Comparer des méthodes

	Robust Boosting	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1999–2001	2001
Auteur d'origine	Freund, Y.; Mason, L. et al.	Friedman, J. H.
Type	Ensemble (robust sequential boosting)	Ensemble (sequential boosting of decision trees)
Source fondatrice	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.