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_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, RandomState instance or None, default=None

Controls the randomness of each repeated cross-validation instance. Pass an int for reproducible output across multiple function calls. See Glossary.

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

>>> import numpy as np
>>> 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)
>>> rkf.get_n_splits(X, y)
4
>>> print(rkf)
RepeatedKFold(n_repeats=2, n_splits=2, random_state=2652124)
>>> for i, (train_index, test_index) in enumerate(rkf.split(X)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}")
...     print(f"  test:  index={test_index}")
...
Fold 0:
  Train: index=[0 1]
  test:  index=[2 3]
Fold 1:
  Train: index=[2 3]
  test:  index=[0 1]
Fold 2:
  Train: index=[1 2]
  test:  index=[0 3]
Fold 3:
  Train: index=[0 3]
  test:  index=[1 2]

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

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

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)

Generate indices to split data into training and test set.

Parameters:

Xarray-like of 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 shape (n_samples,)

The target variable for supervised learning problems.

groupsobject

Always ignored, exists for compatibility.

Yields:

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.

Gallery examples

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models