手法を比較

	勾配ブースティングアンサンブル	XGBoost
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2001	2016
提唱者	Friedman, J. H.	Chen, T. & Guestrin, C.
種類	Ensemble (sequential boosting of decision trees)	Ensemble (gradient-boosted decision trees)
原典	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.
別名	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	XGBoost, extreme gradient boosting, scalable tree boosting
関連	6	5
概要	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.