Comparer des méthodes

	Robust Boosting	Boosting	Boosting Régularisé
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1999–2001	1990–1997	2001–2016
Auteur d'origine	Freund, Y.; Mason, L. et al.	Schapire, R. E.; Freund, Y.	Friedman, J. H.; extended by Chen & Guestrin
Type	Ensemble (robust sequential boosting)	Sequential ensemble (iterative reweighting)	Regularized ensemble (boosting with shrinkage/penalty)
Source fondatrice	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting
Apparentées	6	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.