[jira] [Commented] (SPARK-18844) Add more binary classification metrics to BinaryClassificationMetrics

["Sandeep Kumar Choudhary (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-18844?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16310776#comment-16310776 ] 

Sandeep Kumar Choudhary commented on SPARK-18844:
-------------------------------------------------

I am working on this JIRA.

> 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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