Abstract
We propose the elastic net, a new regularization and variable selection method. Real
world data and a simulation study show that the elastic net often outperforms the lasso, while
enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping
effect, where strongly correlated predictors tend to be in or out of the model together.The elastic
net is particularly useful when the number of predictors (p) is much bigger than the number of
observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the
pn case. An algorithm called LARS-EN is proposed for computing elastic net regularization
paths efficiently, much like algorithm LARS does for the lasso
