I have a binary classification problem, let's say people can buy or not buy a certain product. Now unlike a standard prediction task, I only want to find which features are the most important for the person's decision to buy.
Which metric should I use to optimize the algorithm? Maximize out of sample accuracy like when I would be interested in making the best prediction? Or maximize fit and don't care about overfitting? A mixture of both?
I am using