Compara mètodes

	Gradient Boosting	Gradient Boosting Robust
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2001	2001
Autor original	Friedman, J. H.	Friedman, J. H. (with Huber loss from Huber, P. J.)
Tipus	Ensemble (sequential boosting of decision trees)	Ensemble (boosted trees with robust loss)
Font seminal	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.
Àlies	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Relacionats	5	6
Resum	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.	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.