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https://issues.apache.org/jira/browse/SPARK-18844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16364175#comment-16364175
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Apache Spark commented on SPARK-18844:
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User 'sandecho' has created a pull request for this issue:
https://github.com/apache/spark/pull/20609
> Add more binary classification metrics to BinaryClassificationMetrics
> ---------------------------------------------------------------------
>
> Key: SPARK-18844
> URL: https://issues.apache.org/jira/browse/SPARK-18844
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.0.2
> Reporter: Zak Patterson
> Priority: Minor
> Labels: evaluation
> Original Estimate: 5h
> Remaining Estimate: 5h
>
> BinaryClassificationMetrics only implements Precision (positive predictive
> value) and recall (true positive rate). It should implement more
> comprehensive metrics.
> Moreover, the instance variables storing computed counts are marked private,
> and there are no accessors for them. So if one desired to add this
> functionality, one would have to duplicate this calculation, which is not
> trivial:
> https://github.com/apache/spark/blob/v2.0.2/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala#L144
> Currently Implemented Metrics
> ---
> * Precision (PPV): `precisionByThreshold`
> * Recall (Sensitivity, true positive rate): `recallByThreshold`
> Desired additional metrics
> ---
> * False omission rate: `forByThreshold`
> * False discovery rate: `fdrByThreshold`
> * Negative predictive value: `npvByThreshold`
> * False negative rate: `fnrByThreshold`
> * True negative rate (Specificity): `specificityByThreshold`
> * False positive rate: `fprByThreshold`
> Alternatives
> ---
> The `createCurve` method is marked private. If it were marked public, and the
> trait BinaryClassificationMetricComputer were also marked public, then it
> would be easy to define new computers to get whatever the user wanted.
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