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https://issues.apache.org/jira/browse/SPARK-24875?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16551457#comment-16551457
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Liang-Chi Hsieh commented on SPARK-24875:
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hmm, I think for calculation of precision, recall and true/false positive rate,
we should only care about exact calculation but approximate one. Thus is it
reasonable to use countByValueApprox here?
> MulticlassMetrics should offer a more efficient way to compute count by label
> -----------------------------------------------------------------------------
>
> Key: SPARK-24875
> URL: https://issues.apache.org/jira/browse/SPARK-24875
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.3.1
> Reporter: Antoine Galataud
> Priority: Minor
>
> Currently _MulticlassMetrics_ calls _countByValue_() to get count by
> class/label
> {code:java}
> private lazy val labelCountByClass: Map[Double, Long] =
> predictionAndLabels.values.countByValue()
> {code}
> If input _RDD[(Double, Double)]_ is huge (which can be the case with a large
> test dataset), it will lead to poor execution performance.
> One option could be to allow using _countByValueApprox_ (could require adding
> an extra configuration param for MulticlassMetrics).
> Note: since there is no equivalent of _MulticlassMetrics_ in new ML library,
> I don't know how this could be ported there.
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