Comparer des méthodes

	Bagging (Bootstrap Aggregating)	Naive Bayes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1997
Auteur d'origine	Breiman, L.	Mitchell, T. M. (textbook treatment)
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Probabilistic classifier (Bayes' theorem with conditional independence)
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Apparentées	5	4
Résumé	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.