I'm using $k$-fold cross validation technique for generating train, test and validation indexes for a neural network. My sample size is 230~700. What is best $k$ for cross validation here. Now I'm using 10-fold cross validation but I think it is too high. What is your idea?
Actually there is no straight answer to the choice of K in k-fold cross validation. An higher k will give you more but smaller subsets on which run testing. An adopted choice is to select the K that gives you a testing set with the size of 15% of your total dataset.
However, other methods are also available; you may want to consider permutations or exahustive cross validation methods (more infos here).
Hope it helps.