Comparer des méthodes

	Empilement	Régression logistique	Forêt Aléatoire
Domaine	Apprentissage automatique	Statistiques de recherche	Apprentissage automatique
Famille	Machine learning	Process / pipeline	Machine learning
Année d'origine	1992	1958	2001
Auteur d'origine	Wolpert, D.H.	David Roxbee Cox	Breiman, L.
Type	Ensemble (heterogeneous meta-learning)	Method	Ensemble (bagging of decision trees)
Source fondatrice	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	3	4
Résumé	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.