Gradient boosting machines (GBMs) are a powerful ensemble learning technique that builds a model incrementally by combining weak models (typically decision trees) to form a strong predictive model.

The gradient boosting regression model is prepared for training using these four statements: int numTrees = 200; int maxDepth = 2; int minSamples = 2; double lrnRate = 0.05; Notice that unlike some ...

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 ...

Once the gradient boosting model is trained, it is used to make fast predictions of new prices. We show that this approach leads to speed-ups of several orders of magnitude, while the loss of accuracy ...

We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to ...

Machine learning models used were the logistic regression, random forest, extreme gradient boosting, and light gradient boosting models. There were 222,127 outpatient visits from 5000 patients ...