Methods for augmenting binary datasets I have a small (~100 samples) dataset with roughly 20 features which are mostly binary, and a few are numeric (~5). I wanted to use methods for augmenting the training set and see if I can get better test accuracy. What methods/code can I use for augmenting binary datasets?
You may try Metropolis–Hastings sampling if multi-dimensional, or else Adaptive rejection, unless you've already tried.