Performing k-fold cross-validation on entire imbalanced and small dataset? For my dissertation, I have a small and imbalanced dataset. It is made up of 190 cases, and the minority class (which I am most interested in detecting) is only 40 cases.
Since this is an inferential/exploratory analysis, and will not be deployed in production, is it okay to do k-fold CV, and then take the mean F1 score of each fold?

It is ok to do so if you're not doing hyper-parameter optimizations on that cross-validation loop. You can see similar evaluations in many publications, especially in biomedical research, where data is scarce. There is not much to do other than evaluating your model based on pure cross-validation. The choice of metric is an independent problem.
