Σύγκριση μεθόδων

	Λογιστική Παλινδρόμηση	Stacking
Πεδίο	Ερευνητική Στατιστική	Μηχανική Μάθηση
Οικογένεια	Process / pipeline	Machine learning
Έτος προέλευσης	1958	1992
Δημιουργός	David Roxbee Cox	Wolpert, D.H.
Τύπος	Method	Ensemble (heterogeneous meta-learning)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	logit model, binomial logistic regression, LR	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Συναφείς	3	5
Σύνοψη	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.