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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-10097) ML Evaluator should indicate if metric should be maximized or minimized
Date Wed, 19 Aug 2015 18:35:46 GMT

     [ https://issues.apache.org/jira/browse/SPARK-10097?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley resolved SPARK-10097.
---------------------------------------
       Resolution: Fixed
    Fix Version/s: 1.5.0

Issue resolved by pull request 8290
[https://github.com/apache/spark/pull/8290]

> ML Evaluator should indicate if metric should be maximized or minimized
> -----------------------------------------------------------------------
>
>                 Key: SPARK-10097
>                 URL: https://issues.apache.org/jira/browse/SPARK-10097
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: Joseph K. Bradley
>            Assignee: Feynman Liang
>             Fix For: 1.5.0
>
>
> ML Evaluator currently requires that metrics be maximized (bigger is better).  That is
counterintuitive for some metrics.  Currently, we hackily negate some metrics in RegressionEvaluator,
which is weird.  Instead, we should:
> * Return the metric as expected (e.g., "rmse" should return RMSE, not its negation).
> * Provide an indicator of whether the metric should be maximized or minimized.
> Model selection algorithms can use the indicator as needed.



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