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 a9a578c4ac262..89a3f6de4fcb5 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
@@ -29,9 +29,21 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.Algo._
 
+/*
+A class that implements a decision tree algorithm for classification and regression.
+It supports both continuous and categorical features.
 
+@param strategy The configuration parameters for the tree algorithm which specify the type of algorithm (classification,
+regression, etc.), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.
+ */
 class DecisionTree(val strategy : Strategy) extends Serializable with Logging {
 
+  /*
+  Method to train a decision tree model over an RDD
+
+  @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data for DecisionTree
+  @return a DecisionTreeModel that can be used for prediction
+   */
   def train(input : RDD[LabeledPoint]) : DecisionTreeModel = {
 
     //Cache input RDD for speedup during multiple passes