방법 비교

	랜덤 포레스트	로지스틱 회귀	XGBoost
분야	머신러닝	연구 통계	머신러닝
계열	Machine learning	Process / pipeline	Machine learning
기원 연도	2001	1958	2016
창시자	Breiman, L.	David Roxbee Cox	Chen, T. & Guestrin, C.
유형	Ensemble (bagging of decision trees)	Method	Ensemble (gradient-boosted decision trees)
원전	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
별칭	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
관련	4	3	5
요약	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.