[jira] [Issue Comment Deleted] (SPARK-10097) ML Evaluator should indicate if metric should be maximized or minimized

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

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

Joseph K. Bradley updated SPARK-10097:
--------------------------------------
    Comment: was deleted

(was: I'm working on this.)

> 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
>
> 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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