8000 Would it make sense to add False Positive Rate as a metric? · Issue #10391 · scikit-learn/scikit-learn · GitHub
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Would it make sense to add False Positive Rate as a metric? #10391

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sandeepvaday opened this issue Jan 1, 2018 · 5 comments
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Would it make sense to add False Positive Rate as a metric? #10391

sandeepvaday opened this issue Jan 1, 2018 · 5 comments
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@sandeepvaday
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If that makes sense, I can create a pull.

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@jnothman
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jnothman commented Jan 1, 2018 via email

@sandeepvaday
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Sorry if I got this wrong but I thought introducing a metric simply means adding a scorer in sklearn/metrics/classification.py.

To your comment about specificity, adding such a (specificity) scorer would be fine too. As long as computing FPR is straight forward. In the context of network based anomaly detection, use of FPR is very relevant/common.

Please let me know how we can move forward on this.

@jnothman
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jnothman commented Jan 2, 2018 via email

@sandeepvaday sandeepvaday mentioned this issue Mar 18, 2018
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@syadavns
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Sorry for taking this long. Finally got around to adding the specificity score.

Cheers!

@cmarmo cmarmo added Needs Decision - Close Requires decision for closing New Feature labels Dec 23, 2021
@thomasjpfan
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Closing because this is a duplicate of #5516.

There is also a PR for adding specificity here: #19556

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@jnothman @cmarmo @sandeepvaday @thomasjpfan @syadavns
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