I'm no expert, but I know this paper which proposes a generalization of the
ROC for multiclass:
[Hand 2001]: A simple generalization of the area under the ROC curve to
multiple class classification problems
HTH
Eustache
2013/7/17 Josh Wasserstein <[email protected]>
> Hi,
>
> I understand that ROC-AUC is defined for binary classification problems..
> But is there an *equivalent metric* for multi-label problems? i.e.
> something that does not pick a specific threshold (precision/recall regime)
> for each label, but that returns instead a summarizing measure of the
> overall performance (i.e. some statistic that summarizes how the
> distribution of scores across all labels correlate with the ground truth).
>
> Also, say that I want to way to run GridSearchCV to optimize my own
> metric in a multi-label problem (e.g. the average AUC score across all
> labels). Does anyone have any pointers to specific steps on how to do this?
>
> Thanks,
>
> Josh
>
>
>
>
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