Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.

After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic tools for interpreting black box models and explaining individual predictions. In an ideal future, machines will be able to explain their decisions and the algorithmic age we move toward will be as human as possible.

The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. This book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.

You can buy the PDF and e-book version (epub, mobi) on

About me: My name is Christoph Molnar, I’m a statistician and a machine learner. My goal is to make machine learning interpretable. If you are interested in improving the interpretability of your machine learning models, do not hesitate to contact me!



Follow me on Twitter! @ChristophMolnar

Cover by @ArbeitAmText

Creative Commons License

Creative Commons License

This book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.