Gradient boosting decision tree (GBDT) for firm failure prediction is proposed. Sensitivity analysis and model interpretability of GBDT are analyzed and validated. GDBT, bagging, Adaboost, Random ...

Updating The Model By Adding Decision Tree Predictions The predictions of the new weak learner are scaled by a learning rate and added to the previous model to update the overall prediction. 5.

This algorithm consists of a distributed gradient-boosted decision tree (GBDT) machine learning library that can help accurately predict a target variable by combining an ensemble of estimates ...