Comparer des méthodes

	Ensemble de Bagging	Vote majoritaire
Domaine	Apprentissage ensembliste	Apprentissage ensembliste
Famille	Machine learning	Machine learning
Année d'origine	1996	1996
Auteur d'origine	Leo Breiman	Leo Breiman
Type	parallel ensemble	voting aggregation
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Alias	bootstrap aggregating	hard voting
Apparentées	4	5
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.	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.