class sklearn.model_selection.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None)[source]#

Stratified K-Fold cross-validator.

Provides train/test indices to split data in train/test sets.

This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

Read more in the User Guide.

For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn

n_splitsint, default=5

Number of folds. Must be at least 2.

Changed in version 0.22: n_splits default value changed from 3 to 5.

shufflebool, default=False

Whether to shuffle each class’s samples before splitting into batches. Note that the samples within each split will not be shuffled.

random_stateint, RandomState instance or None, default=None

When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold for each class. Otherwise, leave random_state as None. Pass an int for reproducible output across multiple function calls. See Glossary.

See also


Repeats Stratified K-Fold n times.


The implementation is designed to:

  • Generate test sets such that all contain the same distribution of classes, or as close as possible.

  • Be invariant to class label: relabelling y = ["Happy", "Sad"] to y = [1, 0] should not change the indices generated.

  • Preserve order dependencies in the dataset ordering, when shuffle=False: all samples from class k in some test set were contiguous in y, or separated in y by samples from classes other than k.

  • Generate test sets where the smallest and largest differ by at most one sample.

Changed in version 0.22: The previous implementation did not follow the last constraint.


>>> import numpy as np
>>> from sklearn.model_selection import StratifiedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = StratifiedKFold(n_splits=2)
>>> skf.get_n_splits(X, y)
>>> print(skf)
StratifiedKFold(n_splits=2, random_state=None, shuffle=False)
>>> for i, (train_index, test_index) in enumerate(skf.split(X, y)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}")
...     print(f"  Test:  index={test_index}")
Fold 0:
  Train: index=[1 3]
  Test:  index=[0 2]
Fold 1:
  Train: index=[0 2]
  Test:  index=[1 3]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.


A MetadataRequest encapsulating routing information.

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

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


Always ignored, exists for compatibility.


Always ignored, exists for compatibility.


Always ignored, exists for compatibility.


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

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

Generate indices to split data into training and test set.

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.

Note that providing y is sufficient to generate the splits and hence np.zeros(n_samples) may be used as a placeholder for X instead of actual training data.

yarray-like of shape (n_samples,)

The target variable for supervised learning problems. Stratification is done based on the y labels.


Always ignored, exists for compatibility.


The training set indices for that split.


The testing set indices for that split.


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.