71621ee79dee937b5a70dc40588342f5a54b60fb,mvpa2/featsel/rfe.py,RFELearner,_train,#RFELearner#Any#,363

Before Change

        // Now perform 2nd RFE -- until optimal_n
        // so we should provide an alternative stopping criterion
        self._mclf = FeatureSelectionClassifier(
            self.clf,
            RFE(sens_ana,
                None, // we do not have anything to transfer to
                Repeater(2),
                fselector=self.fselector,
                train_pmeasure=False,
                stopping_criterion=None,
                nfeatures_min=nfeatures_min,
                update_sensitivity=True,
                enable_ca=["nfeatures"]))
        self._mclf.train(dataset)

    def _untrain(self):
        super(RFELearner, self)._untrain()
        if self._mclf is not None:

After Change

            debug("RFEC", "Stage 2: running RFE on full training dataset to "
                  "distil best %d features" % nfeatures_min)

        super(SplitRFE, self)._train(dataset)


    def _untrain(self):

Instances