Salīdzināt metodes

	Regresija ar elastīgo tīklu	Regulētā lineārā regresija (Ridge Regression)
Nozare	Statistika	Mašīnmācīšanās
Saime	Regression model	Machine learning
Izcelsmes gads	2005	1970
Autors	Hui Zou and Trevor Hastie	Hoerl, A.E. & Kennard, R.W.
Tips	Penalized linear regression	L2-regularized linear regression
Pirmavots	Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Citi nosaukumi	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Saistītās	6	4
Kopsavilkums	Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.	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.