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diff --git a/examples/src/main/python/mllib/regression_metrics_example.py b/examples/src/main/python/mllib/regression_metrics_example.py
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+++ b/examples/src/main/python/mllib/regression_metrics_example.py
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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.
+#
+# $example on$
+from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD
+from pyspark.mllib.evaluation import RegressionMetrics
+from pyspark.mllib.linalg import DenseVector
+# $example off$
+
+from pyspark import SparkContext
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="Regression Metrics Example")
+
+    # $example on$
+    # Load and parse the data
+    def parsePoint(line):
+        values = line.split()
+        return LabeledPoint(float(values[0]),
+                            DenseVector([float(x.split(':')[1]) for x in values[1:]]))
+
+    data = sc.textFile("data/mllib/sample_linear_regression_data.txt")
+    parsedData = data.map(parsePoint)
+
+    # Build the model
+    model = LinearRegressionWithSGD.train(parsedData)
+
+    # Get predictions
+    valuesAndPreds = parsedData.map(lambda p: (float(model.predict(p.features)), p.label))
+
+    # Instantiate metrics object
+    metrics = RegressionMetrics(valuesAndPreds)
+
+    # Squared Error
+    print("MSE = %s" % metrics.meanSquaredError)
+    print("RMSE = %s" % metrics.rootMeanSquaredError)
+
+    # R-squared
+    print("R-squared = %s" % metrics.r2)
+
+    # Mean absolute error
+    print("MAE = %s" % metrics.meanAbsoluteError)
+
+    # Explained variance
+    print("Explained variance = %s" % metrics.explainedVariance)
+    # $example off$