[pymvpa] Use searchlight algorithm only for predictions.
robbenson18 at gmail.com
Thu Jan 31 14:53:11 UTC 2013
Thank you guys,
Yes, I know that I can't predict using a portion of voxel. Let's say that I
would like to train on full brain and test on a portion, putting out of ROI
voxel intensity to zero.
I don't know if it makes sense conceptually because I would like to predict
using a portion of features on a model built on multiple features.
Probably could be an sensitivity measure, e.g. building a classifier to
predict from flag the country for example Liberarian flag and stars and
stripes, If I use features from stripes part of the flag (common in both of
the flags) the classifier isn't able to classify - well, using a feature
selection probably those features were discarded - but using "stars" part
as ROI the classifier identifies the flag, and so I will know where the
classifier is more sensitive! (Hope I explained it clear).
I don't know if there is still a theoretical problem.
PS: If I could help you to complete...
On 31 January 2013 15:38, Yaroslav Halchenko <debian at onerussian.com> wrote:
> On Thu, 31 Jan 2013, Michael Hanke wrote:
> > As the subject "clearly" says, I would like to train a classifier
> > classical methods for example using all voxels and then try to
> > using only a portion of ROI like a searchlight.
> > I've tried to do this using the classifier as data measure in
> > searchlight class, but obviously the features of the classifier are
> > than those used in the searchlight.
> > How can I do?
> > This is less of a technical question, but more of a conceptual one.
> > can't train an algorithm on one set of features and then run it on a
> > different one with a different number of features.
> > you need to have equally structured input in both training and testing
> > stage. This could be done (think e.g. PCA projection), but whether it
> > makes sense in you context is impossible to tell at this point.
> indeed! But I guess it could be stretched to become a "technically
> legit" one in the case of kernel-based classifiers, where optimization
> and decision is done based on values within the kernel... theoretically
> it should be possible to get the solution for one kernel (estimated on
> full data) and then apply to another (estimated on subset of the
> features)... not sure how legit it would be, but at least possible
> technically -- I guess could become an improved "sensitivity"
> measure to complement existing ones ;-)
> Yaroslav O. Halchenko
> Postdoctoral Fellow, Department of Psychological and Brain Sciences
> 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
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
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