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

["Mahmoud Rawas (JIRA)" <ji...@apache.org>]

Mahmoud Rawas created SPARK-16235:
-------------------------------------

             Summary: "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: 1.6.2, 1.6.1, 2.0.0
            Reporter: Mahmoud Rawas


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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