diff --git a/doc/pub/week38/html/._week38-bs004.html b/doc/pub/week38/html/._week38-bs004.html
index 1f9e9d2d9..401c59cd6 100644
--- a/doc/pub/week38/html/._week38-bs004.html
+++ b/doc/pub/week38/html/._week38-bs004.html
@@ -239,7 +239,7 @@ MathJax.Hub.Config({
- Define a range of interest for the penalty parameter.
- 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as
$$
\begin{align*}
diff --git a/doc/pub/week38/html/._week38-bs005.html b/doc/pub/week38/html/._week38-bs005.html
index 3b733e624..0ef58d866 100644
--- a/doc/pub/week38/html/._week38-bs005.html
+++ b/doc/pub/week38/html/._week38-bs005.html
@@ -243,10 +243,10 @@ MathJax.Hub.Config({
Split the dataset into \( k \) groups.
For each unique group:
- Decide which group to use as set for test data
- Take the remaining groups as a training data set
- Fit a model on the training set and evaluate it on the test set
- Retain the evaluation score and discard the model
+ Decide which group to use as set for test data
+ Take the remaining groups as a training data set
+ Fit a model on the training set and evaluate it on the test set
+ Retain the evaluation score and discard the model
Summarize the model using the sample of model evaluation scores
diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html
index b0a2f8a19..f87016a53 100644
--- a/doc/pub/week38/html/week38-reveal.html
+++ b/doc/pub/week38/html/week38-reveal.html
@@ -307,7 +307,7 @@ cross-validation (LOOCV).
- Define a range of interest for the penalty parameter.
- 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as
@@ -338,10 +338,10 @@ $$
Split the dataset into \( k \) groups.
For each unique group:
- Decide which group to use as set for test data
- Take the remaining groups as a training data set
- Fit a model on the training set and evaluate it on the test set
- Retain the evaluation score and discard the model
+ Decide which group to use as set for test data
+ Take the remaining groups as a training data set
+ Fit a model on the training set and evaluate it on the test set
+ Retain the evaluation score and discard the model
Summarize the model using the sample of model evaluation scores
diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html
index 0ca3b8485..ab4c363e1 100644
--- a/doc/pub/week38/html/week38-solarized.html
+++ b/doc/pub/week38/html/week38-solarized.html
@@ -284,7 +284,7 @@ cross-validation (LOOCV).
- Define a range of interest for the penalty parameter.
- 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as
$$
\begin{align*}
@@ -310,10 +310,10 @@ $$
Split the dataset into \( k \) groups.
For each unique group:
- Decide which group to use as set for test data
- Take the remaining groups as a training data set
- Fit a model on the training set and evaluate it on the test set
- Retain the evaluation score and discard the model
+ Decide which group to use as set for test data
+ Take the remaining groups as a training data set
+ Fit a model on the training set and evaluate it on the test set
+ Retain the evaluation score and discard the model
Summarize the model using the sample of model evaluation scores
diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html
index f1cab1cab..f869d1246 100644
--- a/doc/pub/week38/html/week38.html
+++ b/doc/pub/week38/html/week38.html
@@ -361,7 +361,7 @@ cross-validation (LOOCV).
- Define a range of interest for the penalty parameter.
- 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), 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 \( \boldsymbol{\sigma}_{-i}^2(\lambda) \), as
$$
\begin{align*}
@@ -387,10 +387,10 @@ $$
Split the dataset into \( k \) groups.
