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

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]
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.