[SPARK-4580] [SPARK-4610] [mllib] [docs] Documentation for tree ensembles + DecisionTree API fix

Major changes:
* Added programming guide sections for tree ensembles
* Added examples for tree ensembles
* Updated DecisionTree programming guide with more info on parameters
* **API change**: Standardized the tree parameter for the number of classes (for classification)

Minor changes:
* Updated decision tree documentation
* Updated existing tree and tree ensemble examples
 * Use train/test split, and compute test error instead of training error.
 * Fixed decision_tree_runner.py to actually use the number of classes it computes from data. (small bug fix)

Note: I know this is a lot of lines, but most is covered by:
* Programming guide sections for gradient boosting and random forests.  (The changes are probably best viewed by generating the docs locally.)
* New examples (which were copied from the programming guide)
* The "numClasses" renaming

I have run all examples and relevant unit tests.

CC: mengxr manishamde codedeft

Author: Joseph K. Bradley <joseph@databricks.com>
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #3461 from jkbradley/ensemble-docs and squashes the following commits:

70a75f3 [Joseph K. Bradley] updated forest vs boosting comparison
d1de753 [Joseph K. Bradley] Added note about toString and toDebugString for DecisionTree to migration guide
8e87f8f [Joseph K. Bradley] Combined GBT and RandomForest guides into one ensembles guide
6fab846 [Joseph K. Bradley] small fixes based on review
b9f8576 [Joseph K. Bradley] updated decision tree doc
375204c [Joseph K. Bradley] fixed python style
2b60b6e [Joseph K. Bradley] merged Java RandomForest examples into 1 file.  added header.  Fixed small bug in same example in the programming guide.
706d332 [Joseph K. Bradley] updated python DT runner to print full model if it is small
c76c823 [Joseph K. Bradley] added migration guide for mllib
abe5ed7 [Joseph K. Bradley] added examples for random forest in Java and Python to examples folder
07fc11d [Joseph K. Bradley] Renamed numClassesForClassification to numClasses everywhere in trees and ensembles. This is a breaking API change, but it was necessary to correct an API inconsistency in Spark 1.1 (where Python DecisionTree used numClasses but Scala used numClassesForClassification).
cdfdfbc [Joseph K. Bradley] added examples for GBT
6372a2b [Joseph K. Bradley] updated decision tree examples to use random split.  tested all of them.
ad3e695 [Joseph K. Bradley] added gbt and random forest to programming guide.  still need to update their examples

9 files changed, 71 insertions, 71 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 9f20cd5d00..c4e5fd8e46 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -477,7 +477,7 @@ class PythonMLLibAPI extends Serializable {
       algo = algo,
       impurity = impurity,
       maxDepth = maxDepth,
-      numClassesForClassification = numClasses,
+      numClasses = numClasses,
       maxBins = maxBins,
       categoricalFeaturesInfo = categoricalFeaturesInfo.asScala.toMap,
       minInstancesPerNode = minInstancesPerNode,
@@ -513,7 +513,7 @@ class PythonMLLibAPI extends Serializable {
       algo = algo,
       impurity = impurity,
       maxDepth = maxDepth,
-      numClassesForClassification = numClasses,
+      numClasses = numClasses,
       maxBins = maxBins,
       categoricalFeaturesInfo = categoricalFeaturesInfo.asScala.toMap)
     val cached = data.rdd.persist(StorageLevel.MEMORY_AND_DISK)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 3d91867c89..73e7e32c6d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -136,7 +136,7 @@ object DecisionTree extends Serializable with Logging {
    * @param impurity impurity criterion used for information gain calculation
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   * @param numClassesForClassification number of classes for classification. Default value of 2.
+   * @param numClasses number of classes for classification. Default value of 2.
    * @return DecisionTreeModel that can be used for prediction
    */
   def train(
@@ -144,8 +144,8 @@ object DecisionTree extends Serializable with Logging {
       algo: Algo,
       impurity: Impurity,
       maxDepth: Int,
-      numClassesForClassification: Int): DecisionTreeModel = {
-    val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification)
+      numClasses: Int): DecisionTreeModel = {
+    val strategy = new Strategy(algo, impurity, maxDepth, numClasses)
     new DecisionTree(strategy).run(input)
   }
 
