diff --git a/docs/source/machine_learning.rst b/docs/source/machine_learning.rst
index 41a1353f6..45606b7c6 100644
--- a/docs/source/machine_learning.rst
+++ b/docs/source/machine_learning.rst
@@ -75,34 +75,35 @@ create and train a model from SQL:
         .. code-block:: sql
 
             CREATE MODEL my_model WITH (
-                model_class = 'sklearn.ensemble.GradientBoostingClassifier',
+                model_class = 'sklearn.linear_model.LogisticRegression',
                 wrap_predict = True,
                 target_column = 'target'
             ) AS (
-                SELECT x, y, target
+                SELECT x, y, x*y > 0 as target
                 FROM timeseries
                 LIMIT 100
             )
 
     .. group-tab:: GPU
-
+        
         .. code-block:: sql
 
             CREATE MODEL my_model WITH (
                 model_class = 'cuml.linear_model.LogisticRegression',
                 wrap_predict = True,
-                wrap_fit = False,  -- can we explain why this needs to be added?
                 target_column = 'target'
             ) AS (
-                SELECT x, y, target
+                SELECT x, y, x*y > 0 as target
                 FROM timeseries
                 LIMIT 100
             )
 
-This call will create a new instance of ``sklearn.ensemble.GradientBoostingClassifier``
+This call will create a new instance of ``linear_model.LogisticRegression`` 
 and train it with the data collected from the ``SELECT`` call (again, every valid ``SELECT``
 query can be given). The model can than be used in subsequent calls to ``PREDICT``
-using the given name.
+using the given name.  
+We set ``wrap_predict`` = ``True`` here to parallelize post fit prediction task of non distributed models (sklearn/cuML etc) across workers.  
+
 Have a look into :ref:`ml` for more information.
 
 4. Check Model parameters - Model meta data