Interpretable Libraries

5 Components & Libraries

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Interpretable Libraries

awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

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…

PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

PiML (or π-ML, /ˈpaɪ·ˈem·ˈel/) is a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports a growing list…

interpretable_machine_learning_with_python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

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…

H2O.ai Machine Learning Interpretability Resources

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…