Interpretable Machine Learning

This book provides a detailed overview of techniques to make machine learning models more interpretable.

It covers inherently interpretable models like decision trees and linear regression, as well as model-agnostic methods such as SHAP, LIME, and permutation feature importance.

The book is designed for machine learning practitioners, data scientists, and statisticians interested in enhancing model interpretability.