Comparer des méthodes

	Ensemble par Boosting	Forêt Aléatoire
Domaine	Apprentissage ensembliste	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990	2001
Auteur d'origine	Robert Schapire	Breiman, L.
Type	sequential ensemble	Ensemble (bagging of decision trees)
Source fondatrice	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	adaptive boosting, sequential ensemble	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	4	4
Résumé	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.	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.