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https://issues.apache.org/jira/browse/SPARK-13497?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-13497.
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Resolution: Incomplete
> 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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