diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py
index 7cd1b02c4..65d950119 100644
--- a/machine_learning/decision_tree.py
+++ b/machine_learning/decision_tree.py
@@ -4,7 +4,7 @@ Input data set: The input data set must be 1-dimensional with continuous labels.
 Output: The decision tree maps a real number input to a real number output.
 """
 import numpy as np
-
+import doctest
 
 class DecisionTree:
     def __init__(self, depth=5, min_leaf_size=5):
@@ -151,23 +151,32 @@ class TestDecisionTree:
 
         return float(squared_error_sum / labels.size)
 
-
 def main():
     """
-    In this demonstration we're generating a sample data set from the sin function in
-    numpy.  We then train a decision tree on the data set and use the decision tree to
-    predict the label of 10 different test values. Then the mean squared error over
-    this test is displayed.
+    In this demonstration first we are generating x which is a numpy array containing values starting 
+    from -1 to 1 with an interval of 0.005 i.e [-1,-0.995,....,0.995,1] this is what we are 
+    getting by applying arange function of numpy.Then the we are generating y by applying sin function 
+    on x which is an array containing values from -1 to 1 with difference of 0.005 i.e we are getting 
+    an array y which contains sin of each value of x. We then train a decision tree on the data set 
+    and use the decision tree to predict the label of 10 different test values. Here we should prefer 
+    calculating Root Mean Squared Error over Mean Sqaured error beacause RMSE should be used 
+    when you need to communicate your results in an understandable  way to end users or when 
+    penalising outliers is less of a priority.Interpretation will be easy in this case.
     """
     x = np.arange(-1.0, 1.0, 0.005)
     y = np.sin(x)
 
     tree = DecisionTree(depth=10, min_leaf_size=10)
     tree.train(x, y)
-
+    
     test_cases = (np.random.rand(10) * 2) - 1
     predictions = np.array([tree.predict(x) for x in test_cases])
-    avg_error = np.mean((predictions - test_cases) ** 2)
+    mse = mean_squared_error(y_true, y_pred)
+    
+    mse_error = np.mean((predictions - test_cases) ** 2)
+    
+    """RMSE error"""
+    avg_error = np.sqrt(avg_error)
 
     print("Test values: " + str(test_cases))
     print("Predictions: " + str(predictions))
@@ -176,6 +185,5 @@ def main():
 
 if __name__ == "__main__":
     main()
-    import doctest
-
+    
     doctest.testmod(name="mean_squarred_error", verbose=True)