Hi all, I recently asked a question on dealing with unbalanced datasets and here's a follow-up question. So let's say I have empty runs, or runs where there are zero samples for one of the conditions. This leads to problems if that run happens to be the test run on a leave-one-run-out cross-validation procedure.
My workaround for that was this: if I had one of such runs with empty conditions, then I would set NFoldPartitioner(cvtype=2), together with Balancer() so that any combination of two runs would have at least one sample per condition. But if I had two of such runs with empty conditions, then I would set cvtype=3, and so on. However this means I have less data for the training set on each classification fold. Is there any other possible solution for this? In fact, is it possible to do leave-n-samples-out classification: So on each fold I randomly select n samples per condition to test on, and use the remaining samples (after balancing) for training, disregarding the chunks structure? Thanks! -Edmund
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