XGBoost

lastmod 2026-11-10created 2026-07-312 edits

XGBoost (eXtreme Gradient Boosting) is an open-source machine learning library that implements the gradient boosting tree algorithm in a fast and scalable way. It combines the boosting approach, which sequentially adds decision trees to correct the errors of preceding models, with a regularization term and parallel tree construction, and is widely used for classification and regression problems on structured data. It provides bindings for multiple languages such as Python, R, and Java, and is often compared with similar libraries LightGBM and CatBoost.

See also

• Decision Tree
• Random Forest
• AdaBoost
• Gradient Boosting Tree (GBT)
• lightGBM
• XGBoost
• CatBoost