• Title: Interpretable Machine Learning, 2nd Edition: A Guide for Making Black Box Models Explainable
• Author(s) Christoph Molnar
• Publisher: Independently published (2022); eBook (2nd Edition, 2024-05-26; Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Paperback: 329 pages
• eBook: HTML
• Language: English
• ISBN-10/ASIN: B09TMWHVB4
• ISBN-13: 979-8411463330
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Book Description

TThis book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance.

• Christoph Molnar, is an expert in machine learning and statistics, with a Ph.D. in interpretable machine learning from Ludwig-Maximilians Universität München, LocationMunich Area, Germany.

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• Interpretable Machine Learning, 2nd Edition: A Guide for Making Black Box Models Explainable
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