Bandingkan metode

	XGBoost	Peningkatan Gradien
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2016	2001
Pencetus	Chen, T. & Guestrin, C.	Friedman, J. H.
Tipe	Ensemble (gradient-boosted decision trees)	Ensemble (sequential boosting of decision trees)
Sumber perintis	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.
Alias	XGBoost, extreme gradient boosting, scalable tree boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Terkait	5	5
Ringkasan	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.