What is the right way to pick the best model when you are running multiple models?
I should do variable selection first to find the best subset of variables in each model and then use some model comparison criteria
I should use a model comparison criteria first and then do variable selection just in the best one
I'm asking this, because if the second option is valid I would save a little work. However, I do not know if it is possible to guarantee based on the full model that one particular model will be better than the others after selecting variables.