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DOC Clarify random_state parameter in KFold() doc #5201

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Sep 2, 2015
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DOC Clarify random_state parameter in KFold() doc #5201

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10 changes: 5 additions & 5 deletions sklearn/cross_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -253,7 +253,7 @@ 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.
Expand All @@ -272,8 +272,8 @@ 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
--------
Expand Down Expand Up @@ -362,8 +362,8 @@ 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
--------
Expand Down