Thank you Olivier. I went through that paper and I agree, it looks like
implementing micro-AUC or macro-AUC should not be that hard. I will try to
implement within the next week. I have have never contributed to a project
in GitHub, so I am not sure to what extent my code would meet the standards
but I am happy to try.
In the mean time, is there anything similar to an AUC metric that scikit
supports when working with GridSearchCV in a multi-label setting? I am
looking for some compromise between precision and recall that indirectly
optimizes for the AUC score of each label .
Josh
On Fri, Jul 19, 2013 at 7:24 AM, Olivier Grisel <[email protected]>wrote:
> 2013/7/19 Arnaud Joly <[email protected]>:
> > You can probably average the precision recall curve
> > or use some ranking metrics [1].
> >
> > Arnaud
> >
> > [1] Mining Multi-label Data
> >
> http://lkm.fri.uni-lj.si/xaigor/slo/pedagosko/dr-ui/tsoumakas09-dmkdh.pdf
>
> This paper indeed suggests to use micro and macro averaging for all
> label binary measures (such as precision, recall, f1 and ROC-AUC). We
> already do it for precision, recall and f1. We could add micro and
> macro averaging for ROC-AUC and PR-AUC.
>
> --
> Olivier
>
>
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