Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Robust XGBoost× | XGBoost× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2016 (XGBoost); robust loss concept from 1964 | 2016 |
| Ophavsperson≠ | Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss) | Chen, T. & Guestrin, C. |
| Type≠ | Ensemble (gradient boosting with robust objective) | Ensemble (gradient-boosted decision trees) |
| Oprindelig kilde≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Aliasser≠ | XGBoost with Huber loss, outlier-robust gradient boosting, robust GBDT, XGBoost robust regression | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Robust XGBoost combines the scalable gradient boosting framework of XGBoost with robust loss functions — primarily the Huber loss or its variants — to produce a gradient boosted tree ensemble that resists the distorting influence of outliers. By replacing the squared-error objective with a loss that down-weights large residuals, the model delivers reliable predictions on continuous targets even when training data contain extreme values or label noise. | 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. |
| ScholarGateDatasæt ↗ |
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