[pymvpa] Unbalanced Datasets

[J.A. Etzel]

I wonder if the lack of responses is because people (myself included) 
don't use weighting for fMRI datasets, but rather balance through 
subsetting and experimental design ... anyone use (or ever tried) 
weighting unbalanced datasets?

I've never tried analyzing a dataset as badly balanced (3 to 1) as your 
example; subsetting is certainly very unstable in this case. Perhaps you 
can reduce the imbalance by changing the cross-validation partitioning 
(eg leave 2 runs out instead of 1 or on the subjects)?

Jo


On 4/22/2015 12:43 PM, Bill Broderick wrote:
> 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
>
>
> _______________________________________________
> Pkg-ExpPsy-PyMVPA mailing list
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
>