sklearn.model_selection.RepeatedStratifiedKFold

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.

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 : None, int or RandomState, default=None Random state to be used to generate random state for each repetition.

See also

RepeatedKFold

Repeats K-Fold n times.

Examples

>>> 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]

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) [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. 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) [source]

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.
Returns:	train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split.