Elastic Net Vs Lasso. In statistics and machine learning lasso is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model. Number between 0 and 1 passed to elastic net scaling between l1 and l2 penalties.
The advantage of that it does not easily eliminate the high collinearity coefficient. Sometimes the lasso regression can cause a small bias in the model where the prediction is too dependent upon a particular variable. The elastic net penalty mixes these two.
Use elastic net when you have several highly correlated variables.
For other values of α the penalty term P α β interpolates between the L 1 norm of β and the squared L 2 norm of β. Lasso 306 031 387 038 650 282 466 396 Elastic Net 251 029 316 027 566 175 345 164 No re-scaling 570 041 273 023 410 213 459 372 Variable selection results Method Ex1 Ex2 Ex3 Ex4 Lasso 5 6 24 11 Elastic Net 6 7 27 16. Elastic Net reduces the impact of different features while not eliminating. The elastic net penalty mixes these two.
