[pymvpa] Use searchlight algorithm only for predictions.

[Yaroslav Halchenko]

On Thu, 31 Jan 2013, Michael Hanke wrote:
>      As the subject "clearly" says, I would like to train a classifier using
>      classical methods for example using all voxels and then try to predict
>      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 more
>      than those used in the searchlight.
>      How can I do?

>    This is less of a technical question, but more of a conceptual one. You
>    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
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