قارن الطرق

	انحدار ريدج (Ridge Regression)	شبكة المرونة (Elastic Net)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1970	2005
صاحب الطريقة	Hoerl, A.E. & Kennard, R.W.	Zou, H. & Hastie, T.
النوع	L2-regularized linear regression	Regularized linear regression (L1 + L2 penalty)
المصدر التأسيسي	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.
الأسماء البديلة	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
ذات صلة	4	4
الملخص	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.