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	Robust Random Forest	XGBoost
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2000s–2010s	2016
Twórca	Various (extensions of Breiman 2001 Random Forest)	Chen, T. & Guestrin, C.
Typ	Robust Ensemble (noise-tolerant bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Źródło pierwotne	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Inne nazwy	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest	XGBoost, extreme gradient boosting, scalable tree boosting
Pokrewne	6	5
Podsumowanie	Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.	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.