I am trying to do variable selection using elastic net (Matlab
Lasso function with alpha of 0.5). I have 75 predictors in total (some are correlated with each other, hence using elastic net instead of lasso), and I would like to get a subset of them, which are good predictors for my outcome.
So my question is: How can I calculate something like $R^2$ that show how much of my outcome is explained by these selected variables?
If I use the selected variables in a multiple linear regression model, is the $R^2$ gonna be valid, since my variables are correlated?
Can I calculate cross-validated $R^2$ (using leave-one-out) to get a more accurate $R^2$?
Is there any other way than calculating $R^2$ that I show my variable selection method predicts well?