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https://issues.apache.org/jira/browse/SPARK-16235?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15353979#comment-15353979
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Mahmoud Rawas commented on SPARK-16235:
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I don't fully agree with the statement 'it has no clear meaning' as
mathematically it still has a quite acceptable representation for the error
occurred on the predicted value, please check a quick graph created here
(https://docs.google.com/spreadsheets/d/1VWr0-IO4KZkPwLdzji9gCd-yvRuKbZ3Hc5xnPfWnhsE/edit?usp=sharing)
And returning to the log-loss that is implemented in spark, I would agree with
the fact that it will only work within the range of [-1,1], but as a developer
I would prefer to do the values transformation inside the Loss Calculator so
the mapping from [0,1] into [-1,1] needs to happen in side LogLoss class, and
by this way both measure will be satisfied, and then user will have to decide
which measure to use.
Your Thoughts?
> "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
>
> 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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