Does elastic net perform variable selection?
Similar to the lasso, the elastic net simultaneously does automatic variable selection and continuous shrinkage, and it can select groups of correlated variables. Simulation studies and real data examples show that the elastic net often outperforms the lasso in terms of prediction accuracy.
What is variable selection in LASSO?
Lasso is a supervised algorithm wherein the process identifies the variables that are strongly associated with the response variable. This is called variable selection. Then, Lasso forces the coefficients of the variables towards zero. This is now the process of shrinkage.
How is the elastic net used in variable selection?
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…
Which is better for variable selection lasso or Lars?
By contrast, the lasso is not a very satisfactory variable selection method in the p ≫ n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso. Journal of the Royal Statistical Society.
Which is the best regularization and variable selection method?
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.
When to use variable selection in explanatory modeling?
Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p -values or confidence intervals.