For each unique group:
- Decide which group to use as set for test data
- Take the remaining groups as a training data set
- Fit a model on the training set and evaluate it on the test set
- Retain the evaluation score and discard the model
+ Decide which group to use as set for test data
+ Take the remaining groups as a training data set
+ Fit a model on the training set and evaluate it on the test set
+ Retain the evaluation score and discard the model
Summarize the model using the sample of model evaluation scores
diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz
index 74c51b95e..50a4dd821 100644
Binary files a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz and b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz differ
diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb
index 1856abdbf..75f2ac3ec 100644
--- a/doc/pub/week38/ipynb/week38.ipynb
+++ b/doc/pub/week38/ipynb/week38.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "ec385caf",
+ "id": "5def2812",
"metadata": {
"editable": true
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@@ -14,7 +14,7 @@
},
{
"cell_type": "markdown",
- "id": "cadc57c2",
+ "id": "565e2f9b",
"metadata": {
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@@ -27,7 +27,7 @@
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{
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- "id": "38cafe97",
+ "id": "91fca3ec",
"metadata": {
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@@ -53,7 +53,7 @@
},
{
"cell_type": "markdown",
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"metadata": {
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@@ -66,7 +66,7 @@
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{
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+ "id": "9728f4cd",
"metadata": {
"editable": true
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@@ -78,7 +78,7 @@
},
{
"cell_type": "markdown",
- "id": "aec8b6e0",
+ "id": "e4716124",
"metadata": {
"editable": true
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@@ -88,7 +88,7 @@
},
{
"cell_type": "markdown",
- "id": "b9b1b764",
+ "id": "9e02d12b",
"metadata": {
"editable": true
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@@ -101,7 +101,7 @@
},
{
"cell_type": "markdown",
- "id": "e1dd5847",
+ "id": "e67c60c1",
"metadata": {
"editable": true
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@@ -111,7 +111,7 @@
},
{
"cell_type": "markdown",
- "id": "0774db82",
+ "id": "c1e3c785",
"metadata": {
"editable": true
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@@ -123,7 +123,7 @@
},
{
"cell_type": "markdown",
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+ "id": "64eef79f",
"metadata": {
"editable": true
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@@ -136,7 +136,7 @@
},
{
"cell_type": "markdown",
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+ "id": "87155dca",
"metadata": {
"editable": true
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@@ -149,7 +149,7 @@
},
{
"cell_type": "markdown",
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+ "id": "be318805",
"metadata": {
"editable": true
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@@ -161,7 +161,7 @@
},
{
"cell_type": "markdown",
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+ "id": "b02aa814",
"metadata": {
"editable": true
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@@ -173,7 +173,7 @@
},
{
"cell_type": "markdown",
- "id": "aa5f2fd9",
+ "id": "1dfbc8c3",
"metadata": {
"editable": true
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@@ -183,7 +183,7 @@
},
{
"cell_type": "markdown",
- "id": "93f80bea",
+ "id": "0709e8d4",
"metadata": {
"editable": true
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@@ -196,7 +196,7 @@
},
{
"cell_type": "markdown",
- "id": "2f2c70e9",
+ "id": "e6c5367c",
"metadata": {
"editable": true
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@@ -208,7 +208,7 @@
},
{
"cell_type": "markdown",
- "id": "3894a153",
+ "id": "63427486",
"metadata": {
"editable": true
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@@ -220,7 +220,7 @@
},
{
"cell_type": "markdown",
- "id": "0bbcba84",
+ "id": "83a0ff77",
"metadata": {
"editable": true
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@@ -245,7 +245,7 @@
},
{
"cell_type": "markdown",
- "id": "340fdbf9",
+ "id": "5cfabb4d",
"metadata": {
"editable": true
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@@ -256,12 +256,12 @@
