So sánh phương pháp

	Stacking	Cây Quyết định
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1992	1984
Người khởi xướng	Wolpert, D.H.	Breiman, Friedman, Olshen & Stone
Loại	Ensemble (heterogeneous meta-learning)	Recursive partitioning (if-then rules)
Công trình gốc	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Tên gọi khác	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liên quan	5	5
Tóm tắt	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.	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.