I have worked on project where we evaluated transfer learning of several CNNs for medical dataset. We have used 5-fold cross validation and now are about to report results. I have taken the relevant metrics (F1 and Kappa) and averaged them across the folds for each model getting average numbers.
I have seen quite a few papers with confidence interval estimation in classic ML, but not so much in the area of Deep Learning. If I understood correctly, calculating CI require a lot of repetitions and could be unsuitable for Deep Learning. I am personally using dataset with over 10k images where one fold of 400 epochs takes approx. 16 hours. I think the 5 folds I created aren't significant and can't be used for CI creation, right?
If so, what should one report with F1 and Kappa to give better idea about the results? Std?