Chapter 1 Introduction

New methods for the interpretation of machine learning models are published at breakneck speed. To keep up with everything that is published would be madness and simply impossible. That is why you will not find the most novel and fancy methods in this book, but established methods and basic concepts of machine learning interpretability. These basics prepare you for making machine learning models interpretable. Internalizing the basic concepts also empowers you to better understand and evaluate any new paper on interpretability published on arxiv.org in the last 5 minutes since you began reading this book (I might be exaggerating the publication rate).

The book ends with an optimistic outlook on what might look like.

I hope you will enjoy the read!

• Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. “The elements of statistical learning”. www.web.stanford.edu/~hastie/ElemStatLearn/ (2009).↩