@@ -164,7 +164,7 @@ object DecisionTree extends Serializable with Logging {
    * @param impurity criterion used for information gain calculation
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   * @param numClassesForClassification number of classes for classification. Default value of 2.
+   * @param numClasses number of classes for classification. Default value of 2.
    * @param maxBins maximum number of bins used for splitting features
    * @param quantileCalculationStrategy  algorithm for calculating quantiles
    * @param categoricalFeaturesInfo Map storing arity of categorical features.
@@ -177,11 +177,11 @@ object DecisionTree extends Serializable with Logging {
       algo: Algo,
       impurity: Impurity,
       maxDepth: Int,
-      numClassesForClassification: Int,
+      numClasses: Int,
       maxBins: Int,
       quantileCalculationStrategy: QuantileStrategy,
       categoricalFeaturesInfo: Map[Int,Int]): DecisionTreeModel = {
-    val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification, maxBins,
+    val strategy = new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo)
     new DecisionTree(strategy).run(input)
   }
@@ -191,7 +191,7 @@ object DecisionTree extends Serializable with Logging {
    *
    * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    *              Labels should take values {0, 1, ..., numClasses-1}.
-   * @param numClassesForClassification number of classes for classification.
+   * @param numClasses number of classes for classification.
    * @param categoricalFeaturesInfo Map storing arity of categorical features.
    *                                E.g., an entry (n -> k) indicates that feature n is categorical
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
@@ -206,13 +206,13 @@ object DecisionTree extends Serializable with Logging {
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
-      numClassesForClassification: Int,
+      numClasses: Int,
       categoricalFeaturesInfo: Map[Int, Int],
       impurity: String,
       maxDepth: Int,
       maxBins: Int): DecisionTreeModel = {
     val impurityType = Impurities.fromString(impurity)
-    train(input, Classification, impurityType, maxDepth, numClassesForClassification, maxBins, Sort,
+    train(input, Classification, impurityType, maxDepth, numClasses, maxBins, Sort,
       categoricalFeaturesInfo)
   }
 
@@ -221,12 +221,12 @@ object DecisionTree extends Serializable with Logging {
    */
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
-      numClassesForClassification: Int,
+      numClasses: Int,
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
       impurity: String,
       maxDepth: Int,
       maxBins: Int): DecisionTreeModel = {
-    trainClassifier(input.rdd, numClassesForClassification,
+    trainClassifier(input.rdd, numClasses,
       categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap,
       impurity, maxDepth, maxBins)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 482d339551..e9304b5e5c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -56,7 +56,7 @@ import org.apache.spark.util.Utils
  *                 etc.
  * @param numTrees If 1, then no bootstrapping is used.  If > 1, then bootstrapping is done.
  * @param featureSubsetStrategy Number of features to consider for splits at each node.
- *                              Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
+ *                              Supported: "auto", "all", "sqrt", "log2", "onethird".
  *                              If "auto" is set, this parameter is set based on numTrees:
  *                                if numTrees == 1, set to "all";
  *                                if numTrees > 1 (forest) set to "sqrt" for classification and
@@ -269,7 +269,7 @@ object RandomForest extends Serializable with Logging {
    * @param strategy Parameters for training each tree in the forest.
    * @param numTrees Number of trees in the random forest.
    * @param featureSubsetStrategy Number of features to consider for splits at each node.
-   *                              Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
+   *                              Supported: "auto", "all", "sqrt", "log2", "onethird".
    *                              If "auto" is set, this parameter is set based on numTrees:
    *                                if numTrees == 1, set to "all";
    *                                if numTrees > 1 (forest) set to "sqrt".
@@ -293,13 +293,13 @@ object RandomForest extends Serializable with Logging {
    *
    * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    *              Labels should take values {0, 1, ..., numClasses-1}.
-   * @param numClassesForClassification number of classes for classification.
+   * @param numClasses number of classes for classification.
    * @param categoricalFeaturesInfo Map storing arity of categorical features.
    *                                E.g., an entry (n -> k) indicates that feature n is categorical
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
    * @param numTrees Number of trees in the random forest.
    * @param featureSubsetStrategy Number of features to consider for splits at each node.
-   *                              Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
+   *                              Supported: "auto", "all", "sqrt", "log2", "onethird".
    *                              If "auto" is set, this parameter is set based on numTrees:
    *                                if numTrees == 1, set to "all";
    *                                if numTrees > 1 (forest) set to "sqrt".
@@ -315,7 +315,7 @@ object RandomForest extends Serializable with Logging {
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
-      numClassesForClassification: Int,
+      numClasses: Int,
       categoricalFeaturesInfo: Map[Int, Int],
       numTrees: Int,
       featureSubsetStrategy: String,
@@ -325,7 +325,7 @@ object RandomForest extends Serializable with Logging {
       seed: Int = Utils.random.nextInt()): RandomForestModel = {
     val impurityType = Impurities.fromString(impurity)
     val strategy = new Strategy(Classification, impurityType, maxDepth,
-      numClassesForClassification, maxBins, Sort, categoricalFeaturesInfo)
+      numClasses, maxBins, Sort, categoricalFeaturesInfo)
     trainClassifier(input, strategy, numTrees, featureSubsetStrategy, seed)
   }
 
