Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Устойчиво градиентно усилване× | XGBoost× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2001 | 2016 |
| Създател≠ | Friedman, J. H. (with Huber loss from Huber, P. J.) | Chen, T. & Guestrin, C. |
| Тип≠ | Ensemble (boosted trees with robust loss) | Ensemble (gradient-boosted decision trees) |
| Основополагащ източник≠ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Други названия≠ | gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees | XGBoost, extreme gradient boosting, scalable tree boosting |
| Свързани≠ | 6 | 5 |
| Резюме≠ | Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees. | 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 от данни ↗ |
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