For a dataset that is not too large, I am trying a couple of models for prediction. I get the following train and test MSE for them:
- Model 1: Train MSE = 100, Test MSE = 104
- Model 2: Train MSE = 30, Test MSE = 65
Now the second model has obviously a smaller test error. Should I just forget about the overfitting and variability in it and choose model 2? Are there any other considerations that I should take into account?
Another thing that I was wondering: if I use cross validation can I just compare “cross validation test score means” and not care about the train and test score difference?