Comparer des méthodes

	XGBoost Robuste	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2016 (XGBoost); robust loss concept from 1964	2001
Auteur d'origine	Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss)	Friedman, J. H.
Type	Ensemble (gradient boosting with robust objective)	Ensemble (sequential boosting of decision trees)
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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	XGBoost with Huber loss, outlier-robust gradient boosting, robust GBDT, XGBoost robust regression	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Apparentées	6	5
Résumé	Robust XGBoost combines the scalable gradient boosting framework of XGBoost with robust loss functions — primarily the Huber loss or its variants — to produce a gradient boosted tree ensemble that resists the distorting influence of outliers. By replacing the squared-error objective with a loss that down-weights large residuals, the model delivers reliable predictions on continuous targets even when training data contain extreme values or label noise.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.