Comparer des méthodes

	Bagging (Bootstrap Aggregating)	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	2016
Auteur d'origine	Breiman, L.	Chen, T. & Guestrin, C.
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (gradient-boosted decision trees)
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	5	5
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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.