Interpretable Machine Learning (A Guide for Making Black Box Models Explainable)

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🔘 Book page: christophm.github.io/interpretable-ml-book/index.html

Objective

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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Author

Christoph Molnar . Data scientist and PhD candidate in interpretable machine learning. Interested in making the decisions from algorithms more understandable for humans. (Source: christophm.github.io).

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