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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
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  • Title: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
  • Author(s) Christoph Molnar
  • Publisher: Independently published (2022); eBook (Creative Commons Licensed)
  • License(s): CC BY-NC-SA 4.0
  • Paperback: 329 pages
  • eBook: HTML
  • Language: English
  • ISBN-10/ASIN: B09TMWHVB4
  • ISBN-13: 979-8411463330
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Book Description

This book explains to you how to make (supervised) machine learning models interpretable.

The 2nd edition of Interpretable Machine Learning offers a substantial improvement over the 1st edition. The book now also covers approaches specific to interpreting deep neural networks. The update also brings many new model-agnostic interpretation methods such as the popular SHAP, Anchors, and functional decomposition.

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 methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.

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. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.

This book is not for people trying to learn machine learning from scratch. If you are new to machine learning, there are a lot of books and other resources to learn the basics. The author recommends the book The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman (2009).

It starts with some (dystopian) short stories that are not needed to understand the book, but hopefully will entertain and make you think. Then the book explores the concepts of machine learning interpretability. It ends with an optimistic outlook on what the future of interpretable machine learning might look like.

You can either read the book from beginning to end or jump directly to the methods that interest you.

About the Authors
  • Christoph Molnar is a data scientist and PhD candidate in interpretable machine learning, Ludwig-Maximilians Universit√§t M√ľnchen, LocationMunich Area, Germany.
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