Comparer des méthodes

	Robust Boosting	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1999–2001	2016
Auteur d'origine	Freund, Y.; Mason, L. et al.	Chen, T. & Guestrin, C.
Type	Ensemble (robust sequential boosting)	Ensemble (gradient-boosted decision trees)
Source fondatrice	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	6	5
Résumé	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.	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.