Hi all, I think my first question was broader than it needed to be, so hopefully this is more to the point.
I'm trying to run MVPA on a classification with unbalanced classes, using a Linear SVM, and would like to weight the error signals to correct for unbalanced-ness. With PyMVPA's Linear CSVMC ( http://www.pymvpa.org/generated/mvpa2.clfs.svm.LinearCSVMC.html), it looks like there's a weight and weight_label parameter that would do what I would like, but I cannot find any usage examples. Can someone provide me with one? For example, if I have a dataset with three times as many examples in class A as in class B, how would I set up the Linear CSVMC to weight the error in class B as three times larger? Thanks, William
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