zhengruifeng commented on a change in pull request #25383: [SPARK-13677][ML]
Implement Tree-Based Feature Transformation for ML
URL: https://github.com/apache/spark/pull/25383#discussion_r313696881
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File path: mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
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@@ -455,7 +508,19 @@ private[ml] object GBTClassifierParams {
Array("logistic").map(_.toLowerCase(Locale.ROOT))
}
-private[ml] trait GBTClassifierParams extends GBTParams with
HasVarianceImpurity {
+private[ml] trait GBTClassifierParams extends GBTParams with
HasVarianceImpurity
+ with ProbabilisticClassifierParams {
+
+ override protected def validateAndTransformSchema(
Review comment:
@mgaido91 I tend to keep current way, that is because the superclasses are
different:
1,the `super.validateAndTransformSchema(schema, fitting, featuresDataType)`
in `GBTClassifierParams` & `RandomForestClassifierParams` are from
`ProbabilisticClassifierParams`, which check cols
probabilityCol,rawPredictionCol,featuresCol,labelCol,weightCol,predictionCol
2,while the super method called in `RandomForestRegressorParams` &
`GBTRegressorParams` are from `PredictorParams`, which only check cols
featuresCol,labelCol,weightCol,predictionCol
We can add another two trait for classification and regression,
respectively. Like `TreeEnsembleClassifierParams` &
`TreeEnsembleRegressorParams`.
However, I think this maybe not worthwhile, since there will be only two
subclasses for each, and this will make the hierarchy more complex.
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