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updating lab version

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Jonathan Taylor
2026-02-02 17:04:01 -08:00
parent 39a00cc02d
commit aae67bdb06
24 changed files with 3299 additions and 17785 deletions
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Ch02-statlearn-lab.ipynb
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Ch03-linreg-lab.Rmd
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@@ -13,11 +13,11 @@ jupyter:
# Linear Regression # Linear Regression
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch03-linreg-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch03-linreg-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch03-linreg-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch03-linreg-lab.ipynb)

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Ch04-classification-lab.Rmd
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@@ -13,11 +13,11 @@ jupyter:
# Logistic Regression, LDA, QDA, and KNN # Logistic Regression, LDA, QDA, and KNN
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch04-classification-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch04-classification-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch04-classification-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch04-classification-lab.ipynb)

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Ch05-resample-lab.Rmd
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@@ -13,11 +13,11 @@ jupyter:
# Cross-Validation and the Bootstrap # Cross-Validation and the Bootstrap
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch05-resample-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch05-resample-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch05-resample-lab.ipynb)
In this lab, we explore the resampling techniques covered in this In this lab, we explore the resampling techniques covered in this

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Ch05-resample-lab.ipynb
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@@ -7,11 +7,11 @@
"source": [ "source": [
"# Cross-Validation and the Bootstrap\n", "# Cross-Validation and the Bootstrap\n",
"\n", "\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb\">\n", "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch05-resample-lab.ipynb\">\n",
"<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n", "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
"</a>\n", "</a>\n",
"\n", "\n",
"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch05-resample-lab.ipynb)" "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch05-resample-lab.ipynb)"
] ]
}, },
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@@ -136,10 +136,10 @@
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@@ -166,10 +166,10 @@
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} }
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@@ -210,10 +210,10 @@
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@@ -253,10 +253,10 @@
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} }
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@@ -297,10 +297,10 @@
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} }
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@@ -380,10 +380,10 @@
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@@ -731,10 +731,10 @@
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@@ -958,10 +958,10 @@
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"shell.execute_reply": "2025-04-03T19:33:25.422385Z" "shell.execute_reply": "2026-02-02T23:39:40.789705Z"
}, },
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@@ -1077,10 +1077,10 @@
"id": "7d561f70", "id": "7d561f70",
"metadata": { "metadata": {
"execution": { "execution": {
"iopub.execute_input": "2025-04-03T19:33:25.423860Z", "iopub.execute_input": "2026-02-02T23:39:40.791196Z",
"iopub.status.busy": "2025-04-03T19:33:25.423786Z", "iopub.status.busy": "2026-02-02T23:39:40.791129Z",
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"shell.execute_reply": "2025-04-03T19:33:27.088869Z" "shell.execute_reply": "2026-02-02T23:39:42.690189Z"
}, },
"lines_to_next_cell": 2 "lines_to_next_cell": 2
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@@ -1127,10 +1127,10 @@
"id": "3888aa0a", "id": "3888aa0a",
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"execution": { "execution": {
"iopub.execute_input": "2025-04-03T19:33:27.090386Z", "iopub.execute_input": "2026-02-02T23:39:42.692058Z",
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"shell.execute_reply": "2025-04-03T19:33:27.106554Z" "shell.execute_reply": "2026-02-02T23:39:42.737346Z"
}, },
"lines_to_next_cell": 2 "lines_to_next_cell": 2
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@@ -1199,10 +1199,10 @@
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"shell.execute_reply": "2025-04-03T19:33:29.434045Z" "shell.execute_reply": "2026-02-02T23:39:45.404784Z"
} }
}, },
"outputs": [ "outputs": [
@@ -1242,10 +1242,10 @@
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Ch06-varselect-lab.Rmd
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@@ -1,23 +1,26 @@
--- ---
jupyter: jupyter:
jupytext: jupytext:
cell_metadata_filter: -all cell_metadata_filter: language,-all
formats: Rmd formats: Rmd
main_language: python
text_representation: text_representation:
extension: .Rmd extension: .Rmd
format_name: rmarkdown format_name: rmarkdown
format_version: '1.2' format_version: '1.2'
jupytext_version: 1.19.1 jupytext_version: 1.19.1
kernelspec:
display_name: Python 3 (ipykernel)
language: python
name: python3
--- ---
# Linear Models and Regularization Methods # Linear Models and Regularization Methods
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch06-varselect-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch06-varselect-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch06-varselect-lab.ipynb)
In this lab we implement many of the techniques discussed in this chapter. In this lab we implement many of the techniques discussed in this chapter.
@@ -39,7 +42,7 @@ from functools import partial
``` ```
We again collect the new imports We again collect the new imports
needed for this lab. Readers will also have to have installed `l0bnb` using `pip install l0bnb`. needed for this lab. Readers will have installed `l0bnb` when installing the requirements.
```{python} ```{python}
from sklearn.pipeline import Pipeline from sklearn.pipeline import Pipeline
@@ -53,6 +56,14 @@ from l0bnb import fit_path
``` ```
Using `skl.ElasticNet` to fit ridge regression
throws up many warnings. We will suppress them below by a call to `warnings.simplefilter()`.
```{python}
import warnings
warnings.simplefilter("ignore")
```
## Subset Selection Methods ## Subset Selection Methods
Here we implement methods that reduce the number of parameters in a Here we implement methods that reduce the number of parameters in a
model by restricting the model to a subset of the input variables. model by restricting the model to a subset of the input variables.
@@ -368,7 +379,7 @@ estimates on the original scale, we must *unstandardize*
the coefficient estimates. The parameter the coefficient estimates. The parameter
$\lambda$ in (\ref{Ch6:ridge}) and (\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to $\lambda$ in (\ref{Ch6:ridge}) and (\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to
be consistent with the rest of this chapter, we use `lambdas` be consistent with the rest of this chapter, we use `lambdas`
rather than `alphas` in what follows. {At the time of publication, ridge fits like the one in code chunk [22] issue unwarranted convergence warning messages; we expect these to disappear as this package matures.} rather than `alphas` in what follows. {At the time of publication, ridge fits like the one in code chunk [23] issue unwarranted convergence warning messages; we suppressed these when we filtered the warnings above.}
```{python} ```{python}
Xs = X - X.mean(0)[None,:] Xs = X - X.mean(0)[None,:]

