This repository provides my solutions for all exercises in the book "An Introduction to Statistical Learning with Applications in R", second edition, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
If you use these solutions or find them useful, please star this repository!
The solutions are written in bookdown format using (my) ISLRv2 solutions template.
For anyone reading this book, I believe there is great value in deriving the solutions yourself, and the template above can be forked to provide a great starting point as I've created template Rmarkdown files for each chapter and transcribed all questions as quotes within the chapter files leaving space for you to write your solutions either as text, or by writing R code chunks. For more details, refer to the template repository.
I've tried my best to provide solutions to each problem in this book, and I believe my answers should be (at least for the most part) correct. The solutions are relatively concise but hopefully comprehensive enough to address the purpose of each question.
If, when reading these solutions, you notice any inconsistencies, mistakes or have any other questions, please feel free to raise an issue or provide suggestions and I'll happily take a look and try to incorporate.
The HTML book is compiled from the Rmarkdown files in using a GitHub Actions workflow and then automatically deployed to GitHub pages.
You can view these solutions hosted from GitHub at the following location:
The R dependencies are provided in the DESCRIPTION file and you can install them by running:
devtools::install()Other than that you need the Python packages tensorflow and keras installed in the Python environment you are using to run the examples:
pip3 install tensorflow kerasRebuild the book with compiled solutions by running:
bookdown::render_book('index.Rmd', 'bookdown::gitbook')To render only one file/chapter (e.g. solutions for chapter 2), use:
rmarkdown::render(input="02-statistical-learning.Rmd", output_format=bookdown::pdf_document2())