class sklearn.model_selection.RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=None)
[source]
Repeated Stratified K-Fold cross validator.
Repeats Stratified K-Fold n times with different randomization in each repetition.
Read more in the User Guide.
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See also
RepeatedKFold
Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state
to an integer.
>>> from sklearn.model_selection import RepeatedStratifiedKFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=2, ... random_state=36851234) >>> for train_index, test_index in rskf.split(X, y): ... print("TRAIN:", train_index, "TEST:", test_index) ... X_train, X_test = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_index] ... TRAIN: [1 2] TEST: [0 3] TRAIN: [0 3] TEST: [1 2] TRAIN: [1 3] TEST: [0 2] TRAIN: [0 2] TEST: [1 3]
get_n_splits ([X, y, groups]) | Returns the number of splitting iterations in the cross-validator |
split (X[, y, groups]) | Generates indices to split data into training and test set. |
__init__(n_splits=5, n_repeats=10, random_state=None)
[source]
get_n_splits(X=None, y=None, groups=None)
[source]
Returns the number of splitting iterations in the cross-validator
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Returns: |
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split(X, y=None, groups=None)
[source]
Generates indices to split data into training and test set.
Parameters: |
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Yields: |
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html