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| Gradient Boosting× | Huber-regresszió× | |
|---|---|---|
| Tudományterület≠ | Gépi tanulás | Statisztika |
| Módszercsalád≠ | Machine learning | Regression model |
| Keletkezés éve≠ | 2001 | 1964 |
| Megalkotó≠ | Friedman, J. H. | Peter J. Huber |
| Típus≠ | Ensemble (sequential boosting of decision trees) | Robust linear regression (M-estimation) |
| Alapmű≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. DOI ↗ |
| Alternatív nevek | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. |
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