sorry about the delay. nothing strikes my mind as obvious reason... also we don't know anything about data preprocessing/nature (are there inherent groups etc)
blind guess that it could also be related to leave-1-out... what if you group samples (randomly if you like) into 4 groups (chunks) and then do cross-validation -- does bias persist? > > I have 140 structural images: 78 are in class A and 62 are in class B. To > > ensure that the training algorithm (LinearNuSVMC) doesn't build a biased > > model, I am using the nperlabel='equal' option in my splitter. I know this > > part of my code is working (see below), so I'm confused why my CVs > > (leave-one-scan-out) are biased with random data (e.g., 55.71%). Can > > someone please clarify why I'm not getting 50% with random data? I suspect > > I'm just not understanding something simple... > > Thanks! > > David -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
