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Code accompanying a review article on interpretability and XAI. Includes examples for both simple (sparse regression) and sophisticated (concept bottlenecks) approaches, using notebooks that can be run in a few minutes.
A notebook exploring the paper "InterpretML: A Unified Framework for Machine Learning Interpretability" (H. Nori, S. Jenkins, P. Koch, and R. Caruana 2019).