@@ -334,7 +334,7 @@ object RandomForest extends Serializable with Logging {
    */
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
-      numClassesForClassification: Int,
+      numClasses: Int,
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
       numTrees: Int,
       featureSubsetStrategy: String,
@@ -342,7 +342,7 @@ object RandomForest extends Serializable with Logging {
       maxDepth: Int,
       maxBins: Int,
       seed: Int): RandomForestModel = {
-    trainClassifier(input.rdd, numClassesForClassification,
+    trainClassifier(input.rdd, numClasses,
       categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap,
       numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed)
   }
@@ -355,7 +355,7 @@ object RandomForest extends Serializable with Logging {
    * @param strategy Parameters for training each tree in the forest.
    * @param numTrees Number of trees in the random forest.
    * @param featureSubsetStrategy Number of features to consider for splits at each node.
-   *                              Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
+   *                              Supported: "auto", "all", "sqrt", "log2", "onethird".
    *                              If "auto" is set, this parameter is set based on numTrees:
    *                                if numTrees == 1, set to "all";
    *                                if numTrees > 1 (forest) set to "onethird".
@@ -384,7 +384,7 @@ object RandomForest extends Serializable with Logging {
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
    * @param numTrees Number of trees in the random forest.
    * @param featureSubsetStrategy Number of features to consider for splits at each node.
-   *                              Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
+   *                              Supported: "auto", "all", "sqrt", "log2", "onethird".
    *                              If "auto" is set, this parameter is set based on numTrees:
    *                                if numTrees == 1, set to "all";
    *                                if numTrees > 1 (forest) set to "onethird".
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index e703adbdbf..cf51d041c6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -51,7 +51,7 @@ case class BoostingStrategy(
   private[tree] def assertValid(): Unit = {
     treeStrategy.algo match {
       case Classification =>
-        require(treeStrategy.numClassesForClassification == 2,
+        require(treeStrategy.numClasses == 2,
           "Only binary classification is supported for boosting.")
       case Regression =>
         // nothing
@@ -80,12 +80,12 @@ object BoostingStrategy {
     treeStrategy.maxDepth = 3
     algo match {
       case "Classification" =>
-        treeStrategy.numClassesForClassification = 2
+        treeStrategy.numClasses = 2
         new BoostingStrategy(treeStrategy, LogLoss)
       case "Regression" =>
         new BoostingStrategy(treeStrategy, SquaredError)
       case _ =>
-        throw new IllegalArgumentException(s"$algo is not supported by the boosting.")
+        throw new IllegalArgumentException(s"$algo is not supported by boosting.")
     }
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index d75f38433c..d5cd89ab94 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -37,7 +37,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                 Supported for Regression: [[org.apache.spark.mllib.tree.impurity.Variance]].
  * @param maxDepth Maximum depth of the tree.
  *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
- * @param numClassesForClassification Number of classes for classification.
+ * @param numClasses Number of classes for classification.
  *                                    (Ignored for regression.)
  *                                    Default value is 2 (binary classification).
  * @param maxBins Maximum number of bins used for discretizing continuous features and
@@ -73,7 +73,7 @@ class Strategy (
     @BeanProperty var algo: Algo,
     @BeanProperty var impurity: Impurity,
     @BeanProperty var maxDepth: Int,
-    @BeanProperty var numClassesForClassification: Int = 2,
+    @BeanProperty var numClasses: Int = 2,
     @BeanProperty var maxBins: Int = 32,
     @BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
     @BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
@@ -86,7 +86,7 @@ class Strategy (
     @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
   def isMulticlassClassification =
-    algo == Classification && numClassesForClassification > 2
+    algo == Classification && numClasses > 2
   def isMulticlassWithCategoricalFeatures
     = isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
 
