방법 비교

	결정 트리	그래디언트 부스팅	XGBoost
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1984	2001	2016
창시자	Breiman, Friedman, Olshen & Stone	Friedman, J. H.	Chen, T. & Guestrin, C.
유형	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)	Ensemble (gradient-boosted decision trees)
원전	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
별칭	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	XGBoost, extreme gradient boosting, scalable tree boosting
관련	5	5	5
요약	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.	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.