Machine-learning-interpretability Libraries

A maintained and curated list of practical and awesome responsible machine learning resources. This section is for responsible ML guidance put forward by organizations or individuals, not for officia…

Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. The notebooks highlight techn…

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques i…