<html><body><div style="color:#000; background-color:#fff; font-family:times new roman, new york, times, serif;font-size:12pt"><div>Hi all,</div><div><br></div><div>I would like to do the following cross-validation procedure in pymvpa. Here is my toy example: Say I have 3 runs in a block-design experiment. I have two conditions, A and B, and in each run I have 3 blocks of each condition. E.g.:</div><div><br></div><div>Run 1: A A B A B B</div><div>Run 2: A A A B B B</div><div>Run 3: A A B B A B</div><div><br></div><div>I would like to do a leave-one-out classification, but on each fold, I would like to train on individual blocks, and test on averaged blocks in the left out run. So I feed individual blocks of 'A' and 'B' from two runs to train the classifier, but on the left out run, I average all the 'A's and 'B's, and test the classifier on each of these. So I test the classifier twice instead of 6 times on each fold.</div><div><br></div><div>How
do I do this? Is this possible by just using the CrossValidation() function? Or do I have to rewrite it...</div><div><br></div><div>Thanks!</div><div><br></div><div>-Edmund Chong</div><div><br></div><div><br></div><div><br></div><div><br></div></div></body></html>