Comparer des méthodes

	Gradient Boosting Robuste	Gradient Boosting Régularisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Auteur d'origine	Friedman, J. H. (with Huber loss from Huber, P. J.)	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Type	Ensemble (boosted trees with robust loss)	Regularized ensemble (additive tree model)
Source fondatrice	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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.
Alias	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Apparentées	6	6
Résumé	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.	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.