Calibrated Random Forest

This Python package implements the CaliForest algorithm presented in ACM CHIL 2020.

You can use CaliForest almost the same way you used RandomForest i.e. you can just replace RandomForest with CaliForest. The only difference would be that its predicted scores will be better calibrated than the regular RandomForest output, while maintaining the original predictive performance. For more details, please see "CaliForest: Calibrated Random Forest for Health Data" in ACM Conference on Health, Inference, and Learning 2020.

Installing from the source:

$ git clone [email protected]:yubin-park/califorest.git
$ cd califorest
$ python setup.py develop

Training + Prediction:

from califorest import CaliForest

model = CaliForest(n_estimators=100,
                    max_depth=5,
                    min_samples_split=3,
                    min_samples_leaf=1,
                    ctype="isotonic")

model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)[:,1]

Calibration metrics:

from califorest import metrics as em

score_auc = roc_auc_score(y_test, y_pred)
score_hl = em.hosmer_lemeshow(y_test, y_pred)
score_sh = em.spiegelhalter(y_test, y_pred)
score_b, score_bs = em.scaled_Brier(y_test, y_pred)
rel_small, rel_large = em.reliability(y_test, y_pred)

MIT License

Y. Park and J. C. Ho. 2020. CaliForest: Calibrated Random Forest for Health Data. ACM Conference on Health, Inference, and Learning (2020)