[GitHub] vruusmann commented on a change in pull request #23868: [SPARK-26966][ML] Update to JPMML 1.4.8

[GitBox <gi...@apache.org>]

vruusmann commented on a change in pull request #23868: [SPARK-26966][ML] Update to JPMML 1.4.8
URL: https://github.com/apache/spark/pull/23868#discussion_r259231996
 
 

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 File path: mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala
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 @@ -44,12 +44,12 @@ private[mllib] object PMMLModelExportFactory {
         new GeneralizedLinearPMMLModelExport(lasso, "lasso regression")
       case svm: SVMModel =>
         new BinaryClassificationPMMLModelExport(
-          svm, "linear SVM", RegressionNormalizationMethodType.NONE,
+          svm, "linear SVM", RegressionModel.NormalizationMethod.NONE,
 
 Review comment:
   `RegressionModel` is a top-level PMML model element for representing all "dot product"-type models; the real mining function type (regression vs classification) is specified using the `functionName` attribute. So, `RegressionModel@functionName="classification"` is how communicates that this model element is encoding a classifier-type function.
   
   Based on my experience with implementing Apache Spark ML-to-PMML converters (https://github.com/jpmml/jpmml-sparkml), then many non-decision tree based Apache Spark ML model classes (eg. `NaiveBayesClassifier`, `LinearSVC`) are based on the "dot product" business logic, and therefore reducible to the `RegressionModel` element. In other words, there is no point in using more complex PMML model elements such as `NaiveBayesModel` or `SupportVectorMachineModel`, when the simplest `RegressionModel` element will be able to capture everything.

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