विधियों की तुलना करें

	लॉजिस्टिक रिग्रेशन	रिज रिग्रेशन
क्षेत्र	अनुसंधान सांख्यिकी	मशीन अधिगम
परिवार	Process / pipeline	Machine learning
उद्भव वर्ष	1958	1970
प्रवर्तक	David Roxbee Cox	Hoerl, A.E. & Kennard, R.W.
प्रकार	Method	L2-regularized linear regression
मौलिक स्रोत	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
उपनाम	logit model, binomial logistic regression, LR	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
संबंधित	3	4
सारांश	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.