sklearn.model_selection.RepeatedKFold

class sklearn.model_selection.RepeatedKFold(n_splits=5, n_repeats=10, random_state=None)

Repeated K-Fold cross validator.

Repeats K-Fold n times with different randomization in each repetition.

Read more in the User Guide.

Parameters:

n_splits : int, default=5

Number of folds. Must be at least 2.

n_repeats : int, default=10

Number of times cross-validator needs to be repeated.

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 np.random.

See also

RepeatedStratifiedKFold

Repeats Stratified K-Fold n times.

Notes

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.

Examples

>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> for train_index, test_index in rkf.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: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]

Methods

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)

Initialize self. See help(type(self)) for accurate signature.

get_n_splits(X=None, y=None, groups=None)

Returns the number of splitting iterations in the cross-validator

Parameters:

X : object

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.

y : object

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.

groups : array-like, with shape (n_samples,), optional

Group labels for the samples used while splitting the dataset into train/test set.

Returns:

n_splits : int

Returns the number of splitting iterations in the cross-validator.

split(X, y=None, groups=None)

Generates 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, of length 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.

Yields:

train : ndarray

The training set indices for that split.

test : ndarray

The testing set indices for that split.