29.22 Learning More

  • An Introduction to Statistical Learning (with Applications in R) (statlearning.com / #book_club-islr): Statistical explanations of various machine learning methods, with explanations of how to apply them in R. A good introduction to all of the types of models and why they work (or don’t work) the way they do.
  • Tidy Modeling with R (tmwr.org / #book_club-tmwr): An opinionated introduction to using the tidymodels family of packages to build predictive models. Very hands-on and useful, but I think I might want to read it again after ISLR.
  • Feature Engineering and Selection: A Practical Approach for Predictive Models (feat.engineering / #book_club-feat_eng): Techniques for manipulating data to get better results out of models.
  • Applied Predictive Modeling (github.com/topepo/tidy-apm / #project-tidy_apm): There isn’t a free online version of this book yet, but it’s at least theoretically in the works. This was published about 10 years ago by the leader of the tidymodels team, and he has started to update it to tidymodels code. I’d recommend not reading this one until/unless he takes that project back up (very possibly with the help of the DSLC community).