Comparer des méthodes

	Forêt Aléatoire	Empilement
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1992
Auteur d'origine	Breiman, L.	Wolpert, D.H.
Type	Ensemble (bagging of decision trees)	Ensemble (heterogeneous meta-learning)
Source fondatrice	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Alias	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Apparentées	4	5
Résumé	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.	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.