Comparer des méthodes

	Ensemble de Bagging	AdaBoost	Forêt Aléatoire
Domaine	Apprentissage ensembliste	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1996	1997	2001
Auteur d'origine	Leo Breiman	Freund, Y. & Schapire, R.E.	Breiman, L.
Type	parallel ensemble	Ensemble (sequential boosting of weak learners)	Ensemble (bagging of decision trees)
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	bootstrap aggregating	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	4	5	4
Résumé	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.