방법 비교

	배깅 (Bootstrap Aggregating)	AdaBoost	결정 트리	그래디언트 부스팅	랜덤 포레스트
분야	머신러닝	머신러닝	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
기원 연도	1996	1997	1984	2001	2001
창시자	Breiman, L.	Freund, Y. & Schapire, R.E.	Breiman, Friedman, Olshen & Stone	Friedman, J. H.	Breiman, L.
유형	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of weak learners)	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
원전	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
별칭	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
관련	5	5	5	5	4
요약	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.