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
 
 

 ##########
 File path: mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
 ##########
 @@ -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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