<div dir="ltr">No problem! I've only used the random majority under-sampling with replacement so far, but it has a whole lot of  options.<br></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Nov 19, 2015 at 10:01 AM, Yaroslav Halchenko <span dir="ltr"><<a href="mailto:debian@onerussian.com" target="_blank">debian@onerussian.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><span class=""><br>
On Thu, 19 Nov 2015, Bill Broderick wrote:<br>
<br>
> I ran into a similar issue with unbalanced classification and wanted<br>
> to look at the individual partitions as well. I couldn't figure<br>
> out how to do so just in PyMVPA, so I ended up using a separate Python module,<br>
> UnbalanceDataset: <a href="https://github.com/fmfn/UnbalancedDataset" rel="noreferrer" target="_blank">https://github.com/fmfn/UnbalancedDataset</a>. With<br>
> that, I sub-sampled the more common group to balance the two groups,<br>
> which created a new dataset. I was then able to investigate what was<br>
> going on in that dataset and what each of the partitions look like as<br>
> if it were a regular dataset.<br>
<br>
</span>That is a sweet little toolbox -- thanks for sharing!<br>
<div class="HOEnZb"><div class="h5"><br>
--<br>
Yaroslav O. Halchenko<br>
Center for Open Neuroscience     <a href="http://centerforopenneuroscience.org" rel="noreferrer" target="_blank">http://centerforopenneuroscience.org</a><br>
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755<br>
Phone: <a href="tel:%2B1%20%28603%29%20646-9834" value="+16036469834">+1 (603) 646-9834</a>                       Fax: <a href="tel:%2B1%20%28603%29%20646-1419" value="+16036461419">+1 (603) 646-1419</a><br>
WWW:   <a href="http://www.linkedin.com/in/yarik" rel="noreferrer" target="_blank">http://www.linkedin.com/in/yarik</a><br>
<br>
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