[pymvpa] Sensitivity analysis

[Dai Xijian]

Dear all,
    I faced with an embarrassing situation. I have completed some sensitivity analysis. When I used the script like below, I achieved a discouraging accuracy rate:0.51267.
         clf = LinearCSVMC()
           cv = CrossValidation(clf, NFoldPartitioner(), errorfx = lambda p,t:np.mean(p==t), enable_ca=['stats'],postproc = mean_sample())
           sensana = clf.get_sensitivity_analyzer()
           cv_sensana = RepeatedMeasure(sensana, ChainNode((NFoldPartitioner(), Splitter('partitions', attr_values=(1,)))))
           acc = cv(ds)
           sensmap_cv = cv_sensana(ds)
   Then I want to improve the accuracy. So I firstly select 20% features which contributed most for the predicted accuracy. However, I achieved a lower accuracy rate:0.40268.
       fsel = SensitivityBasedFeatureSelection(OneWayAnova(),FractionTailSelector(0.2,mode='select',tail='upper'))
         fclf = FeatureSelectionClassifier(clf, fsel)
         cv_sel = CrossValidation(fclf,NFoldPartitioner(), errorfx = lambda p,t:np.mean(p==t),enable_ca=['stats'],postproc = mean_sample())
         sensana_sel = fclf.get_sensitivity_analyzer()
         cv_sensana_sel = RepeatedMeasure(sensana_sel, ChainNode((NFoldPartitioner(), Splitter('partitions', attr_values=(1,)))))
         acc_sel = cv_sel(ds)
         sensmap_cv_sel = cv_sensana_sel(ds)
  What's wrong? Would you tell me?



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