ISLP_labs: Reproducible Jupyter Labs for Statistical Learning

What ISLP_labs covers and who should use it

ISLP_labs is the notebook companion repository for Introduction to Statistical Learning, or ISLP. The README describes it as an up-to-date version of the labs and states that the repository tracks changes to the book's source code. That positioning matters. This is not a generic statistics package with a command-line interface or a reusable estimator API. It is a collection of computational exercises designed to accompany the material in ISLP.

The repository layout makes the coverage visible. It contains one notebook for each major portion of the book, including `Ch02-statlearn-lab.ipynb`, `Ch03-linreg-lab.ipynb`, `Ch04-classification-lab.ipynb`, `Ch05-resample-lab.ipynb`, `Ch06-varselect-lab.ipynb`, `Ch07-nonlin-lab.ipynb`, `Ch08-baggboost-lab.ipynb`, `Ch09-svm-lab.ipynb`, `Ch10-deeplearning-lab.ipynb`, `Ch11-surv-lab.ipynb`, `Ch12-unsup-lab.ipynb`, and `Ch13-multiple-lab.ipynb`. The topics therefore span introductory statistical learning, linear regression, classification, resampling, variable selection, nonlinear models, bagging and boosting, support vector machines, deep learning, survival analysis, unsupervised learning, and multiple testing.

Readers who benefit most are those working through ISLP and wanting executable examples tied to the current lab sources. An instructor can select notebooks for a course. A self-directed reader can use them as a sequence of analyses rather than building an environment from scattered dependencies. A professional data-science team may find the notebooks useful for teaching or reproduction, but the repository does not present itself as a deployable application or a maintained library API.

How the notebook and dependency workflow is organized

The repository uses Jupyter Notebook as its primary language and stores data files such as `Auto.csv`, `Auto.data`, and `imagenet_class_index.json` alongside the notebooks and image assets. The README says the intent is that a virtual environment built from `requirements.txt` will reproduce the results in the repository. That is a concrete reproducibility goal, although the repository does not provide a separate command for checking every notebook against a reference output.

The dependency file pins exact versions. It includes `numpy==2.3.5`, `pandas==3.0.0`, `scipy==1.16.3`, `scikit-learn==1.8.0`, `statsmodels==0.14.6`, `lifelines==0.30.0`, `pygam==0.12.0`, `torch==2.10.0`, `torchvision==0.25.0`, `pytorch-lightning==2.6.1`, and `ISLP==0.4.1`. It also installs `l0bnb` from a Git repository and a specific branch reference. This pinning gives users a defined dependency set, but it also means upgrades are deliberate rather than automatic. The Makefile reads `.python-version` and uses it when creating a virtual environment.

For notebook execution, the documented command starts JupyterLab with the Chapter 2 notebook. JupyterLab is not included as a requirement of the labs, so a user must install it separately or install the ISLP environment first. The Makefile offers a `jupyter` target that installs the package and launches JupyterLab, while `Rmd` installs `jupytext` and converts the notebooks into R Markdown files. The README states that earlier R Markdown files were removed and can be rebuilt from the notebooks.

Installing uv and reproducing the v2.2.4 lab environment

The repository documents two installation paths. The shorter path uses the published requirements file directly. Install `uv` first, then install the pinned requirements. The command below is copied from the README and targets the `v2.2.4` requirements file.

uv pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2.2.4/requirements.txt;

After installation, the README gives this command for launching the Chapter 2 lab in JupyterLab:

uv run jupyter lab Ch02-statlearn-lab.ipynb

The command expects `jupyter lab` to be available in the environment. The README specifically says that JupyterLab installation is not included as a requirement of the labs and directs readers to follow the ISLP installation instructions when necessary. If the command cannot find `jupyter`, the missing piece is JupyterLab rather than a missing lab dependency.

The longer path creates a directory, downloads a selected release, and creates a virtual environment with a specified Python version. The documented setup command is:

uv run python setup_notebook_env.py --outdir ISLP --commit v2.2.4 --python-version 3.12

The script itself must first be downloaded from the repository's raw file URL:

curl -LO https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/main/setup_notebook_env.py

On macOS and Linux, the README gives `source ISLP/.venv/bin/activate` for activating the created environment. On Windows, it gives `ISLP\.venv\Scripts\activate`. Once activated, start JupyterLab with `jupyter lab`. The setup command can optionally receive notebook names after the arguments, which the README describes as a way to run selected notebooks automatically for testing. Those names are arguments to the script, not a separate documented package or configuration key.

A first real use with Chapter 2

A practical first step is to launch `Ch02-statlearn-lab.ipynb` and work through the cells in notebook order. The repository provides the notebook as a Jupyter file, so the actual execution interface is the browser-based JupyterLab environment rather than a project-specific CLI. The accompanying `Auto.csv` and `Auto.data` files are available in the repository, and the notebook can therefore be used without first identifying a separate application entry point.

