Dear all, I'm trying to combine RFE and cross-validation but not sure how to obtain the final sensitivity map. I used the example codes on the following webpage:
http://www.pymvpa.org/generated/mvpa2.featsel.rfe.RFE.html and then put the above classifier (clf = FeatureSelectionClassifier) in CrossValication: cv = CrossValidation(clf, NFoldPartitioner(), enable_ca=['confusion','stats']) Is it correct? If so, how can I obtain the final sensitivity map of the selected features? I tried the following: sen = clf.get_sensitivity_analyzer() cv_sen = RepeatedMeasure(sen, NFoldPartitioner()) error = cv(ds) sensmap_cv = cv_sen(ds) But I always got the following error message: "RuntimeError: Cannot reverse-map data since the original data shape is unknown. Either set `dshape` in the constructor, or call train()." Any help would be much appreciated! Best, Meng
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