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Interpretable_machine_learning_with_python ⭐ 629

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

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

Interpretable Ml ⭐ 17

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Imodels Experiments ⭐ 13

Experiments with experimental rule-based models to go along with imodels.

Anchorsonr ⭐ 8

Implementation of the Anchors algorithm: Explain black-box ML models

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