diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/LabelBinarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/LabelBinarizer.scala
new file mode 100644
index 000000000000..2c97397335bd
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/LabelBinarizer.scala
@@ -0,0 +1,96 @@
+/*
+ * 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.ml.feature
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable}
+import org.apache.spark.mllib.linalg.{Matrices, MatrixUDT}
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * :: Experimental ::
+ * Binarize a column of continuous features given a set of labels.
+ */
+
+@Experimental
+final class LabelBinarizer @Since("2.0.0")(override val uid: String)
+  extends Transformer with HasInputCol with HasOutputCol with DefaultParamsWritable {
+
+  def this() = this(Identifiable.randomUID("labelBinarizer"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+
+    val iter = udf { (s: String) =>
+      val arr = s.split(",")
+      val len = arr.length
+      val clsLen = arr.distinct.length
+      val vec: Array[Double] = new Array(len * clsLen)
+      var i: Int = 0
+      var j: Int = 0
+      arr.distinct.sortWith(_ < _).foreach { (v: String) =>
+        while (i < arr.length) {
+          val idx: Int = arr.indexOf(v, i)
+          if (idx != -1) {
+            vec.update(idx + j, 1)
+          }
+          i += 1
+        }
+        i = 0
+        j += len
+      }
+      Matrices.dense(len, clsLen, vec)
+    }
+    dataset.withColumn($(outputCol), iter(col($(inputCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val outputColName = $(outputCol)
+    val inputType = schema($(inputCol)).dataType
+
+    if (!inputType.typeName.equals("string")) {
+      throw new IllegalArgumentException(s"Data type $inputType is not supported.")
+    }
+
+    if (schema.fieldNames.contains(outputColName)) {
+      throw new IllegalArgumentException(s"Output column $outputColName already exists.")
+    }
+    val outputFields = schema.fields :+ StructField($(outputCol), new MatrixUDT, false)
+    StructType(outputFields)
+  }
+
+  override def copy(extra: ParamMap): LabelBinarizer = defaultCopy(extra)
+}
+
+@Since("1.6.0")
+object LabelBinarizer extends DefaultParamsReadable[LabelBinarizer] {
+
+  @Since("1.6.0")
+  override def load(path: String): LabelBinarizer = super.load(path)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/LabelBinarizerSuiter.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/LabelBinarizerSuiter.scala
new file mode 100644
index 000000000000..32b7faa1199a
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/LabelBinarizerSuiter.scala
@@ -0,0 +1,109 @@
+/*
+ * 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.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{Matrices, Matrix}
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql._
+
+class LabelBinarizerSuiter extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
+  test("Label binarize one-class case") {
+    val data = Array("pos,pos,pos,pos")
+    val expected: Array[Matrix] = Array(
+      Matrices.dense(4, 1, Array(1.0, 1.0, 1.0, 1.0)))
+    val dataFrame: DataFrame = sqlContext.createDataFrame(
+      data.zip(expected)).toDF("feature", "expected")
+
+    val lBinarizer: LabelBinarizer = new LabelBinarizer()
+      .setInputCol("feature")
+      .setOutputCol("binarized_feature")
+
+    lBinarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach {
+      case Row(x: Matrix, y: Matrix) =>
+        assert(x === y, "The feature value is not correct after binarization.")
+    }
+  }
+
+  test("Label binarize two-class case") {
+    val data = Array("pos,neg,neg,pos")
+    val expected: Array[Matrix] = Array(
+      Matrices.dense(4, 2, Array(0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0)))
+    val dataFrame: DataFrame = sqlContext.createDataFrame(
+      data.zip(expected)).toDF("feature", "expected")
+
+    val lBinarizer: LabelBinarizer = new LabelBinarizer()
+      .setInputCol("feature")
+      .setOutputCol("binarized_feature")
+
+    lBinarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach {
+      case Row(x: Matrix, y: Matrix) =>
+        assert(x === y, "The feature value is not correct after binarization.")
+    }
+  }
+
+  test("Label binarize multi-class case") {
+    val data = Array("yellow,green,red,green,0")
+    val expected: Array[Matrix] = Array(
+      Matrices.dense(5, 4,
+        Array(0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,
+          0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0)))
+    val dataFrame: DataFrame = sqlContext.createDataFrame(
+      data.zip(expected)).toDF("feature", "expected")
+
+    val lBinarizer: LabelBinarizer = new LabelBinarizer()
+      .setInputCol("feature")
+      .setOutputCol("binarized_feature")
+
+    lBinarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach {
+      case Row(x: Matrix, y: Matrix) =>
+        assert(x === y, "The feature value is not correct after binarization.")
+    }
+  }
+
+  test("Label binarize combination case") {
+    val data = Array("yellow,green,red,green,0",
+      "pos,neg,neg,pos")
+    val expected: Array[Matrix] = Array(
+      Matrices.dense(5, 4,
+        Array(0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0,
+          0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0)),
+      Matrices.dense(4, 2, Array(0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0))
+    )
+    val dataFrame: DataFrame = sqlContext.createDataFrame(
+      data.zip(expected)).toDF("feature", "expected")
+
+    val lBinarizer: LabelBinarizer = new LabelBinarizer()
+      .setInputCol("feature")
+      .setOutputCol("binarized_feature")
+
+    lBinarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach {
+      case Row(x: Matrix, y: Matrix) =>
+        assert(x === y, "The feature value is not correct after binarization.")
+    }
+  }
+
+  test("read/write") {
+    val t = new Binarizer()
+      .setInputCol("myInputCol")
+      .setOutputCol("myOutputCol")
+      .setThreshold(0.1)
+    testDefaultReadWrite(t)
+  }
+}