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# Non-Linear Modeling # Non-Linear Modeling
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch07-nonlin-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch07-nonlin-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch07-nonlin-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch07-nonlin-lab.ipynb)
In this lab, we demonstrate some of the nonlinear models discussed in In this lab, we demonstrate some of the nonlinear models discussed in

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# Tree-Based Methods # Tree-Based Methods
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch08-baggboost-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch08-baggboost-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch08-baggboost-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch08-baggboost-lab.ipynb)
We import some of our usual libraries at this top We import some of our usual libraries at this top

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# Support Vector Machines # Support Vector Machines
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch09-svm-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch09-svm-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch09-svm-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch09-svm-lab.ipynb)
In this lab, we use the `sklearn.svm` library to demonstrate the support In this lab, we use the `sklearn.svm` library to demonstrate the support

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# Deep Learning # Deep Learning
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch10-deeplearning-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch10-deeplearning-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch10-deeplearning-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch10-deeplearning-lab.ipynb)
In this section we demonstrate how to fit the examples discussed In this section we demonstrate how to fit the examples discussed

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# Survival Analysis # Survival Analysis
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch11-surv-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch11-surv-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch11-surv-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch11-surv-lab.ipynb)
In this lab, we perform survival analyses on three separate data In this lab, we perform survival analyses on three separate data

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Ch12-unsup-lab.Rmd
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@@ -13,11 +13,11 @@ jupyter:
# Unsupervised Learning # Unsupervised Learning
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch12-unsup-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch12-unsup-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch12-unsup-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch12-unsup-lab.ipynb)
In this lab we demonstrate PCA and clustering on several datasets. In this lab we demonstrate PCA and clustering on several datasets.

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Ch13-multiple-lab.Rmd
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@@ -13,11 +13,11 @@ jupyter:
# Multiple Testing # Multiple Testing
<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch13-multiple-lab.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch13-multiple-lab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> </a>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch13-multiple-lab.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch13-multiple-lab.ipynb)

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requirements.txt
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@@ -1,4 +1,5 @@
numpy==2.4.2 numpy==2.3.5
numba==0.63.1
scipy==1.16.3 scipy==1.16.3
pandas==3.0.0 pandas==3.0.0
lxml==6.0.2 lxml==6.0.2