Comparer des méthodes

	Bagging (Bootstrap Aggregating)	Regroupement par K-moyennes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1967
Auteur d'origine	Breiman, L.	MacQueen, J.
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Partitional clustering (centroid-based)
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Apparentées	5	3
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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.