No problem! I've only used the random majority under-sampling with replacement so far, but it has a whole lot of options.
On Thu, Nov 19, 2015 at 10:01 AM, Yaroslav Halchenko <[email protected]> wrote: > > On Thu, 19 Nov 2015, Bill Broderick wrote: > > > I ran into a similar issue with unbalanced classification and wanted > > to look at the individual partitions as well. I couldn't figure > > out how to do so just in PyMVPA, so I ended up using a separate Python > module, > > UnbalanceDataset: https://github.com/fmfn/UnbalancedDataset. With > > that, I sub-sampled the more common group to balance the two groups, > > which created a new dataset. I was then able to investigate what was > > going on in that dataset and what each of the partitions look like as > > if it were a regular dataset. > > That is a sweet little toolbox -- thanks for sharing! > > -- > Yaroslav O. Halchenko > Center for Open Neuroscience http://centerforopenneuroscience.org > Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 > Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 > WWW: http://www.linkedin.com/in/yarik > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
