方法对比

	鲁棒梯度提升	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. (with Huber loss from Huber, P. J.)	Chen, T. & Guestrin, C.
类型	Ensemble (boosted trees with robust loss)	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 with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	XGBoost, extreme gradient boosting, scalable tree boosting
相关	6	5
摘要	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.	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.