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https://issues.apache.org/jira/browse/SPARK-18844?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-18844:
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    Assignee: Apache Spark

> 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
>            Assignee: Apache Spark
>            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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