I am looking at regularized logistic regression, (l1 and l2 at the moment) and regularized discriminant analysis.
How do I compare the two? I was thinking of doing gcv on both methods over a set of values of lambdas for each of the three cases (l1, l2, and rda) then choosing the model with the least amount of error, such as AUC or some classification error. Frank Harrell's comments in this post has me confused if this is even a way of doing such a thing: How do we generate the ROC curve for Linear Discriminant Analysis method
Any help or resources would be greatly appreciated.