Merge pull request scikit-learn#5201 from christophebourguignat/master

DOC Clarify random_state parameter in KFold() doc
kykosic · Sep 2, 2015 · e2e5fbd · e2e5fbd
2 parents 364825a + b10e78e
commit e2e5fbd
Showing 1 changed file with 5 additions and 5 deletions.
diff --git a/sklearn/cross_validation.py b/sklearn/cross_validation.py
@@ -254,7 +254,7 @@ class KFold(_BaseKFold):
     """K-Folds cross validation iterator.
 
     Provides train/test indices to split data in train test sets. Split
-    dataset into k consecutive folds (without shuffling).
+    dataset into k consecutive folds (without shuffling by default).
 
     Each fold is then used a validation set once while the k - 1 remaining
     fold form the training set.
@@ -273,8 +273,8 @@ class KFold(_BaseKFold):
         Whether to shuffle the data before splitting into batches.
 
     random_state : None, int or RandomState
-        Pseudo-random number generator state used for random
-        sampling. If None, use default numpy RNG for shuffling
+        When shuffle=True, pseudo-random number generator state used for 
+        shuffling. If None, use default numpy RNG for shuffling.
 
     Examples
     --------
@@ -363,8 +363,8 @@ class StratifiedKFold(_BaseKFold):
         into batches.
 
     random_state : None, int or RandomState
-        Pseudo-random number generator state used for random
-        sampling. If None, use default numpy RNG for shuffling
+        When shuffle=True, pseudo-random number generator state used for 
+        shuffling. If None, use default numpy RNG for shuffling.
 
     Examples
     --------