Σύγκριση μεθόδων

	Stacking	Λογιστική Παλινδρόμηση
Πεδίο	Μηχανική Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	1992	1958
Δημιουργός	Wolpert, D.H.	David Roxbee Cox
Τύπος	Ensemble (heterogeneous meta-learning)	Method
Θεμελιώδης πηγή	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	logit model, binomial logistic regression, LR
Συναφείς	5	3
Σύνοψη	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.	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.