christophM/interpretable-ml-book
A guide book on interpretable machine learning techniques for explaining black box models, written in Jupyter Notebook format.

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This repository hosts a comprehensive book on interpretable machine learning, covering techniques to make black box models transparent and explainable. The book progresses from simple interpretable models to analyzing complex model decisions, targeting practitioners, data scientists, and stakeholders working with ML systems.