tabs and whitespaces

Author: najeeb-kazmi

src/python/docs/docstrings/Dart.txt

@@ -7,9 +7,9 @@
         <https://arxiv.org/abs/1505.01866>`_ is an
         ensemble method of boosted regression trees. The Dropouts meet
         Multiple Additive Regression
-	    Trees (DART) employs dropouts in MART and overcomes the issues of over-
+        Trees (DART) employs dropouts in MART and overcomes the issues of over-
         specialization of MART,
-	    achiving better performance in many tasks.
+        achieving better performance in many tasks.
 
 
         **Reference**

src/python/docs/docstrings/FastLinearClassifier.txt

@@ -1,7 +1,7 @@
     """
 
     A Stochastic Dual Coordinate Ascent (SDCA) optimization trainer for
-	multi class classification.
+    multi class classification.
 
     .. remarks::
         ``FastLinearClassifier`` is a trainer based on the Stochastic Dual

src/python/docs/docstrings/Goss.txt

@@ -5,9 +5,9 @@
     .. remarks::
         Gradient-based One-Side Sampling (GOSS) employs an adaptive sampling
         named gradient-based
-	    sampling. For datasets with large sample size, GOSS has considerable
+        sampling. For datasets with large sample size, GOSS has considerable
         advantage in terms of
-	    statistical and computational efficiency.	
+        statistical and computational efficiency.
 
 
 

src/python/nimbusml/ensemble/booster/dart.py

@@ -26,7 +26,7 @@ class Dart(core):
         Multiple Additive Regression
         Trees (DART) employs dropouts in MART and overcomes the issues of over-
         specialization of MART,
-        achiving better performance in many tasks.
+        achieving better performance in many tasks.
 
 
         **Reference**

src/python/nimbusml/internal/core/ensemble/booster/dart.py

@@ -27,7 +27,7 @@ class Dart(Component):
         Multiple Additive Regression
         Trees (DART) employs dropouts in MART and overcomes the issues of over-
         specialization of MART,
-        achiving better performance in many tasks.
+        achieving better performance in many tasks.
 
 
         **Reference**