diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index d0c342b450202..c642568b73f20 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -238,17 +238,22 @@ class RandomForestRegressionModel private[ml] (
     if ($(predictionCol).nonEmpty || $(leafCol).nonEmpty) {
       var predColNames = Seq.empty[String]
       var predCols = Seq.empty[Column]
-      val bcModel = dataset.sparkSession.sparkContext.broadcast(this)
+      val bcRootNodes = dataset.sparkSession.sparkContext.broadcast(_trees.map(_.rootNode))
 
       if ($(predictionCol).nonEmpty) {
-        val predUDF = udf { features: Vector => bcModel.value.predict(features) }
+        val predUDF = udf { features: Vector =>
+          val rootNodes = bcRootNodes.value
+          TreeEnsembleModel.predictRaw(features, rootNodes) / rootNodes.length
+        }
         predColNames :+= $(predictionCol)
         predCols :+= predUDF(col($(featuresCol)))
           .as($(predictionCol), outputSchema($(predictionCol)).metadata)
       }
 
       if ($(leafCol).nonEmpty) {
-        val leafUDF = udf { features: Vector => bcModel.value.predictLeaf(features) }
+        val leafUDF = udf { features: Vector =>
+          TreeEnsembleModel.predictLeaf(features, bcRootNodes.value)
+        }
         predColNames :+= $(leafCol)
         predCols :+= leafUDF(col($(featuresCol)))
           .as($(leafCol), outputSchema($(leafCol)).metadata)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
index f970726760b26..b750411d5edf0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
@@ -207,6 +207,16 @@ private[ml] object TreeEnsembleModel {
     prediction
   }
 
+  private[ml] def predictRaw(features: Vector, rootNodes: Array[Node]): Double = {
+    var prediction = 0.0
+    var i = 0
+    while (i < rootNodes.length) {
+      prediction += rootNodes(i).predictImpl(features).prediction
+      i += 1
+    }
+    prediction
+  }
+
   private[ml] def predictLeaf(features: Vector, rootNodes: Array[Node]): Vector = {
     val indices = Array.ofDim[Double](rootNodes.length)
     var i = 0