Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Градиентный бустинг× | Online Random Forest× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2001 | 2009 |
| Автор метода≠ | Friedman, J. H. | Saffari, A. et al. |
| Тип≠ | Ensemble (sequential boosting of decision trees) | Incremental ensemble (streaming decision trees) |
| Основополагающий источник≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE. link ↗ |
| Другие названия | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | ORF, streaming random forest, incremental random forest, adaptive random forest |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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 Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time. |
| ScholarGateНабор данных ↗ |
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