Welcome to ML Insights’s documentation!¶
This package currently contains two useful sets of features. The first is around the Model X-ray, which gives some ways to understand black-box models. The second is around probability calibration.
$ pip install ml_insights
>>> import ml_insights as mli >>> xray = mli.ModelXRay(model, data)
>>> rfm = RandomForestClassifier(n_estimators = 500, class_weight='balanced_subsample') >>> rfm_cv = mli.SplineCalibratedClassifierCV(rfm) >>> rfm_cv.fit(X_train,y_train) >>> test_res_calib_cv = rfm_cv.predict_proba(X_test)[:,1] >>> log_loss(y_test,test_res_calib_cv)