What is the difference between GBDT and XGBoost?

1. GBDT

GBDT (Gradient Boosting Decision Tree) is an integrated learning algorithm that belongs to the Boosting family. It improves the predictive performance of the overall model by iteratively training multiple decision trees, each of which attempts to correct the errors of the previous tree. GBDT is widely used in both regression and classification problems and is considered a powerful machine learning algorithm.

The main features and steps of GBDT are as follows:

1. Base model: GBDTs usually use decision trees as base models (weak learners). Initially, a tree is trained that contains a single leaf node whose value is the mean (for regression problems) or probability (for classification problems) of all samples.

2. Iterative training: GBDT trains multiple decision trees iteratively. In each iteration, a new decision tree is trained to correct the error of the previous round based on the residual between the predicted result of the previous round and the actual label. The predictions of the new tree are then added to the previous predictions, gradually improving the model predictions. </