방법 비교

	AdaBoost	로지스틱 회귀
분야	머신러닝	연구 통계
계열	Machine learning	Process / pipeline
기원 연도	1997	1958
창시자	Freund, Y. & Schapire, R.E.	David Roxbee Cox
유형	Ensemble (sequential boosting of weak learners)	Method
원전	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
별칭	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	logit model, binomial logistic regression, LR
관련	5	3
요약	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.	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.