방법 비교

	컨포멀 예측	로지스틱 회귀	불확실성 정량화
분야	머신러닝	연구 통계	시뮬레이션
계열	Machine learning	Process / pipeline	Process / pipeline
기원 연도	2005	1958	Seminal modern form: 2002
창시자	Vovk, Gammerman & Shafer	David Roxbee Cox	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)
유형	Distribution-free uncertainty quantification framework	Method	Computational uncertainty analysis framework
원전	Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer. ISBN: 978-0-387-00152-4	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.
별칭	Conformal Inference, Conformal Risk Control, Inductive Conformal Prediction, Uyumsal Tahmin	logit model, binomial logistic regression, LR	UQ, polynomial chaos expansion, PCE, Kriging surrogate
관련	2	3	9
요약	Conformal Prediction is a distribution-free framework for constructing statistically valid prediction sets (for classification) or prediction intervals (for regression) around the output of any pre-trained machine learning model. Introduced by Vovk, Gammerman, and Shafer in their 2005 monograph, it provides a finite-sample marginal coverage guarantee — the true label falls inside the prediction set with at least 1-alpha probability — without requiring parametric assumptions about the data distribution.	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.	Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs.