Interpretable Machine Learning is a comprehensive guide book about techniques for explaining black box machine learning models and making their decisions transparent.
The book addresses the challenge that machine learning systems increasingly influence daily decisions yet often operate as unexplainable black boxes. It tackles this by surveying methods to interpret model behavior and build trust in algorithmic decisions. The approach begins with algorithms that inherently produce interpretable models and their interpretation methods, then progresses to techniques for analyzing and explaining complex models after they have been trained. The book serves practitioners who need to verify whether models learned generalizable patterns or merely captured training data artifacts, as well as stakeholders evaluating whether to deploy machine learning systems.
The book is recommended for machine learning practitioners, data scientists, statisticians, and decision-makers considering algorithmic solutions. It suits anyone building machine learning products who needs to understand or explain model behavior to users or regulators. The content spans from foundational interpretability concepts through advanced analysis methods, making it accessible to those new to the topic while providing depth for experienced practitioners. The book is available online for free, with paid ebook and paperback editions also offered.
The project maintains an automated build process that validates the book content. Contributions are actively welcomed through pull requests for error corrections, and the maintainer solicits feedback on content suggestions and real-world applications of the interpretation methods discussed. The book references related resources including an R package for implementing interpretation methods and a companion book focused specifically on SHAP techniques.