@@ -97,10 +97,10 @@ class Strategy (
       algo: Algo,
       impurity: Impurity,
       maxDepth: Int,
-      numClassesForClassification: Int,
+      numClasses: Int,
       maxBins: Int,
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]) {
-    this(algo, impurity, maxDepth, numClassesForClassification, maxBins, Sort,
+    this(algo, impurity, maxDepth, numClasses, maxBins, Sort,
       categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap)
   }
 
@@ -117,8 +117,8 @@ class Strategy (
    */
   def setCategoricalFeaturesInfo(
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]): Unit = {
-    setCategoricalFeaturesInfo(
-      categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap)
+    this.categoricalFeaturesInfo =
+      categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap
   }
 
   /**
@@ -128,9 +128,9 @@ class Strategy (
   private[tree] def assertValid(): Unit = {
     algo match {
       case Classification =>
-        require(numClassesForClassification >= 2,
-          s"DecisionTree Strategy for Classification must have numClassesForClassification >= 2," +
-          s" but numClassesForClassification = $numClassesForClassification.")
+        require(numClasses >= 2,
+          s"DecisionTree Strategy for Classification must have numClasses >= 2," +
+          s" but numClasses = $numClasses.")
         require(Set(Gini, Entropy).contains(impurity),
           s"DecisionTree Strategy given invalid impurity for Classification: $impurity." +
           s"  Valid settings: Gini, Entropy")
@@ -160,7 +160,7 @@ class Strategy (
 
   /** Returns a shallow copy of this instance. */
   def copy: Strategy = {
-    new Strategy(algo, impurity, maxDepth, numClassesForClassification, maxBins,
+    new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo, minInstancesPerNode, minInfoGain,
       maxMemoryInMB, subsamplingRate, useNodeIdCache, checkpointDir, checkpointInterval)
   }
@@ -176,9 +176,9 @@ object Strategy {
   def defaultStrategy(algo: String): Strategy = algo match {
     case "Classification" =>
       new Strategy(algo = Classification, impurity = Gini, maxDepth = 10,
-        numClassesForClassification = 2)
+        numClasses = 2)
     case "Regression" =>
       new Strategy(algo = Regression, impurity = Variance, maxDepth = 10,
-        numClassesForClassification = 0)
+        numClasses = 0)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index 5bc0f2635c..951733fada 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -110,7 +110,7 @@ private[tree] object DecisionTreeMetadata extends Logging {
     val numFeatures = input.take(1)(0).features.size
     val numExamples = input.count()
     val numClasses = strategy.algo match {
-      case Classification => strategy.numClassesForClassification
+      case Classification => strategy.numClasses
       case Regression => 0
     }
 
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 972c905ec9..9347eaf922 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
@@ -57,7 +57,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
       Classification,
       Gini,
       maxDepth = 2,
-      numClassesForClassification = 2,
+      numClasses = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 2, 1-> 2))
 
@@ -81,7 +81,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
       Classification,
       Gini,
       maxDepth = 2,
-      numClassesForClassification = 2,
+      numClasses = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
 
@@ -177,7 +177,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
       Classification,
       Gini,
       maxDepth = 2,
-      numClassesForClassification = 100,
+      numClasses = 100,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
 
