So sánh phương pháp

	Bagging (Bootstrap Aggregating)	XGBoost
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1996	2016
Người khởi xướng	Breiman, L.	Chen, T. & Guestrin, C.
Loại	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (gradient-boosted decision trees)
Công trình gốc	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Tên gọi khác	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	5	5
Tóm tắt	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.