Gradient boosting using decision trees and random forests are similar ... meaning that 96.5% of our predictions are very good. The graph below demonstrates our predicted results from the XGBoost ...

In this paper, we employ a gradient-boosting decision-tree (GBDT) method to improve firm failure prediction and explain how to better analyze the relative importance of each financial variable.

We then compare the performances of seven supervised models, i.e., naive Bayes, logistic, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), as well as tree-based methods ...

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 ...