I wonder which type of model cross-validation to choose for classification problem: K-fold or random sub-sampling (bootstrap sampling)?
My best guess is to use 2/3 of the data set (which is ~1000 items) for training and 1/3 for validation.
In this case K-fold gives only three iterations(folds), which is not enough to see stable average error.
On the other hand I don't like random sub-sampling feature: that some items won't be ever selected for training/validation, and some will be used more than once.
Classification algorithms used: random forest & logistic regression.