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diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTrees.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTrees.scala
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+/*
+ * 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.
+ */
+
+package org.apache.spark.examples.mllib
+
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.tree.GradientBoosting
+import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo}
+import org.apache.spark.util.Utils
+
+/**
+ * An example runner for Gradient Boosting using decision trees as weak learners. Run with
+ * {{{
+ * ./bin/run-example org.apache.spark.examples.mllib.GradientBoostedTrees [options]
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ *
+ * Note: This script treats all features as real-valued (not categorical).
+ *       To include categorical features, modify categoricalFeaturesInfo.
+ */
+object GradientBoostedTrees {
+
+  case class Params(
+      input: String = null,
+      testInput: String = "",
+      dataFormat: String = "libsvm",
+      algo: String = "Classification",
+      maxDepth: Int = 5,
+      numIterations: Int = 10,
+      fracTest: Double = 0.2) extends AbstractParams[Params]
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("GradientBoostedTrees") {
+      head("GradientBoostedTrees: an example decision tree app.")
+      opt[String]("algo")
+        .text(s"algorithm (${Algo.values.mkString(",")}), default: ${defaultParams.algo}")
+        .action((x, c) => c.copy(algo = x))
+      opt[Int]("maxDepth")
+        .text(s"max depth of the tree, default: ${defaultParams.maxDepth}")
+        .action((x, c) => c.copy(maxDepth = x))
+      opt[Int]("numIterations")
+        .text(s"number of iterations of boosting," + s" default: ${defaultParams.numIterations}")
+        .action((x, c) => c.copy(numIterations = x))
+      opt[Double]("fracTest")
+        .text(s"fraction of data to hold out for testing.  If given option testInput, " +
+          s"this option is ignored. default: ${defaultParams.fracTest}")
+        .action((x, c) => c.copy(fracTest = x))
+      opt[String]("testInput")
+        .text(s"input path to test dataset.  If given, option fracTest is ignored." +
+          s" default: ${defaultParams.testInput}")
+        .action((x, c) => c.copy(testInput = x))
+      opt[String]("<dataFormat>")
+        .text("data format: libsvm (default), dense (deprecated in Spark v1.1)")
+        .action((x, c) => c.copy(dataFormat = x))
+      arg[String]("<input>")
+        .text("input path to labeled examples")
+        .required()
+        .action((x, c) => c.copy(input = x))
+      checkConfig { params =>
+        if (params.fracTest < 0 || params.fracTest > 1) {
+          failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1].")
+        } else {
+          success
+        }
+      }
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    }.getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+
+    val conf = new SparkConf().setAppName(s"GradientBoostedTrees with $params")
+    val sc = new SparkContext(conf)
+
+    println(s"GradientBoostedTrees with parameters:\n$params")
+
+    // Load training and test data and cache it.
+    val (training, test, numClasses) = DecisionTreeRunner.loadDatasets(sc, params.input,
+      params.dataFormat, params.testInput, Algo.withName(params.algo), params.fracTest)
+
+    val boostingStrategy = BoostingStrategy.defaultParams(params.algo)
+    boostingStrategy.numClassesForClassification = numClasses
+    boostingStrategy.numIterations = params.numIterations
+    boostingStrategy.weakLearnerParams.maxDepth = params.maxDepth
+
+    val randomSeed = Utils.random.nextInt()
+    if (params.algo == "Classification") {
+      val startTime = System.nanoTime()
+      val model = GradientBoosting.trainClassifier(training, boostingStrategy)
+      val elapsedTime = (System.nanoTime() - startTime) / 1e9
+      println(s"Training time: $elapsedTime seconds")
+      if (model.totalNumNodes < 30) {
+        println(model.toDebugString) // Print full model.
+      } else {
+        println(model) // Print model summary.
+      }
+      val trainAccuracy =
+        new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
+          .precision
+      println(s"Train accuracy = $trainAccuracy")
+      val testAccuracy =
+        new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
+      println(s"Test accuracy = $testAccuracy")
+    } else if (params.algo == "Regression") {
+      val startTime = System.nanoTime()
+      val model = GradientBoosting.trainRegressor(training, boostingStrategy)
+      val elapsedTime = (System.nanoTime() - startTime) / 1e9
+      println(s"Training time: $elapsedTime seconds")
+      if (model.totalNumNodes < 30) {
+        println(model.toDebugString) // Print full model.
+      } else {
+        println(model) // Print model summary.
+      }
+      val trainMSE = DecisionTreeRunner.meanSquaredError(model, training)
+      println(s"Train mean squared error = $trainMSE")
+      val testMSE = DecisionTreeRunner.meanSquaredError(model, test)
+      println(s"Test mean squared error = $testMSE")
+    }
+
+    sc.stop()
+  }
+}