Can you use elastic net for ridge regression?
Yes! and no. Elastic net is a combination of two regularization techniques, the L2 regularization (used in ridge regression) and L1 regularization (used in LASSO). Lasso produces naturally sparse models, i.e. most of the variable coefficients will be shrinked to 0 and effectively excluded out of the model.
Is the elastic net a combination of two techniques?
But isn’t this what we want. I mean it saves us from the trouble of multicollinearity isn’t it. Yes! and no. Elastic net is a combination of two regularization techniques, the L2 regularization (used in ridge regression) and L1 regularization (used in LASSO).
How is elastic net an extension of linear regression?
Elastic Net is an extension of linear regression that adds regularization penalties to the loss function during training. How to evaluate an Elastic Net model and use a final model to make predictions for new data.
How are penalties used in elastic net regression?
Another popular penalty is to penalize a model based on the sum of the absolute coefficient values. This is called the L1 penalty. An L1 penalty minimizes the size of all coefficients and allows some coefficients to be minimized to the value zero, which removes the predictor from the model.
How are the least significant variables shrinked in elastic net?
So the least significant variables are shrinked away, before shrinking the others, unlike with ridge, where all variables are shrinked, while none of them are really shrinked to 0. Elastic net uses a linear combination of both these approaches. The specific case mentioned by Hastie when discussing the method was in the case of large p, small n.
When does elastic net outperform Lasso data?
It has been shown (by Hastie and others) that Elastic Net can outperform Lasso when the data is highly correlated. Lasso may just select one of the correlated variables and does not care which one is selected. This can be a problem when one wants to validate the selected variables in an independent dataset.