विधियों की तुलना करें

	लासो रिग्रेशन	इलास्टिक नेट	लॉजिस्टिक रिग्रेशन	रिज रिग्रेशन
क्षेत्र	मशीन अधिगम	मशीन अधिगम	अनुसंधान सांख्यिकी	मशीन अधिगम
परिवार	Machine learning	Machine learning	Process / pipeline	Machine learning
उद्भव वर्ष	1996	2005	1958	1970
प्रवर्तक	Tibshirani, R.	Zou, H. & Hastie, T.	David Roxbee Cox	Hoerl, A.E. & Kennard, R.W.
प्रकार	Regularized linear regression (L1 penalty)	Regularized linear regression (L1 + L2 penalty)	Method	L2-regularized linear regression
मौलिक स्रोत	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	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	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	logit model, binomial logistic regression, LR	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
संबंधित	4	4	3	4
सारांश	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	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.