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| Robuster XGBoost× | Gradient Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016 (XGBoost); robust loss concept from 1964 | 2001 |
| Urheber≠ | Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss) | Friedman, J. H. |
| Typ≠ | Ensemble (gradient boosting with robust objective) | Ensemble (sequential boosting of decision trees) |
| Wegweisende Quelle≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Aliasnamen | XGBoost with Huber loss, outlier-robust gradient boosting, robust GBDT, XGBoost robust regression | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | 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. |
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