christophM/interpretable-ml-book resource

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5364 stars · 1101 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

The source repository for Christoph Molnar's book 'Interpretable Machine Learning: A Guide for Making Black Box Models Explainable', written in Jupyter Notebooks and published free online. It covers interpretable models and model-agnostic explanation methods such as LIME, Shapley values, permutation feature importance, and accumulated local effects.

Use cases

• learn how to explain black box machine learning models
• understand LIME and Shapley values
• find a guide to interpretable machine learning techniques
• study model-agnostic interpretation methods
• learn about feature importance and accumulated local effects
• reference material for explainable AI research

When to choose

• you want a comprehensive, free, well-regarded reference on model interpretability
• you are a data scientist or ML practitioner needing to explain model predictions
• you want critical, in-depth discussion of interpretation methods' strengths and weaknesses

When to avoid

• you need a software library or tool rather than a book
• you want hands-on code tutorials for a specific framework
• you need a formal textbook with exercises for a course

Facets

learning-resource · maturity active

machine-learning nlp data-science documentation machine-learning artificial-intelligence data-science tutorials python cross-platform explainable-ai xai book lime shapley-values model-interpretation jupyter-notebook web-server

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