'Is k-folds cross validation a smarter idea than using a validation set instead?
I have a somewhat large (~2000) set of medical images I plan to use to train a CV model (using efficentnet architecture) in my workplace. In preparation for this, I was reading up on some good practices for training medical images. I have split the dataset by patients to prevent leakages and split my data in train:test:val in the order of 60:20:20. However, I read that k-folds cross validation was a newer practice then using a validation set, but I was recommended away from doing so as k-folds is supposed to be far more complicated. What would you recommend in this instance, and are there any other good practices to adopt?
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