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| Solidne zwiększanie (Robust Boosting)× | XGBoost× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1999–2001 | 2016 |
| Twórca≠ | Freund, Y.; Mason, L. et al. | Chen, T. & Guestrin, C. |
| Typ≠ | Ensemble (robust sequential boosting) | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors. | 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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