Võrdle meetodeid

	Robustne võimendamine	Gradient Boosting	Robustne gradient boosting
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	1999–2001	2001	2001
Looja	Freund, Y.; Mason, L. et al.	Friedman, J. H.	Friedman, J. H. (with Huber loss from Huber, P. J.)
Tüüp	Ensemble (robust sequential boosting)	Ensemble (sequential boosting of decision trees)	Ensemble (boosted trees with robust loss)
Algallikas	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	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.
Rööpnimetused	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Seotud	6	5	6
Kokkuvõte	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.	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.