So sánh phương pháp

	Gradient Boosting	Cây Quyết định
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001	1984
Người khởi xướng	Friedman, J. H.	Breiman, Friedman, Olshen & Stone
Loại	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)
Công trình gốc	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Tên gọi khác	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liên quan	6	5
Tóm tắt	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.