RepeatedKFold (original) (raw)

class sklearn.model_selection.RepeatedKFold(*, n_splits=5, n_repeats=10, random_state=None)[source]#

Repeated K-Fold cross validator.

Repeats K-Fold n_repeats 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.

Notes

Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_stateto 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() 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()[source]#

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)[source]#

Returns the number of splitting iterations as set with the n_splits param when instantiating the cross-validator.

Parameters:

Xarray-like of shape (n_samples, n_features), default=None

Always ignored, exists for API compatibility.

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

Always ignored, exists for API compatibility.

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

Always ignored, exists for API compatibility.

Returns:

n_splitsint

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

split(X, y=None, groups=None)[source]#

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,), default=None

The target variable for supervised learning problems.

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

Always ignored, exists for API compatibility.

Yields:

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.

Gallery examples#