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Straightforward implementations of interpretable ML models + demos of how to use various interpretability techniques. Code is optimized for readability. Pull requests welcome!
Implementations of imodels • Demo notebooks • Docs
Scikit-learn style wrappers/implementations of different interpretable models. Docs available here. 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 import RuleListClassifier, RuleFit, GreedyRuleList, SkopeRules, SLIM, IRFClassifier
model = RuleListClassifier() # initialize Bayesian Rule List
model.fit(X_train, y_train) # fit model
preds = model.predict(X_test) # discrete predictions: shape is (n_test, 1)
preds_proba = model.predict_proba(X_test) # predicted probabilities: shape is (n_test, n_classes)
The demos are contained in 3 main notebooks, following this cheat-sheet:
Press p or to see the previous file or, n or to see the next file
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