"\n",
"* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n",
"\n",
- "* 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"
+ "* 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"
]
},
{
"cell_type": "markdown",
- "id": "bbafcf09",
+ "id": "c3932510",
"metadata": {
"editable": true
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@@ -277,7 +277,7 @@
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{
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- "id": "66371364",
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"metadata": {
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@@ -291,7 +291,7 @@
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{
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"metadata": {
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@@ -319,7 +319,7 @@
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{
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"metadata": {
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@@ -332,7 +332,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "ca6fb185",
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"metadata": {
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@@ -434,7 +434,7 @@
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"metadata": {
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@@ -456,7 +456,7 @@
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{
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@@ -482,7 +482,7 @@
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{
"cell_type": "markdown",
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"metadata": {
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@@ -506,7 +506,7 @@
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{
"cell_type": "markdown",
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+ "id": "80c8560d",
"metadata": {
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@@ -531,7 +531,7 @@
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{
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"metadata": {
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@@ -543,7 +543,7 @@
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{
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@@ -561,7 +561,7 @@
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{
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@@ -579,7 +579,7 @@
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{
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"metadata": {
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@@ -590,7 +590,7 @@
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{
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"metadata": {
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@@ -615,7 +615,7 @@
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{
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"metadata": {
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@@ -628,7 +628,7 @@
{
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"execution_count": 2,
- "id": "50255b4f",
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@@ -693,7 +693,7 @@
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{
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"metadata": {
"editable": true
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@@ -706,7 +706,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "bfd56a95",
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"metadata": {
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@@ -725,7 +725,7 @@
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{
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"metadata": {
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@@ -736,7 +736,7 @@
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{
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"metadata": {
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@@ -748,7 +748,7 @@
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{
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@@ -767,7 +767,7 @@
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@@ -789,7 +789,7 @@
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@@ -801,7 +801,7 @@
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"metadata": {
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@@ -811,7 +811,7 @@
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@@ -824,7 +824,7 @@
