sklearn.model_selection
.RepeatedKFold¶

class
sklearn.model_selection.
RepeatedKFold
(n_splits=5, n_repeats=10, random_state=None)[source]¶ Repeated KFold cross validator.
Repeats KFold 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 crossvalidator needs to be repeated.
 random_stateint, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
.
See also
RepeatedStratifiedKFold
Repeats Stratified KFold 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) >>> for train_index, test_index in rkf.split(X): ... 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: [0 1] TEST: [2 3] TRAIN: [2 3] TEST: [0 1] TRAIN: [1 2] TEST: [0 3] TRAIN: [0 3] TEST: [1 2]
Methods
get_n_splits
(self[, X, y, groups])Returns the number of splitting iterations in the crossvalidator
split
(self, X[, y, groups])Generates indices to split data into training and test set.

__init__
(self, n_splits=5, n_repeats=10, random_state=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

get_n_splits
(self, X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
 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. groupsarraylike, with shape (n_samples,), optional
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 crossvalidator.

split
(self, X, y=None, groups=None)[source]¶ Generates indices to split data into training and test set.
 Parameters
 Xarraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 yarraylike, of length n_samples
The target variable for supervised learning problems.
 groupsarraylike, with shape (n_samples,), optional
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.