diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index e7a55d52e7367..664abf742d4a1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -133,7 +133,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       maxDepth = 3,
       numClassesForClassification = 2,
       maxBins = 100,
-      categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
+      categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy)
 
     // Check splits.
@@ -483,7 +483,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.predict === 1)
   }
 
-  test("test second level node building with/without groups") {
+  test("second level node building with/without groups") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -529,6 +529,33 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
   }
 
+  test("stump with continuous variables for multiclass classification") {
+    assert(true==true)
+  }
+
+  test("stump with categorical variables for multiclass classification") {
+    val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass()
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
+      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
+    assert(strategy.isMulticlassClassification)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+    val bestSplits = DecisionTree.findBestSplits(input, new Array(31), strategy, 0,
+      Array[List[Filter]](), splits, bins, 10)
+
+    assert(bestSplits.length === 1)
+    val bestSplit = bestSplits(0)._1
+    assert(bestSplit.feature === 0)
+    assert(bestSplit.categories.length === 1)
+    assert(bestSplit.categories.contains(0))
+    assert(bestSplit.featureType === Categorical)
+    println(bestSplit)
+  }
+
+  test("stump with continuous + categorical variables for multiclass classification") {
+    assert(true==true)
+  }
+
 }
 
 object DecisionTreeSuite {
@@ -576,4 +603,22 @@ object DecisionTreeSuite {
     }
     arr
   }
+
+  def generateCategoricalDataPointsForMulticlass(): Array[WeightedLabeledPoint] = {
+    val arr = new Array[WeightedLabeledPoint](3000)
+    for (i <- 0 until 3000) {
+      if (i < 1000) {
+        arr(i) = new WeightedLabeledPoint(2.0, Vectors.dense(2.0, 2.0))
+      } else if (i < 2000) {
+        arr(i) = new WeightedLabeledPoint(1.0, Vectors.dense(1.0, 2.0))
+      } else {
+        arr(i) = new WeightedLabeledPoint(2.0, Vectors.dense(2.0, 2.0))
+      }
+    }
+    println(arr(0))
+    println(arr(1000))
+    println(arr(2000))
+    arr
+  }
+
 }