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I'm using Tensorflow for an auto-tagging task on audio clips. The problem is actually a multilabel classification problem meaning that each clip can have multiple tags at the same time.

Regarding the model evaluation, I would like to be able to compute the AUC per clip and per tag. My problem is that AUC is basically a metric for binary classification and I don't know how it can be extended to a multilabel problem.

I have found many references of AUC computation on multilabel classification tasks, but none of these explain how to do it.

(An example)

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  • $\begingroup$ How have found any solution for this yet? $\endgroup$ May 30, 2018 at 11:37

1 Answer 1

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AUC (Area under the ROC Curve) can not be calculated for multilabel classification directly.

Multilabel classification metrics have to be converted to a binary problem. The two most common conversions are:

  • OvO - One vs One
  • OvR - One vs Rest

sklearn.metrics.roc_auc_score discusses this in detail.

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