Methoden vergleichen

	Ridge Regression	Elastic Net
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1970	2005
Urheber	Hoerl, A.E. & Kennard, R.W.	Zou, H. & Hastie, T.
Typ	L2-regularized linear regression	Regularized linear regression (L1 + L2 penalty)
Wegweisende Quelle	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	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.
Aliasnamen	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
Verwandt	4	4
Zusammenfassung	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.	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.