@@ -271,7 +271,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
       Classification,
       Gini,
       maxDepth = 2,
-      numClassesForClassification = 100,
+      numClasses = 100,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 10, 1-> 10))
     // 2^(10-1) - 1 > 100, so categorical features will be ordered
@@ -295,7 +295,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val strategy = new Strategy(
       Classification,
       Gini,
-      numClassesForClassification = 2,
+      numClasses = 2,
       maxDepth = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
@@ -377,7 +377,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Gini, maxDepth = 3,
-      numClassesForClassification = 2, maxBins = 100)
+      numClasses = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
@@ -401,7 +401,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Gini, maxDepth = 3,
-      numClassesForClassification = 2, maxBins = 100)
+      numClasses = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
@@ -426,7 +426,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Entropy, maxDepth = 3,
-      numClassesForClassification = 2, maxBins = 100)
+      numClasses = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
@@ -451,7 +451,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Entropy, maxDepth = 3,
-      numClassesForClassification = 2, maxBins = 100)
+      numClasses = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
     assert(!metadata.isUnordered(featureIndex = 1))
@@ -485,7 +485,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
 
     // Train a 1-node model
     val strategyOneNode = new Strategy(Classification, Entropy, maxDepth = 1,
-      numClassesForClassification = 2, maxBins = 100)
+      numClasses = 2, maxBins = 100)
     val modelOneNode = DecisionTree.train(rdd, strategyOneNode)
     val rootNode1 = modelOneNode.topNode.deepCopy()
     val rootNode2 = modelOneNode.topNode.deepCopy()
@@ -545,7 +545,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
+      numClasses = 3, categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(strategy.isMulticlassClassification)
     assert(metadata.isUnordered(featureIndex = 0))
@@ -568,7 +568,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     arr(3) = new LabeledPoint(1.0, Vectors.dense(3.0))
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 2)
+      numClasses = 2)
 
     val model = DecisionTree.train(rdd, strategy)
     DecisionTreeSuite.validateClassifier(model, arr, 1.0)
@@ -585,7 +585,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
 
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 2)
+      numClasses = 2)
 
     val model = DecisionTree.train(rdd, strategy)
     DecisionTreeSuite.validateClassifier(model, arr, 1.0)
@@ -600,7 +600,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, maxBins = maxBins,
+      numClasses = 3, maxBins = maxBins,
       categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
@@ -629,7 +629,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, maxBins = 100)
+      numClasses = 3, maxBins = 100)
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
 
@@ -650,7 +650,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, maxBins = 100, categoricalFeaturesInfo = Map(0 -> 3))
+      numClasses = 3, maxBins = 100, categoricalFeaturesInfo = Map(0 -> 3))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(metadata.isUnordered(featureIndex = 0))
@@ -671,7 +671,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, maxBins = 100,
+      numClasses = 3, maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
@@ -692,7 +692,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, maxBins = 10,
+      numClasses = 3, maxBins = 10,
       categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
     assert(strategy.isMulticlassClassification)
 
@@ -708,7 +708,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
 
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini,
-      maxDepth = 2, numClassesForClassification = 2, minInstancesPerNode = 2)
+      maxDepth = 2, numClasses = 2, minInstancesPerNode = 2)
 
     val model = DecisionTree.train(rdd, strategy)
     assert(model.topNode.isLeaf)
@@ -737,7 +737,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini,
       maxBins = 2, maxDepth = 2, categoricalFeaturesInfo = Map(0 -> 2, 1-> 2),
-      numClassesForClassification = 2, minInstancesPerNode = 2)
+      numClasses = 2, minInstancesPerNode = 2)
 
     val rootNode = DecisionTree.train(rdd, strategy).topNode
 
@@ -755,7 +755,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
 
     val input = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
-      numClassesForClassification = 2, minInfoGain = 1.0)
+      numClasses = 2, minInfoGain = 1.0)
 
     val model = DecisionTree.train(input, strategy)
     assert(model.topNode.isLeaf)
@@ -781,7 +781,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val input = sc.parallelize(arr)
 
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 1,
-      numClassesForClassification = 2, categoricalFeaturesInfo = Map(0 -> 3))
+      numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3))
     val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
     val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
 
