Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Gradient Boosting× | Online Boosting× | Random Forest× | |
|---|---|---|---|
| Obor | Strojové učení | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning | Machine learning |
| Rok vzniku | 2001 | 2001 | 2001 |
| Tvůrce≠ | Friedman, J. H. | Oza, N. C. & Russell, S. | Breiman, L. |
| Typ≠ | Ensemble (sequential boosting of decision trees) | Online ensemble (incremental boosting) | Ensemble (bagging of decision trees) |
| Původní zdroj≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Další názvy | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Příbuzné≠ | 5 | 6 | 4 |
| Shrnutí≠ | 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. | Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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