Elastic Net Vs Lasso. For lasso regularization of regression ensembles see regularize. Loading required R packages tidyverse for easy data manipulation and visualization.
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. Elastic net is a related technique. The advantage of that it does not easily eliminate the high collinearity coefficient.
Number between 0 and 1 passed to elastic net scaling between l1 and l2 penalties.
Mar 04 2020 Lasso Regression adds L 1 regularization penalty term to loss function. In these cases elastic Net is proved to better it combines the regularization of both lasso and Ridge. Elastic Net produces a regression model that is penalized with both the L1-norm and L2-norm. Elastic-net is a compromise between the two that attempts to shrink and do a sparse selection simultaneously.
