1 files changed, 14 insertions, 7 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index b3cc361154..43f13fe24f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -49,6 +49,7 @@ object DecisionTreeRunner {
   case class Params(
       input: String = null,
       algo: Algo = Classification,
+      numClassesForClassification: Int = 2,
       maxDepth: Int = 5,
       impurity: ImpurityType = Gini,
       maxBins: Int = 100)
@@ -68,6 +69,10 @@ object DecisionTreeRunner {
       opt[Int]("maxDepth")
         .text(s"max depth of the tree, default: ${defaultParams.maxDepth}")
         .action((x, c) => c.copy(maxDepth = x))
+      opt[Int]("numClassesForClassification")
+        .text(s"number of classes for classification, "
+          + s"default: ${defaultParams.numClassesForClassification}")
+        .action((x, c) => c.copy(numClassesForClassification = x))
       opt[Int]("maxBins")
         .text(s"max number of bins, default: ${defaultParams.maxBins}")
         .action((x, c) => c.copy(maxBins = x))
@@ -118,7 +123,13 @@ object DecisionTreeRunner {
       case Variance => impurity.Variance
     }
 
-    val strategy = new Strategy(params.algo, impurityCalculator, params.maxDepth, params.maxBins)
+    val strategy
+      = new Strategy(
+          algo = params.algo,
+          impurity = impurityCalculator,
+          maxDepth = params.maxDepth,
+          maxBins = params.maxBins,
+          numClassesForClassification = params.numClassesForClassification)
     val model = DecisionTree.train(training, strategy)
 
     if (params.algo == Classification) {
@@ -139,12 +150,8 @@ object DecisionTreeRunner {
    */
   private def accuracyScore(
       model: DecisionTreeModel,
-      data: RDD[LabeledPoint],
-      threshold: Double = 0.5): Double = {
-    def predictedValue(features: Vector): Double = {
-      if (model.predict(features) < threshold) 0.0 else 1.0
-    }
-    val correctCount = data.filter(y => predictedValue(y.features) == y.label).count()
+      data: RDD[LabeledPoint]): Double = {
+    val correctCount = data.filter(y => model.predict(y.features) == y.label).count()
     val count = data.count()
     correctCount.toDouble / count
   }