Linganisha mbinu

	Regressioni ya Elastic Net	Regressioni ya Mtepe
Nyanja	Takwimu	Ujifunzaji wa Mashine
Familia	Regression model	Machine learning
Mwaka wa asili	2005	1970
Mwanzilishi	Hui Zou and Trevor Hastie	Hoerl, A.E. & Kennard, R.W.
Aina	Penalized linear regression	L2-regularized linear regression
Chanzo asilia	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.
Majina mbadala	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Zinazohusiana	6	4
Muhtasari	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.