Closed hcho3 closed 11 months ago
Scikit-learn's gradient boosting algorithm performs "boosting from the average," where a simple base estimator is fitted from the distribution of class labels (or the average label, if regression is used) and is set as the initial learner in the ensemble model. Boosting from the average speeds up convergence. See https://github.com/dmlc/xgboost/issues/4321 and https://lightgbm.readthedocs.io/en/latest/Parameters.html#boost_from_average
Currently, Treelite throws an error if init="zero" is omitted when building GradientBoostingClassifier / GradientBoostingRegressor objects. We should remove this restriction, in order to support boosting from the average
init="zero"
GradientBoostingClassifier
GradientBoostingRegressor
Scikit-learn's gradient boosting algorithm performs "boosting from the average," where a simple base estimator is fitted from the distribution of class labels (or the average label, if regression is used) and is set as the initial learner in the ensemble model. Boosting from the average speeds up convergence. See https://github.com/dmlc/xgboost/issues/4321 and https://lightgbm.readthedocs.io/en/latest/Parameters.html#boost_from_average
Currently, Treelite throws an error if
init="zero"is omitted when buildingGradientBoostingClassifier/GradientBoostingRegressorobjects. We should remove this restriction, in order to support boosting from the average