Comparer des méthodes

	Gradient Boosting Robuste	Régression linéaire robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1964–1987
Auteur d'origine	Friedman, J. H. (with Huber loss from Huber, P. J.)	Huber, P. J.; Rousseeuw, P. J.
Type	Ensemble (boosted trees with robust loss)	Outlier-resistant supervised regression
Source fondatrice	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Alias	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Apparentées	6	5
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.	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.