เปรียบเทียบวิธี

	ต้นไม้ตัดสินใจ	การถดถอยโลจิสติก	การซ้อนทับ
สาขาวิชา	การเรียนรู้ของเครื่อง	สถิติการวิจัย	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Process / pipeline	Machine learning
ปีกำเนิด	1984	1958	1992
ผู้ริเริ่ม	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Wolpert, D.H.
ประเภท	Recursive partitioning (if-then rules)	Method	Ensemble (heterogeneous meta-learning)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	3	5
สรุป	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.