class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)
[source]
K-Folds cross-validator
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 once as a validation while the k - 1 remaining folds form the training set.
Read more in the User Guide.
Parameters: |
n_splits : 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 : None, int or RandomState When shuffle=True, pseudo-random number generator state used for shuffling. If None, use default numpy RNG for shuffling. |
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See also
StratifiedKFold
GroupKFold
The first n_samples % n_splits
folds have size n_samples // n_splits + 1
, other folds have size n_samples // n_splits
, where n_samples
is the number of samples.
>>> from sklearn.model_selection import KFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4]) >>> kf = KFold(n_splits=2) >>> kf.get_n_splits(X) 2 >>> print(kf) KFold(n_splits=2, random_state=None, shuffle=False) >>> for train_index, test_index in kf.split(X): ... 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]
get_n_splits ([X, y, groups]) | Returns the number of splitting iterations in the cross-validator |
split (X[, y, groups]) | Generate indices to split data into training and test set. |
__init__(n_splits=3, shuffle=False, random_state=None)
[source]
get_n_splits(X=None, y=None, groups=None)
[source]
Returns the number of splitting iterations in the cross-validator
Parameters: |
X : object Always ignored, exists for compatibility. y : object Always ignored, exists for compatibility. groups : object Always ignored, exists for compatibility. |
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Returns: |
n_splits : int Returns the number of splitting iterations in the cross-validator. |
split(X, y=None, groups=None)
[source]
Generate indices to split data into training and test set.
Parameters: |
X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) The target variable for supervised learning problems. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. |
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Returns: |
train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split. |
sklearn.model_selection.KFold
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html