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 bba67bb5894a9..059a9336b5f9e 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
@@ -50,23 +50,34 @@ class DecisionTree private(val strategy: Strategy) extends Serializable with Log
 
     //Cache input RDD for speedup during multiple passes
     input.cache()
+    logDebug("algo = " + strategy.algo)
 
+    //Finding the splits and the corresponding bins (interval between the splits) using a sample
+    // of the input data.
     val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
     logDebug("numSplits = " + bins(0).length)
+
+    //Noting numBins for the input data
     strategy.numBins = bins(0).length
 
+    //The depth of the decision tree
     val maxDepth = strategy.maxDepth
-
+    //The max number of nodes possible given the depth of the tree
     val maxNumNodes = scala.math.pow(2, maxDepth).toInt - 1
+    //Initalizing an array to hold filters applied to points for each node
     val filters = new Array[List[Filter]](maxNumNodes)
+    //The filter at the top node is an empty list
     filters(0) = List()
+    //Initializing an array to hold parent impurity calculations for each node
     val parentImpurities = new Array[Double](maxNumNodes)
     //Dummy value for top node (updated during first split calculation)
-    //parentImpurities(0) = Double.MinValue
     val nodes = new Array[Node](maxNumNodes)
 
-    logDebug("algo = " + strategy.algo)
-
+    //The main-idea here is to perform level-wise training of the decision tree nodes thus
+    // reducing the passes over the data from l to log2(l) where l is the total number of nodes.
+    // Each data sample is checked for validity w.r.t to each node at a given level -- i.e.,
+    // the sample is only used for the split calculation at the node if the sampled would have
+    // still survived the filters of the parent nodes.
     breakable {
       for (level <- 0 until maxDepth) {
 
@@ -79,36 +90,41 @@ class DecisionTree private(val strategy: Strategy) extends Serializable with Log
           level, filters, splits, bins)
 
         for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
-
+          //Extract info for nodes at the current level
           extractNodeInfo(nodeSplitStats, level, index, nodes)
+          //Extract info for nodes at the next lower level
           extractInfoForLowerLevels(level, index, maxDepth, nodeSplitStats, parentImpurities,
             filters)
           logDebug("final best split = " + nodeSplitStats._1)
 
         }
         require(scala.math.pow(2, level) == splitsStatsForLevel.length)
-
+        //Check whether all the nodes at the current level at leaves
         val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
         logDebug("all leaf = " + allLeaf)
-        if (allLeaf) break
+        if (allLeaf) break  //no more tree construction
 
       }
     }
 
+    //Initialize the top or root node of the tree
     val topNode = nodes(0)
+    //Build the full tree using the node info calculated in the level-wise best split calculations
     topNode.build(nodes)
 
-    val decisionTreeModel = {
-      return new DecisionTreeModel(topNode, strategy.algo)
-    }
-    return decisionTreeModel
+    //Return a decision tree model
+    return new DecisionTreeModel(topNode, strategy.algo)
   }
 
-
+  /**
+   * Extract the decision tree node information for th given tree level and node index
+   */
   private def extractNodeInfo(
       nodeSplitStats: (Split, InformationGainStats),
-      level: Int, index: Int,
-      nodes: Array[Node]) {
+      level: Int,
+      index: Int,
+      nodes: Array[Node])
+    : Unit = {
 
     val split = nodeSplitStats._1
     val stats = nodeSplitStats._2
@@ -119,35 +135,37 @@ class DecisionTree private(val strategy: Strategy) extends Serializable with Log
     nodes(nodeIndex) = node
   }
 
+  /**
+   *  Extract the decision tree node information for the children of the node
+   */
   private def extractInfoForLowerLevels(
       level: Int,
       index: Int,
       maxDepth: Int,
       nodeSplitStats: (Split, InformationGainStats),
       parentImpurities: Array[Double],
-      filters: Array[List[Filter]]) {
+      filters: Array[List[Filter]])
+    : Unit = {
 
+    // 0 corresponds to the left child node and 1 corresponds to the right child node.
     for (i <- 0 to 1) {
-
+     //Calculating the index of the node from the node level and the index at the current level
       val nodeIndex = scala.math.pow(2, level + 1).toInt - 1 + 2 * index + i
-
       if (level < maxDepth - 1) {
-
         val impurity = if (i == 0) {
           nodeSplitStats._2.leftImpurity
         } else {
           nodeSplitStats._2.rightImpurity
         }
-
         logDebug("nodeIndex = " + nodeIndex + ", impurity = " + impurity)
+        //noting the parent impurities
         parentImpurities(nodeIndex) = impurity
+        //noting the parents filters for the child nodes
         val childFilter = new Filter(nodeSplitStats._1, if (i == 0) -1 else 1)
         filters(nodeIndex) = childFilter :: filters((nodeIndex - 1) / 2)
-
         for (filter <- filters(nodeIndex)) {
           logDebug("Filter = " + filter)
         }
-
       }
     }
   }