We'll begin the modeling process of developing a classification algorithm to predict y. We'll conduct, in sequence, ridge regression, LASSO, and elastic net models, evaluating their performance as we go using the area under the curve and log-loss.
Modeling and evaluation
Ridge regression
The package we're using will be glmnet. I like it because it has a built-in cross-validation function, standardizes the input features, and returns coefficients on their original scale, so it's quite easy to implement. If you standardize your features yourself, you can specify standardize = FALSE in the function. Either way, don't run features that aren't standardized as the results will be undesirable as the...
