方法对比

	梯度提升（Gradient Boosting）	XGBoost
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2001	2016
提出者	Friedman, J. H.	Chen, T. & Guestrin, C.
类型	Ensemble (sequential boosting of decision trees)	Ensemble (gradient-boosted decision trees)
开创性文献	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
别名	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	XGBoost, extreme gradient boosting, scalable tree boosting
相关	5	5
摘要	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.