Thank you Michael. Both of those seem like plausible techniques and make sense. Once again I'm surprised by the flexibility of PyMVPA :)
Thanks! Cheers Jason On Wednesday, March 26, 2014, Michael Hanke <[email protected]> wrote: > Hi, > > On Tue, Mar 25, 2014 at 12:58:27PM -0400, Jason Ozubko wrote: > > The classifiers in PyMVPA all seem to be targeted at classifying patterns > > into nominal categories. Is there anything that can be done when you > have > > a linear "category"? > > Two ideas: > > 1. Use a regression. PyMVPA handles regressions and classifiers in very > similar ways, hence in most cases you can just replace a classifier > instance with a regression. Here is an example on how to use any > regression algorithm implemented in scikit-learn within PyMVPA: > > http://www.pymvpa.org/examples/skl_regression_demo.html > > 2. Keep doing classification, but use a custom error function. So if the > "distance" from the target value within you linear "category" is > meaningful, you can assign an error function like this one: > > def eucd(targets, predictions): > return targets - predictions > > Does that make sense in your context? > > Michael > > -- > J.-Prof. Dr. Michael Hanke > Psychoinformatik Labor, Institut für Psychologie II > Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, Geb.24 > Tel.: +49(0)391-67-18481 Fax: +49(0)391-67-11947 GPG: 4096R/7FFB9E9B > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] <javascript:;> > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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