방법 비교

	설명 가능한 결정 트리	로지스틱 회귀	랜덤 포레스트
분야	머신러닝	연구 통계	머신러닝
계열	Machine learning	Process / pipeline	Machine learning
기원 연도	1984 (CART); XAI framing formalized 2010s–2020s	1958	2001
창시자	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	David Roxbee Cox	Breiman, L.
유형	Interpretable supervised learning model	Method	Ensemble (bagging of decision trees)
원전	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
별칭	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
관련	4	3	4
요약	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.	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.	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.