[jira] [Resolved] (SPARK-16235) "evaluateEachIteration" is returning wrong results when calculated for classification model.

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-16235?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-16235.
----------------------------------
    Resolution: Incomplete

> "evaluateEachIteration" is returning wrong results when calculated for classification model.
> --------------------------------------------------------------------------------------------
>
>                 Key: SPARK-16235
>                 URL: https://issues.apache.org/jira/browse/SPARK-16235
>             Project: Spark
>          Issue Type: Bug
>    Affects Versions: 1.6.1, 1.6.2, 2.0.0
>            Reporter: Mahmoud Rawas
>            Priority: Major
>              Labels: bulk-closed
>
> Basically within the mentioned function there is a code to map the actual value which supposed to be in the range of \[0,1] into the range of \[-1,1], in order to make it compatible with the predicted value produces by a classification mode. 
> {code}
> val remappedData = algo match {
>       case Classification => data.map(x => new LabeledPoint((x.label * 2) - 1, x.features))
>       case _ => data
>     }
> {code}
> the problem with this approach is the fact that it will calculate an incorrect error for an example mse will be be 4 time larger than the actual expected mse 
> Instead we should map the predicted value into probability value in [0,1].



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