Technical requirements
Although we began the book with a "toy example," we will be leveraging real datasets throughout this book to be used in specific interpretation use cases. These come from many different sources and are often used only once.
To avoid that, readers spend a lot of time downloading, loading, and preparing datasets for single examples; there's a library called mldatasets that takes care of most of this. Instructions on how to install this library are located in the preface. In addition to mldatasets, this chapter's examples also use the pandas, numpy, statsmodel, sklearn, and matplotlib libraries. The code for this chapter is located here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter02.
