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

• Category: Computer
• Author: Christoph Molnar
• License: CC BY-NC-SA 4.0
• Pages: : 318 pages
• Size: : HTML

Read and download free eBook intituled Interpretable Machine Learning: A Guide for Making Black Box Models Explainable in format PDF – : 318 pages created by Christoph Molnar.

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

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.

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