Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Wzmocnienie× | Gradient Boosting× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1990–1997 | 2001 |
| Twórca≠ | Schapire, R. E.; Freund, Y. | Friedman, J. H. |
| Typ≠ | Sequential ensemble (iterative reweighting) | Ensemble (sequential boosting of decision trees) |
| Źródło pierwotne≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Inne nazwy | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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. |
| ScholarGateZbiór danych ↗ |
|
|