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diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.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.mllib.util
+
+import scala.util.Random
+
+import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.regression.LabeledPoint
+
+/**
+ * Generate test data for LogisticRegression. This class chooses positive labels
+ * with probability `probOne` and scales features for positive examples by `eps`.
+ */
+
+object LogisticRegressionDataGenerator {
+
+  /**
+   * Generate an RDD containing test data for LogisticRegression.
+   *
+   * @param sc SparkContext to use for creating the RDD.
+   * @param nexamples Number of examples that will be contained in the RDD.
+   * @param nfeatures Number of features to generate for each example.
+   * @param eps Epsilon factor by which positive examples are scaled.
+   * @param nparts Number of partitions of the generated RDD. Default value is 2.
+   * @param probOne Probability that a label is 1 (and not 0). Default value is 0.5.
+   */
+  def generateLogisticRDD(
+    sc: SparkContext,
+    nexamples: Int,
+    nfeatures: Int,
+    eps: Double,
+    nparts: Int = 2,
+    probOne: Double = 0.5): RDD[LabeledPoint] = {
+    val data = sc.parallelize(0 until nexamples, nparts).map { idx =>
+      val rnd = new Random(42 + idx)
+
+      val y = if (idx % 2 == 0) 0.0 else 1.0
+      val x = Array.fill[Double](nfeatures) {
+        rnd.nextGaussian() + (y * eps)
+      }
+      LabeledPoint(y, x)
+    }
+    data
+  }
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      println("Usage: LogisticRegressionGenerator " +
+        "<master> <output_dir> <num_examples> <num_features> <num_partitions>")
+      System.exit(1)
+    }
+
+    val sparkMaster: String = args(0)
+    val outputPath: String = args(1)
+    val nexamples: Int = if (args.length > 2) args(2).toInt else 1000
+    val nfeatures: Int = if (args.length > 3) args(3).toInt else 2
+    val parts: Int = if (args.length > 4) args(4).toInt else 2
+    val eps = 3
+
+    val sc = new SparkContext(sparkMaster, "LogisticRegressionDataGenerator")
+    val data = generateLogisticRDD(sc, nexamples, nfeatures, eps, parts)
+
+    MLUtils.saveLabeledData(data, outputPath)
+    sc.stop()
+  }
+}