{
"cell_type": "code",
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- "id": "cccad33d",
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@@ -889,7 +889,7 @@
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@@ -901,7 +901,7 @@
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@@ -916,7 +916,7 @@
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@@ -928,7 +928,7 @@
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@@ -940,7 +940,7 @@
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@@ -957,7 +957,7 @@
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@@ -971,7 +971,7 @@
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@@ -981,7 +981,7 @@
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{
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@@ -993,7 +993,7 @@
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{
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@@ -1005,7 +1005,7 @@
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@@ -1017,7 +1017,7 @@
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@@ -1040,7 +1040,7 @@
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@@ -1067,7 +1067,7 @@
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@@ -1079,7 +1079,7 @@
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@@ -1101,7 +1101,7 @@
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@@ -1175,7 +1175,7 @@
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{
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- "id": "3c6ba3c6",
+ "id": "caf0b869",
"metadata": {
"editable": true
},
@@ -1362,7 +1362,7 @@
},
{
"cell_type": "markdown",
- "id": "cc5a2850",
+ "id": "e81efcc0",
"metadata": {
"editable": true
},
@@ -1377,7 +1377,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "3f85651e",
+ "id": "6719759d",
"metadata": {
"collapsed": false,
"editable": true
@@ -1413,7 +1413,7 @@
},
{
"cell_type": "markdown",
- "id": "1d0b8a89",
+ "id": "dc130ffa",
"metadata": {
"editable": true
},
@@ -1427,7 +1427,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "55b3b079",
+ "id": "5e4453ed",
"metadata": {
"collapsed": false,
"editable": true
@@ -1472,7 +1472,7 @@
},
{
"cell_type": "markdown",
- "id": "6c99f717",
+ "id": "22ed51f5",
"metadata": {
"editable": true
},
@@ -1497,7 +1497,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "501b9db1",
+ "id": "bb5f770c",
"metadata": {
"collapsed": false,
"editable": true
@@ -1509,7 +1509,7 @@
},
{
"cell_type": "markdown",
- "id": "1b70e9de",
+ "id": "c997da6e",
"metadata": {
"editable": true
},
@@ -1520,7 +1520,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "6901ef3b",
+ "id": "3ff8b827",
"metadata": {
"collapsed": false,
"editable": true
@@ -1532,7 +1532,7 @@
},
{
"cell_type": "markdown",
- "id": "98ef68cf",
+ "id": "cc3ee93c",
"metadata": {
"editable": true
},
@@ -1545,7 +1545,7 @@
},
{
"cell_type": "markdown",
- "id": "97bc4a72",
+ "id": "f8b434c3",
"metadata": {
"editable": true
},
@@ -1556,7 +1556,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "2f04b018",
+ "id": "2f0c2ea3",
"metadata": {
"collapsed": false,
"editable": true
@@ -1611,7 +1611,7 @@
},
{
"cell_type": "markdown",
- "id": "9795e60a",
+ "id": "f89296cd",
"metadata": {
"editable": true
},
@@ -1621,7 +1621,7 @@
},
{
"cell_type": "markdown",
- "id": "6a297979",
+ "id": "bad04be1",
"metadata": {
"editable": true
},
@@ -1642,7 +1642,7 @@
},
{
"cell_type": "markdown",
- "id": "e770a474",
+ "id": "c6948a94",
"metadata": {
"editable": true
},
@@ -1659,7 +1659,7 @@
},
{
"cell_type": "markdown",
- "id": "02166d90",
+ "id": "d48f3124",
"metadata": {
"editable": true
},
@@ -1674,7 +1674,7 @@
},
{
"cell_type": "markdown",
- "id": "0cb68e69",
+ "id": "65ab6c61",
"metadata": {
"editable": true
},
@@ -1684,7 +1684,7 @@
},
{
"cell_type": "markdown",
- "id": "f27a5419",
+ "id": "dcf5a0be",
"metadata": {
"editable": true
},
@@ -1700,7 +1700,7 @@
},
{
"cell_type": "markdown",
- "id": "aa19db65",
+ "id": "982ec876",
"metadata": {
"editable": true
},
@@ -1712,7 +1712,7 @@
},
{
"cell_type": "markdown",
- "id": "b165a6be",
+ "id": "5166d10b",
"metadata": {
"editable": true
},
@@ -1723,7 +1723,7 @@
},
{
"cell_type": "markdown",
- "id": "7de6d2de",
+ "id": "354aa98f",
"metadata": {
"editable": true
},
@@ -1735,7 +1735,7 @@
},
{
"cell_type": "markdown",
- "id": "483c8008",
+ "id": "88f270ab",
"metadata": {
"editable": true
},
@@ -1745,7 +1745,7 @@
},
{
"cell_type": "markdown",
- "id": "24140dc0",
