​​Interpretable Machine Learning: A Guide for Making Black Box | Data Science Digest

​​Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

The book by Christoph Molnar goes deep to explain how to make supervised machine learning models more interpretable. You’ll start by exploring the concepts of interpretability to learn about simple, interpretable models such as decision trees, decision rules, and linear regression. Then, you’ll look into 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 ML models for tabular 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.

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