Vertaile menetelmiä

	Gradient Boosting	Huber-regressio
Tieteenala	Koneoppiminen	Tilastotiede
Menetelmäperhe	Machine learning	Regression model
Syntyvuosi	2001	1964
Kehittäjä	Friedman, J. H.	Peter J. Huber
Tyyppi	Ensemble (sequential boosting of decision trees)	Robust linear regression (M-estimation)
Alkuperäislähde	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.
Rinnakkaisnimet	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
Liittyvät	5	5
Tiivistelmä	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.	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.