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https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15240607#comment-15240607
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Yong Tang commented on SPARK-14409:
-----------------------------------
Thanks [~mlnick] [~josephkb]. Yes I think wrapping RankingMetrics could be the
first step and reimplementing all RankingEvaluator methods in ML using
DataFrames would be good after that. I will work on the reimplementation in
several followup PRs.
> Investigate adding a RankingEvaluator to ML
> -------------------------------------------
>
> Key: SPARK-14409
> URL: https://issues.apache.org/jira/browse/SPARK-14409
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Nick Pentreath
> Priority: Minor
>
> {{mllib.evaluation}} contains a {{RankingMetrics}} class, while there is no
> {{RankingEvaluator}} in {{ml.evaluation}}. Such an evaluator can be useful
> for recommendation evaluation (and can be useful in other settings
> potentially).
> Should be thought about in conjunction with adding the "recommendAll" methods
> in SPARK-13857, so that top-k ranking metrics can be used in cross-validators.
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