The README's basic launch command is sufficient once the environment contains JupyterLab:

uv run jupyter lab Ch02-statlearn-lab.ipynb

Readers should expect an interactive notebook session in which code cells are executed sequentially. The README does not document a single “run all” flag, an expected numerical output format, or a validation command that compares outputs with the book's figures. Consequently, the first use is best treated as an exploratory and instructional workflow. The notebook itself is the source of the analysis sequence, while the repository's release tag and pinned requirements identify the surrounding software configuration.

Other chapters can be opened by replacing the notebook filename passed to `jupyter lab`. The repository's Makefile also provides `make jupyter`, which installs dependencies, installs JupyterLab, and starts JupyterLab. That target is convenient when working from a repository checkout, but the README's explicit `uv run jupyter lab` command is the more precise first-use instruction for someone following the release instructions.

The limits of a notebook-first lab repository

The clearest limitation is scope. ISLP_labs is educational material, not a packaged statistical learning framework. The README does not document a public Python API, installation of an `islp_labs` library, command-line interface, service endpoint, or production deployment model. That makes it a poor fit for an application that needs importable components and a stable software interface. Its value depends on reading and adapting the notebooks.

Reproducibility is also conditional. The README says the virtual environment should reproduce repository results, but it does not document a test suite or a command that verifies every notebook output. Notebook execution can also fail when optional software, data downloads, platform-specific libraries, or external Git dependencies are unavailable. The requirements include a Git dependency for `l0bnb`, so network access and Git availability become part of a clean installation. The repository does not say how to handle an offline installation or how to validate that a generated figure matches the book.

The lab collection also carries a version boundary. `requirements.txt` and the README refer to `v2.2.4`, while the repository's current setup command is tied to that release. Users who want to match a later book revision should check the release and requirements before assuming that the current notebook set is identical. Finally, the removal of checked-in R Markdown files means R users must either convert notebooks with `make Rmd` or accept Jupyter as the primary interface. The repository documents conversion, but it does not describe an R-specific runtime as part of the lab environment.

ISLP_labs compared with a standalone analysis package

The closest practical alternative is to use the individual libraries listed in `requirements.txt` directly, creating your own environment and writing analysis code for each dataset. That approach gives an engineer direct control over imports, versions, and application structure, but it removes the chapter sequence, teaching context, and the prepared data files supplied by ISLP_labs.

Another alternative is to use an earlier repository state or a local copy of the ISLP book examples and manage dependencies manually. This can be preferable when a project needs a single stable notebook independent of the current repository, but it requires the reader to identify compatible package versions and recreate any missing data or conversion steps. ISLP_labs instead gives users a release tag, a pinned requirements file, a setup script, and a documented path to a reproducible notebook environment.

The difference is therefore pedagogical and operational, not a claim that ISLP_labs contains better algorithms. A standalone package is more appropriate when you need a maintained API and a small deployable dependency surface. ISLP_labs is more appropriate when you need the exercises to follow the ISLP chapters and want the repository's data and environment setup alongside them.

Maintenance, licensing, and upgrade implications

The repository is not archived, and its last push was on 2026-10-08. The README says the repository will track lab changes as the ISLP source changes, so notebook content and dependency pins may change with the book. A recent release alone does not guarantee that every notebook is compatible with an arbitrary Python environment. Users who need reproducibility should record the release tag and use the requirements associated with it.

The repository is licensed under `BSD-2-Clause`. That identifier permits use and modification under the terms of that license, but this review does not interpret its legal obligations or provide legal advice. The practical review point is that the license applies to this repository and does not remove the need to review dependencies separately, especially because the requirements include packages installed from GitHub rather than only versioned releases from a package index.

The Makefile offers explicit maintenance-related workflows. `make install` creates the environment and installs requirements, `make jupyter` installs JupyterLab and launches it, `make Rmd` builds R Markdown files, and `make marimo` installs Marimo and opens the repository. These targets centralize setup commands, but they also make upgrades dependent on the contents of `requirements.txt` and `.python-version`. The repository does not document a migration procedure for old notebooks, so an upgrade should be treated as a new release check rather than an automatic compatibility promise.

Editorial conclusion

Adopt ISLP_labs if you are following ISLP and want versioned lab notebooks rather than a standalone analysis library. Do not treat it as a production machine-learning package, since it contains educational notebooks and relies on the separate ISLP setup. Verify first that Python 3.12, uv, JupyterLab, and the selected notebook release can run in your environment; the repository's last push was on 2026-10-08.

Frequently asked questions

Official sources

1. intro-stat-learning/ISLP_labs on GitHub
2. Issues
3. License: BSD-2-Clause
4. README
5. Releases