Comparer des méthodes

	Ensemble de Bagging	Forêt Aléatoire
Domaine	Apprentissage ensembliste	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	2001
Auteur d'origine	Leo Breiman	Breiman, L.
Type	parallel ensemble	Ensemble (bagging of decision trees)
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	bootstrap aggregating	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	4	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.	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.