So sánh phương pháp

	Cây Quyết định	Hồi quy Logistic	Stacking
Lĩnh vực	Học máy	Thống kê nghiên cứu	Học máy
Họ	Machine learning	Process / pipeline	Machine learning
Năm ra đời	1984	1958	1992
Người khởi xướng	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Wolpert, D.H.
Loại	Recursive partitioning (if-then rules)	Method	Ensemble (heterogeneous meta-learning)
Công trình gốc	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Tên gọi khác	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Liên quan	5	3	5
Tóm tắt	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.