Warning
DEPRECATED
class sklearn.cross_validation.KFold(n, n_folds=3, shuffle=False, random_state=None)
[source]
K-Folds cross validation iterator.
Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.KFold
instead.
Provides train/test indices to split data in train test sets. Split dataset into k consecutive folds (without shuffling by default).
Each fold is then used as a validation set once while the k - 1 remaining fold(s) form the training set.
Read more in the User Guide.
Parameters: |
n : int Total number of elements. n_folds : int, default=3 Number of folds. Must be at least 2. shuffle : boolean, optional Whether to shuffle the data before splitting into batches. random_state : int, RandomState instance or None, optional, default=None If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by |
---|
See also
StratifiedKFold
, folds
, classification
LabelKFold
The first n % n_folds folds have size n // n_folds + 1, other folds have size n // n_folds.
>>> from sklearn.cross_validation import KFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4]) >>> kf = KFold(4, n_folds=2) >>> len(kf) 2 >>> print(kf) sklearn.cross_validation.KFold(n=4, n_folds=2, shuffle=False, random_state=None) >>> for train_index, test_index in kf: ... 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: [2 3] TEST: [0 1] TRAIN: [0 1] TEST: [2 3] .. automethod:: __init__
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Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html