+ "id": "75d1a620",
"metadata": {
"editable": true
},
@@ -1759,7 +1759,7 @@
},
{
"cell_type": "markdown",
- "id": "a7faf375",
+ "id": "d8b70707",
"metadata": {
"editable": true
},
@@ -1771,7 +1771,7 @@
},
{
"cell_type": "markdown",
- "id": "7aeae637",
+ "id": "f19e919e",
"metadata": {
"editable": true
},
@@ -1781,7 +1781,7 @@
},
{
"cell_type": "markdown",
- "id": "95478a44",
+ "id": "ff1981ee",
"metadata": {
"editable": true
},
@@ -1793,7 +1793,7 @@
},
{
"cell_type": "markdown",
- "id": "b603fb28",
+ "id": "bb9b3bb6",
"metadata": {
"editable": true
},
@@ -1805,7 +1805,7 @@
},
{
"cell_type": "markdown",
- "id": "e8306b9f",
+ "id": "eeeb36fc",
"metadata": {
"editable": true
},
@@ -1825,7 +1825,7 @@
},
{
"cell_type": "markdown",
- "id": "b15c1cc9",
+ "id": "00690bd8",
"metadata": {
"editable": true
},
@@ -1841,7 +1841,7 @@
},
{
"cell_type": "markdown",
- "id": "948c4651",
+ "id": "009d5f64",
"metadata": {
"editable": true
},
@@ -1857,7 +1857,7 @@
},
{
"cell_type": "markdown",
- "id": "5c56b6f6",
+ "id": "e657de4b",
"metadata": {
"editable": true
},
@@ -1868,7 +1868,7 @@
},
{
"cell_type": "markdown",
- "id": "b6f24a4b",
+ "id": "49ea333c",
"metadata": {
"editable": true
},
@@ -1880,7 +1880,7 @@
},
{
"cell_type": "markdown",
- "id": "68c2e01a",
+ "id": "179ba575",
"metadata": {
"editable": true
},
@@ -1890,7 +1890,7 @@
},
{
"cell_type": "markdown",
- "id": "5377932b",
+ "id": "d90b5b98",
"metadata": {
"editable": true
},
@@ -1902,7 +1902,7 @@
},
{
"cell_type": "markdown",
- "id": "39aeb53b",
+ "id": "d52f76c7",
"metadata": {
"editable": true
},
@@ -1912,7 +1912,7 @@
},
{
"cell_type": "markdown",
- "id": "d605bc59",
+ "id": "8834b7fc",
"metadata": {
"editable": true
},
@@ -1924,7 +1924,7 @@
},
{
"cell_type": "markdown",
- "id": "c09e39c1",
+ "id": "5765da6d",
"metadata": {
"editable": true
},
@@ -1946,7 +1946,7 @@
},
{
"cell_type": "markdown",
- "id": "aa6ed8e3",
+ "id": "4671ea0e",
"metadata": {
"editable": true
},
@@ -1959,7 +1959,7 @@
},
{
"cell_type": "markdown",
- "id": "a6258e0d",
+ "id": "b1266fbd",
"metadata": {
"editable": true
},
@@ -1972,7 +1972,7 @@
},
{
"cell_type": "markdown",
- "id": "b956b74a",
+ "id": "6a1356d4",
"metadata": {
"editable": true
},
@@ -1982,7 +1982,7 @@
},
{
"cell_type": "markdown",
- "id": "1d097b98",
+ "id": "96ed0828",
"metadata": {
"editable": true
},
@@ -2000,7 +2000,7 @@
},
{
"cell_type": "markdown",
- "id": "488c5487",
+ "id": "d79d1cb9",
"metadata": {
"editable": true
},
@@ -2010,7 +2010,7 @@
},
{
"cell_type": "markdown",
- "id": "ac43e6ad",
+ "id": "20525170",
"metadata": {
"editable": true
},
@@ -2025,7 +2025,7 @@
},
{
"cell_type": "markdown",
- "id": "3d143f29",
+ "id": "d56b0128",
"metadata": {
"editable": true
},
@@ -2035,7 +2035,7 @@
},
{
"cell_type": "markdown",
- "id": "1bb94551",
+ "id": "69b71d65",
"metadata": {
"editable": true
},
@@ -2049,7 +2049,7 @@
},
{
"cell_type": "markdown",
- "id": "c9593657",
+ "id": "5d3766b4",
"metadata": {
"editable": true
},
@@ -2059,7 +2059,7 @@
},
{
"cell_type": "markdown",
- "id": "49bc90a7",
+ "id": "642c34cb",
"metadata": {
"editable": true
},
@@ -2073,7 +2073,7 @@
},
{
"cell_type": "markdown",
- "id": "964663f4",
+ "id": "b7dacac3",
"metadata": {
"editable": true
},
@@ -2088,7 +2088,7 @@
},
{
"cell_type": "markdown",
- "id": "180b3c60",
+ "id": "503a6419",
"metadata": {
"editable": true
},
@@ -2105,7 +2105,7 @@
},
{
"cell_type": "markdown",
- "id": "580b26a1",
+ "id": "cacde55b",
"metadata": {
"editable": true
},
@@ -2117,7 +2117,7 @@
},
{
"cell_type": "markdown",
- "id": "8fec49f5",
+ "id": "217cb23e",
"metadata": {
"editable": true
},
@@ -2131,7 +2131,7 @@
},
{
"cell_type": "markdown",
- "id": "0cde0f71",
+ "id": "191d2ce2",
"metadata": {
"editable": true
},
@@ -2146,7 +2146,7 @@
},
{
"cell_type": "markdown",
- "id": "1bdd8fa3",
+ "id": "cc0fdf90",
"metadata": {
"editable": true
},
@@ -2158,7 +2158,7 @@
},
{
"cell_type": "markdown",
- "id": "a9f1046f",
+ "id": "4fe9484e",
"metadata": {
"editable": true
},
@@ -2169,7 +2169,7 @@
},
{
"cell_type": "markdown",
- "id": "51e73a2b",
+ "id": "5ed167ec",
"metadata": {
"editable": true
},
@@ -2197,7 +2197,7 @@
},
{
"cell_type": "markdown",
- "id": "671e0546",
+ "id": "9f0db830",
"metadata": {
"editable": true
},
diff --git a/doc/src/week38/week38.do.txt b/doc/src/week38/week38.do.txt
index d833f4448..692b68f7a 100644
--- a/doc/src/week38/week38.do.txt
+++ b/doc/src/week38/week38.do.txt
@@ -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