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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-14409) Investigate adding a RankingEvaluator to ML
Date Fri, 24 Feb 2017 07:51:44 GMT

    [ https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15882163#comment-15882163
] 

Nick Pentreath commented on SPARK-14409:
----------------------------------------

[~roberto.mirizzi] the {{goodThreshold}} param seems pretty reasonable in this context to
exclude irrelevant items. I think it can be a good {{expertParam}} addition.

Ok, I think that a first pass at this should just aim to replicate what we have exposed in
{{mllib}} and wrap {{RankingMetrics}}. Initially we can look at: (a) supporting numeric columns
and doing the windowing & {{collect_list}} approach to feed into {{RankingMetrics}}; (b)
support Array columns and feed directly into {{RankingMetrics}} or (c) support both.

[~yongtang] already did a PR here: https://github.com/apache/spark/pull/12461. It is fairly
complete and also includes MRR. [~yongtang] are you able to work on reviving that PR? If os,
[~roberto.mirizzi] [~danilo.ascione] are you able to help review that PR?

> 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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