Preface

This book is a compilation of the most common mistakes when building machine learning systems with examples in Python. It is assumed that the reader already has some experience with machine learning and with the scikit-learn package. This book can also be used as a complementary source when learning about machine learning for the first time. After reading this book, you will be ready to build more robust and trustworthy machine learning models.

Supplemental Material

Supplemental material consists of the examples’ code and datasets. The source code for the examples can be downloaded from https://github.com/enriquegit/ml-mistakes-code. Instructions on how to set up the code are in Appendix A. A reference for all the utilized datasets is in Appendix B.

Conventions

DATASET names are written in uppercase italics. Functions are referred to by their name followed by parenthesis and omitting their arguments, for example: myFunction(). Class labels are written in italics and between single quotes: ‘label1’. The following icons are used to provide additional contextual information:

Provides a brief summary of the mistake.

Important information to consider.

A summary of how a mistake can be avoided.

Acknowledgements

I want to thank all my fish for their company while writing this book.

Figure 0.1: The real Science-Fish-Team.