diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 568cdd11a12a..b6b02e77909b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -210,6 +210,9 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
     outputData.toDF
   }
 
+  final override def transformImpl(dataset: Dataset[_]): DataFrame =
+    throw new UnsupportedOperationException(s"transformImpl is not supported in $getClass")
+
   /**
    * Predict label for the given features.
    * This method is used to implement `transform()` and output [[predictionCol]].
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index a5ed4a38a886..5e0c66b3ab41 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -34,7 +34,7 @@ import org.apache.spark.ml.util.DefaultParamsReader.Metadata
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.{Dataset, Row}
 import org.apache.spark.sql.functions._
 
 /**
@@ -286,14 +286,6 @@ class GBTClassificationModel private[ml](
   @Since("1.4.0")
   override def treeWeights: Array[Double] = _treeWeights
 
-  override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
-    val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
-    }
-    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
-  }
-
   override def predict(features: Vector): Double = {
     // If thresholds defined, use predictRaw to get probabilities, otherwise use optimization
     if (isDefined(thresholds)) {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 3500f2ad52a5..4424319a4c63 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -25,7 +25,7 @@ import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree._
-import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{TreeClassifierParams, TreeEnsembleModel}
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
@@ -34,8 +34,7 @@ import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset}
-import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.Dataset
 
 /**
  * <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> learning algorithm for
@@ -208,14 +207,6 @@ class RandomForestClassificationModel private[ml] (
   @Since("1.4.0")
   override def treeWeights: Array[Double] = _treeWeights
 
-  override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
-    val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
-    val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
-    }
-    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
-  }
-
   override protected def predictRaw(features: Vector): Vector = {
     // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128
     // Classifies using majority votes.