@@ -824,7 +824,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     val input = sc.parallelize(arr)
 
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
-      numClassesForClassification = 2, categoricalFeaturesInfo = Map(0 -> 3))
+      numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3))
     val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
     val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
index d4d54cf4c9..3aa97e5446 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala
@@ -100,7 +100,7 @@ class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext {
         val rdd = sc.parallelize(GradientBoostedTreesSuite.data, 2)
 
         val treeStrategy = new Strategy(algo = Classification, impurity = Variance, maxDepth = 2,
-          numClassesForClassification = 2, categoricalFeaturesInfo = Map.empty,
+          numClasses = 2, categoricalFeaturesInfo = Map.empty,
           subsamplingRate = subsamplingRate)
         val boostingStrategy =
           new BoostingStrategy(treeStrategy, LogLoss, numIterations, learningRate)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
index 90a8c2dfda..f7f0f20c6c 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
@@ -57,7 +57,7 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
     " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
     val categoricalFeaturesInfo = Map.empty[Int, Int]
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
-      numClassesForClassification = 2, categoricalFeaturesInfo = categoricalFeaturesInfo)
+      numClasses = 2, categoricalFeaturesInfo = categoricalFeaturesInfo)
     binaryClassificationTestWithContinuousFeatures(strategy)
   }
 
@@ -65,7 +65,7 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
     " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
     val categoricalFeaturesInfo = Map.empty[Int, Int]
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
-      numClassesForClassification = 2, categoricalFeaturesInfo = categoricalFeaturesInfo,
+      numClasses = 2, categoricalFeaturesInfo = categoricalFeaturesInfo,
       useNodeIdCache = true)
     binaryClassificationTestWithContinuousFeatures(strategy)
   }
@@ -93,7 +93,7 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
     " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
     val categoricalFeaturesInfo = Map.empty[Int, Int]
     val strategy = new Strategy(algo = Regression, impurity = Variance,
-      maxDepth = 2, maxBins = 10, numClassesForClassification = 2,
+      maxDepth = 2, maxBins = 10, numClasses = 2,
       categoricalFeaturesInfo = categoricalFeaturesInfo)
     regressionTestWithContinuousFeatures(strategy)
   }
@@ -102,7 +102,7 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
     " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
     val categoricalFeaturesInfo = Map.empty[Int, Int]
     val strategy = new Strategy(algo = Regression, impurity = Variance,
-      maxDepth = 2, maxBins = 10, numClassesForClassification = 2,
+      maxDepth = 2, maxBins = 10, numClasses = 2,
       categoricalFeaturesInfo = categoricalFeaturesInfo, useNodeIdCache = true)
     regressionTestWithContinuousFeatures(strategy)
   }
@@ -169,14 +169,14 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
   test("Binary classification with continuous features: subsampling features") {
     val categoricalFeaturesInfo = Map.empty[Int, Int]
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
-      numClassesForClassification = 2, categoricalFeaturesInfo = categoricalFeaturesInfo)
+      numClasses = 2, categoricalFeaturesInfo = categoricalFeaturesInfo)
     binaryClassificationTestWithContinuousFeaturesAndSubsampledFeatures(strategy)
   }
 
   test("Binary classification with continuous features and node Id cache: subsampling features") {
     val categoricalFeaturesInfo = Map.empty[Int, Int]
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
-      numClassesForClassification = 2, categoricalFeaturesInfo = categoricalFeaturesInfo,
+      numClasses = 2, categoricalFeaturesInfo = categoricalFeaturesInfo,
       useNodeIdCache = true)
     binaryClassificationTestWithContinuousFeaturesAndSubsampledFeatures(strategy)
   }
@@ -191,7 +191,7 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
     val input = sc.parallelize(arr)
 
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5,
-      numClassesForClassification = 3, categoricalFeaturesInfo = categoricalFeaturesInfo)
+      numClasses = 3, categoricalFeaturesInfo = categoricalFeaturesInfo)
     val model = RandomForest.trainClassifier(input, strategy, numTrees = 2,
       featureSubsetStrategy = "sqrt", seed = 12345)
     EnsembleTestHelper.validateClassifier(model, arr, 1.0)