add comments

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

@@ -739,7 +739,7 @@ object DecisionTree extends Serializable with Logging {
     val rightCount = rightImpurityCalculator.count
 
     // If left child or right child doesn't satisfy minimum instances per node,
-    // then this split is invalid, return invalid information gain stats
+    // then this split is invalid, return invalid information gain stats.
     if ((leftCount < metadata.minInstancesPerNode) ||
         (rightCount < metadata.minInstancesPerNode)) {
       return InformationGainStats.invalidInformationGainStats
@@ -764,13 +764,23 @@ object DecisionTree extends Serializable with Logging {
     val rightWeight = rightCount / totalCount.toDouble
 
     val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
+
+    // if information gain doesn't satisfy minimum information gain,
+    // then this split is invalid, return invalid information gain stats.
     if (gain < metadata.minInfoGain) {
       return InformationGainStats.invalidInformationGainStats
     }
 
     new InformationGainStats(gain, impurity, leftImpurity, rightImpurity)
   }
 
+  /**
+   * Calculate predict value for current node, given stats of any split.
+   * Note that this function is called only once for each node.
+   * @param leftImpurityCalculator left node aggregates for a split
+   * @param rightImpurityCalculator right node aggregates for a node
+   * @return predict value for current node
+   */
   private def calculatePredict(
       leftImpurityCalculator: ImpurityCalculator,
       rightImpurityCalculator: ImpurityCalculator): Predict =  {
@@ -799,6 +809,7 @@ object DecisionTree extends Serializable with Logging {
 
     logDebug("node impurity = " + nodeImpurity)
 
+    // calculate predict only once
     var predict: Option[Predict] = None
 
     // For each (feature, split), calculate the gain, and select the best (feature, split).

mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala

@@ -42,5 +42,10 @@ class InformationGainStats(
 
 
 private[tree] object InformationGainStats {
+  /**
+   * An [[org.apache.spark.mllib.tree.model.InformationGainStats]] object to
+   * denote that current split doesn't satisfies minimum info gain or
+   * minimum number of instances per node.
+   */
   val invalidInformationGainStats = new InformationGainStats(Double.MinValue, -1.0, -1.0, -1.0)
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala

@@ -68,7 +68,11 @@ private[tree] class DummyHighSplit(feature: Int, featureType: FeatureType)
 private[tree] class DummyCategoricalSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MaxValue, featureType, List())
 
-
 private[tree] object Split {
+  /**
+   * A [[org.apache.spark.mllib.tree.model.Split]] object to denote that
+   * we can't find a valid split that satisfies minimum info gain
+   * or minimum number of instances per node.
+   */
   val noSplit = new Split(-1, Double.MinValue, FeatureType.Continuous, List())
 }