[jira] [Updated] (SPARK-13497) PMML export for logistic regression does not conform to the PMML standard

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

     [ https://issues.apache.org/jira/browse/SPARK-13497?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-13497:
---------------------------------
    Labels: bulk-closed  (was: )

> PMML export for logistic regression does not conform to the PMML standard
> -------------------------------------------------------------------------
>
>                 Key: SPARK-13497
>                 URL: https://issues.apache.org/jira/browse/SPARK-13497
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.6.0
>            Reporter: Chris Papadopoulos
>            Priority: Minor
>              Labels: bulk-closed
>
> In line 52 of spark/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala
> the binary classification for n=2 is exported with RegressionNormalizationMethodType.LOGIT
> But, the PMML standard specifies that it should be a softmax for linear regression: 
> http://dmg.org/pmml/v4-2-1/Regression.html
> Quote: 
> " Note that Binary logistic regression is a special case with
> y = intercept + Sumi (coefficienti * independent variablei )
> p = 1/(1+exp(-y))
> It should be implemented as a classification model
> <RegressionModel functionName="classification"  normalizationMethod="softmax" ...
>   <RegressionTable targetCategory="YES" ...
>   <RegressionTable targetCategory="NO" intercept="0.0"
> "
> Evaluating with the logit option leads to unexpected behavior.



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