Elastic net

Summary

The elastic net fits a linear model by penalised least squares. Its penalty combines the $\ell^1$ penalty of the lasso and the squared $\ell^2$ penalty of ridge regression. It keeps the variable selection of the lasso, since some coefficients are set exactly to zero. The ridge part makes the problem strictly convex. It also lets groups of strongly correlated regressors enter the model together, where the lasso tends to pick one of them. It was proposed by Hui Zou and Trevor Hastie.