방법 비교

	로버스트 부스팅(Robust Boosting)	XGBoost
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1999–2001	2016
창시자	Freund, Y.; Mason, L. et al.	Chen, T. & Guestrin, C.
유형	Ensemble (robust sequential boosting)	Ensemble (gradient-boosted decision trees)
원전	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
별칭	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	XGBoost, extreme gradient boosting, scalable tree boosting
관련	6	5
요약	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.