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	XGBoost	Gradient Boosting
Odbor	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2016	2001
Tvorca	Chen, T. & Guestrin, C.	Friedman, J. H.
Typ	Ensemble (gradient-boosted decision trees)	Ensemble (sequential boosting of decision trees)
Pôvodný zdroj	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Ďalšie názvy	XGBoost, extreme gradient boosting, scalable tree boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Príbuzné	5	5
Zhrnutie	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.	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.