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Danilo Ascione commented on SPARK-14409: ---------------------------------------- [~srowen] [~mlnick] Also about the top-k problem ("You are comparing the top-k items as predicted by the model to the top-k items as defined by the label."). My solution is different in this: it evaluates each label (from the pair user-item) against the top-k items as predicted by the model (for each user). Does this makes sense to you? > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org