[MRG] Add Explanation of MSE vs Friedman MSE vs MAE criterion in Regression Tree Building (scikit-learn#9367)

warut-vijit · jnothman · commit 565bc393cefc · 2017-08-06T14:20:04.000+10:00
* clarified documentation for regression tree criterion

* added explanation on doc/modules/tree.rst
diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst
@@ -481,7 +481,10 @@ Regression criteria
 
 If the target is a continuous value, then for node :math:`m`,
 representing a region :math:`R_m` with :math:`N_m` observations, common
-criteria to minimise are
+criteria to minimise as for determining locations for future
+splits are Mean Squared Error, which minimizes the L2 error
+using mean values at terminal nodes, and Mean Absolute Error, which 
+minimizes the L1 error using median values at terminal nodes. 
 
 Mean Squared Error:
 
diff --git a/sklearn/tree/tree.py b/sklearn/tree/tree.py
@@ -879,8 +879,11 @@ class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
     criterion : string, optional (default="mse")
         The function to measure the quality of a split. Supported criteria
         are "mse" for the mean squared error, which is equal to variance
-        reduction as feature selection criterion, and "mae" for the mean
-        absolute error.
+        reduction as feature selection criterion and minimizes the L2 loss
+        using the mean of each terminal node, "friedman_mse", which uses mean
+        squared error with Friedman's improvement score for potential splits,
+        and "mae" for the mean absolute error, which minimizes the L1 loss
+        using the median of each terminal node.
 
         .. versionadded:: 0.18
            Mean Absolute Error (MAE) criterion.
