I have data for 200 subjects and am creating a neural network to classify (binary state) for each subject. I am doing so in Matlab and have to divide up my data into training, validation, and testing sets. Originally, I created a 6-fold cross-validation method wherein I tested on a different set of 33 subjects, repeated 6 times and the accuracies averaged.
I am unsure if this is the best way to go about cross-validation or divvying up my data.
Other things I have tried include a simple 50% training, 15% validation, and 35% testing split that I repeat many times. Since I cannot guarantee that the same 35% (albeit unlikely) is selected each time, I went with the 6-fold method described above.
Now, I am trying to train/validate on all subjects aside from one, and then repeating that process 200 times for each subject.
Any ideas on what is the best/common way of dividing my data?