Σύγκριση μεθόδων

	Παλινδρόμηση με δίχτυακή ελαστικότητα (Elastic Net Regression)	Παλινδρόμηση Ridge
Πεδίο	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	2005	1970
Δημιουργός	Hui Zou and Trevor Hastie	Hoerl, A.E. & Kennard, R.W.
Τύπος	Penalized linear regression	L2-regularized linear regression
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Συναφείς	6	4
Σύνοψη	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.