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Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily understandable. Pull requests very welcome!
Docs • Popular imodels • Custom imodels • Demo notebooks
Implementations of different interpretable models, all compatible with scikit-learn. The interpretable models can be easily used and installed:
from imodels import BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier
from imodels import SLIMRegressor, RuleFitRegressor
model = BayesianRuleListClassifier() # initialize a model
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)
Install with pip install imodels
(see here for help). Contains the following models:
Model | Reference | Description |
---|---|---|
Rulefit | 🗂️, 🔗, 📄 | Extracts rules from a decision tree then builds a sparse linear model with them |
Skope rules | 🗂️, 🔗 | Extracts rules from gradient-boosted trees, deduplicates them, then forms a linear combination of them based on their OOB precision |
Bayesian rule list | 🗂️, 🔗, 📄 | Learns a compact rule list by sampling rule lists (rather than using a greedy heuristic) |
Greedy rule list | 🗂️, 🔗 | Uses CART to learn a list (only a single path), rather than a decision tree |
Iterative random forest | 🗂️, 🔗, 📄 | (In progress) Repeatedly fit random forest, giving features with high importance a higher chance of being selected. |
Optimal classification tree | 🗂️, 🔗, 📄 | (In progress) Learns succinct trees using global optimization rather than greedy heuristics |
Sparse integer linear model | 🗂️, 📄 | Forces coefficients to be integers |
Rule sets | (Coming soon) Many popular rule sets including SLIPPER, Lightweight Rule Induction, MLRules |
Docs 🗂️, Reference code implementation 🔗, Research paper 📄
The code here contains many useful and readable functions for a variety of rule-based models, contained in the util folder. This includes functions and simple classes for rule deduplication, rule screening, converting between trees, rulesets, and pytorch neural nets. The final derived rules easily allows for extending any of the following general classes of models:
Rule set | Rule list | (Decision) Rule tree | Algebraic models |
---|---|---|---|
Demos are contained in the notebooks folder.
imodels
for deriving a clinical decision ruleFor updates, star the repo, see this related repo, or follow @csinva_. Please make sure to give authors of original methods / base implementations appropriate credit!
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