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