it on the test set</li>
<li> Retain the evaluation score and discard the model</li>
</ol>
<li> Summarize the model using the sample of model evaluation scores</li>
</ol>
doc/pub/week38/html/week38.html
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@@ -361,7 +361,7 @@ cross-validation (LOOCV).
<ul>
<li> Define a range of interest for the penalty parameter.</li>
<li> Divide the data set into training and test set comprising samples \( \{1, \ldots, n\} \setminus i \) and \( \{ i \} \), respectively.</li>
<li> Fit the linear regression model by means of ridge estimation for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
<li> Fit the linear regression model by means of for example Ridge or Lasso regression for each \( \lambda \) in the grid using the training set, and the corresponding estimate of the error variance \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as</li>
</ul>
$$
\begin{align*}
@@ -387,10 +387,10 @@ $$
<li> Split the dataset into \( k \) groups.</li>
<li> For each unique group:
<ol type="a"></li>
<li> Decide which group to use as set for test data</li>
<li> Take the remaining groups as a training data set</li>
<li> Fit a model on the training set and evaluate it on the test set</li>
<li> Retain the evaluation score and discard the model</li>
<li> Decide which group to use as set for test data</li>
<li> Take the remaining groups as a training data set</li>
<li> Fit a model on the training set and evaluate it on the test set</li>
<li> Retain the evaluation score and discard the model</li>
</ol>
<li> Summarize the model using the sample of model evaluation scores</li>
</ol>
doc/pub/week38/ipynb/ipynb-week38-src.tar.gz
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doc/pub/week38/ipynb/week38.ipynb
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doc/src/week38/week38.do.txt
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@@ -110,7 +110,7 @@ cross-validation (LOOCV).
* Divide the data set into training and test set comprising samples $\{1, \ldots, n\} \setminus i$ and $\{ i \}$, respectively.
* Fit the linear regression model by means of ridge estimation for each $\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\bm{\sigma}_{-i}^2(\lambda)$, as
* Fit the linear regression model by means of for example Ridge or Lasso regression for each $\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\bm{\sigma}_{-i}^2(\lambda)$, as
!bt
\begin{align*}
\bm{\beta}_{-i}(\lambda) & = ( \bm{X}_{-i, \ast}^{T}
@@ -134,10 +134,10 @@ For the various values of $k$
o shuffle the dataset randomly.
o Split the dataset into $k$ groups.
o For each unique group:
o Decide which group to use as set for test data
o Take the remaining groups as a training data set
o Fit a model on the training set and evaluate it on the test set
o Retain the evaluation score and discard the model
o Decide which group to use as set for test data
o Take the remaining groups as a training data set
o Fit a model on the training set and evaluate it on the test set
o Retain the evaluation score and discard the model
o Summarize the model using the sample of model evaluation scores