If you're looking for a book that will help you make machine learning models explainable, look no further than Interpretable Machine Learning.

This book provides a clear and concise explanation of the methods and mathematics behind the most important approaches to making machine learning models intepretable.

You'll learn about:

• Inherently interpretable models
• Methods that can make any machine model interpretable, such as SHAP, LIME and permutation feature importance.
• Interpretation methods specific to deep neural networks
• Why interpretability is important and what's behind this concept

About the author:
Christoph Molnar is an expert in machine learning and statistics. He did his Ph.D. in interpretable machine learning.