Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Случайный лес× | XGBoost× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2001 | 2016 |
| Автор метода≠ | Breiman, L. | Chen, T. & Guestrin, C. |
| Тип≠ | Ensemble (bagging of decision trees) | Ensemble (gradient-boosted decision trees) |
| Основополагающий источник≠ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Другие названия≠ | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | XGBoost, extreme gradient boosting, scalable tree boosting |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateНабор данных ↗ |
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