Comparer des méthodes

	Boosting Régularisé	Gradient Boosting Robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001–2016	2001
Auteur d'origine	Friedman, J. H.; extended by Chen & Guestrin	Friedman, J. H. (with Huber loss from Huber, P. J.)
Type	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (boosted trees with robust loss)
Source fondatrice	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Alias	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Apparentées	5	6
Résumé	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.	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.