So sánh phương pháp

	Tăng cường có điều chuẩn hóa	XGBoost
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001–2016	2016
Người khởi xướng	Friedman, J. H.; extended by Chen & Guestrin	Chen, T. & Guestrin, C.
Loại	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (gradient-boosted decision trees)
Công trình gốc	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.
Tên gọi khác	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	5	5
Tóm tắt	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.	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.