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Code for implementations of interpretable machine learning models and demos of how to use various interpretability techniques (with accompanying slides here).
Provides scikit-learn style wrappers/implementations of different interpretable models (see readmes in individual folders within imodels for details)
The demos are contained in 3 main notebooks, summarized in cheat_sheet.pdf
The interpretable models within the imodels folder can be easily installed and used.
pip install git+https://github.com/csinva/interpretability-implementations-demos
from imodels.bayesian_rule_lists import RuleListClassifier
model = RuleListClassifier()
model.fit(X_train, y_train)
model.score(X_test, y_test)
preds = model.predict(X_test)
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