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_splitsint, default=5

Number of folds. Must be at least 2.

n_repeatsint, default=10

Number of times cross-validator needs to be repeated.

random_stateint or RandomState instance, default=None

Controls the generation of the random states for each repetition. Pass an int for reproducible output across multiple function calls. See Glossary.

See also

RepeatedKFold

Repeats 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

>>> import numpy as np
>>> 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]¶

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

get_n_splits(X=None, y=None, groups=None)[source]¶

Returns the number of splitting iterations in the cross-validator

Parameters

Xobject

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

yobject

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

groupsarray-like of shape (n_samples,), default=None

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

Returns

n_splitsint

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

Xarray-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

yarray-like of length n_samples

The target variable for supervised learning problems.

groupsarray-like of shape (n_samples,), default=None

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

Yields

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.