Comparer des méthodes

	Gradient Boosting Régularisé	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)	1990–1997
Auteur d'origine	Chen, T. & Guestrin, C. (building on Friedman, J. H.)	Schapire, R. E.; Freund, Y.
Type	Regularized ensemble (additive tree model)	Sequential ensemble (iterative reweighting)
Source fondatrice	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	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.
Alias	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	6	6
Résumé	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.	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.