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. Thank you R PS: If I could help you to complete... On 31 January 2013 15:38, Yaroslav Halchenko <[email protected]> wrote: > > 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 > 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 >
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