[pymvpa] Unbalanced Datasets

[Bill Broderick]

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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