In contrast, LASSO uses an L1 penalty as a variable selection method to select a sparse set of (uncorrelated) predictors18 while the elastic net linearly combines the L1 and L2 penalties of LASSO and ridge regression respectively to perform variable selection19. We used the R package glmnet to implement LASSO and elastic net with α=0.5.
