Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robust XGBoost× | XGBoost× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2016 (XGBoost); robust loss concept from 1964 | 2016 |
| Autors≠ | Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss) | Chen, T. & Guestrin, C. |
| Tips≠ | Ensemble (gradient boosting with robust objective) | Ensemble (gradient-boosted decision trees) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | XGBoost with Huber loss, outlier-robust gradient boosting, robust GBDT, XGBoost robust regression | XGBoost, extreme gradient boosting, scalable tree boosting |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. |
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