updating javadoc and converting helper methods to package private to …

…allow unit testing

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -172,7 +172,11 @@ class DecisionTree private(val strategy: Strategy) extends Serializable with Log
 object DecisionTree extends Serializable with Logging {
 
   /**
-   * Method to train a decision tree model over an RDD
+   * Method to train a decision tree model where the instances are represented as an RDD of
+   * (label, features) pairs. The method supports binary classification and regression. For the
+   * binary classification, the label for each instance should either be 0 or 1 to denote the two
+   * classes. The parameters for the algorithm are specified using the strategy parameter.
+   *
    * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
    *              for DecisionTree
    * @param strategy The configuration parameters for the tree algorithm which specify the type
@@ -185,7 +189,11 @@ object DecisionTree extends Serializable with Logging {
   }
 
   /**
-   * Method to train a decision tree model over an RDD
+   * Method to train a decision tree model where the instances are represented as an RDD of
+   * (label, features) pairs. The method supports binary classification and regression. For the
+   * binary classification, the label for each instance should either be 0 or 1 to denote the two
+   * classes.
+   *
    * @param input input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as
    *              training data
    * @param algo algorithm, classification or regression
@@ -204,8 +212,13 @@ object DecisionTree extends Serializable with Logging {
 
 
   /**
-   * Method to train a decision tree model over an RDD
-    * @param input input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as
+   * Method to train a decision tree model where the instances are represented as an RDD of
+   * (label, features) pairs. The decision tree method supports binary classification and
+   * regression. For the binary classification, the label for each instance should either be 0 or
+   * 1 to denote the two classes. The method also supports categorical features inputs where the
+   * number of categories can specified using the categoricalFeaturesInfo option.
+   *
+   * @param input input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as
    *              training data for DecisionTree
    * @param algo classification or regression
    * @param impurity criterion used for information gain calculation
@@ -236,6 +249,7 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Returns an array of optimal splits for all nodes at a given level
+   *
    * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
    *              for DecisionTree
    * @param parentImpurities Impurities for all parent nodes for the current level
@@ -247,7 +261,7 @@ object DecisionTree extends Serializable with Logging {
    * @param bins possible bins for all features
    * @return array of splits with best splits for all nodes at a given level.
    */
-  private def findBestSplits(
+  protected[tree] def findBestSplits(
       input: RDD[LabeledPoint],
       parentImpurities: Array[Double],
       strategy: Strategy,
@@ -885,7 +899,7 @@ object DecisionTree extends Serializable with Logging {
    *         .model.Split] of size (numFeatures, numSplits-1) and bins is an Array of [org.apache
    *         .spark.mllib.tree.model.Bin] of size (numFeatures, numSplits1)
    */
-  private def findSplitsBins(
+  protected[tree] def findSplitsBins(
       input: RDD[LabeledPoint],
       strategy: Strategy): (Array[Array[Split]], Array[Array[Bin]]) = {
     val count = input.count()