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diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala
new file mode 100644
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+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostingRegressionExample.scala
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.{SparkContext, SparkConf}
+// $example on$
+import org.apache.spark.mllib.tree.GradientBoostedTrees
+import org.apache.spark.mllib.tree.configuration.BoostingStrategy
+import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
+import org.apache.spark.mllib.util.MLUtils
+// $example off$
+
+object GradientBoostingRegressionExample {
+  def main(args: Array[String]): Unit = {
+    val conf = new SparkConf().setAppName("GradientBoostedTreesRegressionExample")
+    val sc = new SparkContext(conf)
+    // $example on$
+    // Load and parse the data file.
+    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+    // Split the data into training and test sets (30% held out for testing)
+    val splits = data.randomSplit(Array(0.7, 0.3))
+    val (trainingData, testData) = (splits(0), splits(1))
+
+    // Train a GradientBoostedTrees model.
+    // The defaultParams for Regression use SquaredError by default.
+    val boostingStrategy = BoostingStrategy.defaultParams("Regression")
+    boostingStrategy.numIterations = 3 // Note: Use more iterations in practice.
+    boostingStrategy.treeStrategy.maxDepth = 5
+    // Empty categoricalFeaturesInfo indicates all features are continuous.
+    boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()
+
+    val model = GradientBoostedTrees.train(trainingData, boostingStrategy)
+
+    // Evaluate model on test instances and compute test error
+    val labelsAndPredictions = testData.map { point =>
+      val prediction = model.predict(point.features)
+      (point.label, prediction)
+    }
+    val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
+    println("Test Mean Squared Error = " + testMSE)
+    println("Learned regression GBT model:\n" + model.toDebugString)
+
+    // Save and load model
+    model.save(sc, "target/tmp/myGradientBoostingRegressionModel")
+    val sameModel = GradientBoostedTreesModel.load(sc,
+      "target/tmp/myGradientBoostingRegressionModel")
+    // $example off$
+  }
+}
+// scalastyle:on println