If you are treating your Logistic Regression output as binary (i.e. not using predict_proba or decision_function), could you please provide the confusion matrix?
On 26 November 2015 at 05:06, Herbert Schulz <hrbrt....@gmail.com> wrote: > Hi, i think i have some "missunderstanding" due to the classification > metric in scikit-learn > > > > i have a 2 class problem it is 1.0 or 2.0 > > > precision recall f1-score support > > 1.0 0.86 0.76 0.81 254 > 2.0 0.49 0.65 0.56 91 > > avg / total 0.76 0.73 0.74 345 > > > Specificity: [ 1. * 0.35164835* 0. ] > recall,tpr,sensitivity [ 0. * 0.24015748* 1. ] > > > # this part is manually computed ( precision, sens, spec, ballanced > accuracy ) > > logistic regression 0.86,* 0.76, 0.65,* 0.7 > > > > The part with: > > Specificity: [ 1. 0.35164835 0. ] > recall,tpr,sensitivity [ 0. 0.24015748 1. ] > > are computed with > > fpr, tpr, thresholds = metrics.roc_curve(expected, predi, > pos_label=1) > print "Specificity:", 1-fpr > print "recall,tpr,sensitivity",tpr > > Why is th speceficity for 1-fpr are computed wtih [ 1. > 0.35164835 0. ] > > and not 0.65 ? > > Same with recall > > > > > > > > > > > > ------------------------------------------------------------------------------ > Go from Idea to Many App Stores Faster with Intel(R) XDK > Give your users amazing mobile app experiences with Intel(R) XDK. > Use one codebase in this all-in-one HTML5 development environment. > Design, debug & build mobile apps & 2D/3D high-impact games for multiple > OSs. > http://pubads.g.doubleclick.net/gampad/clk?id=254741551&iu=/4140 > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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