sklearn.model_selection.StratifiedKFold  
class sklearn.model_selection.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None) [source]
 
Stratified K-Folds 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.  Parameters 
 
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  
RepeatedStratifiedKFold


Repeats Stratified K-Fold n times.    Notes 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.  Examples >>> 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)
2
>>> print(skf)
StratifiedKFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in skf.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 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]) Generate indices to split data into training and test set.    
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.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Returns 
 
n_splitsint 

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.  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. 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.  
groupsobject 

Always ignored, exists for compatibility.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.     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 using sklearn.model_selection.StratifiedKFold
 
  Recursive feature elimination with cross-validation  

  Test with permutations the significance of a classification score  

  GMM covariances  

  Receiver Operating Characteristic (ROC) with cross validation  

  Visualizing cross-validation behavior in scikit-learn  

  Effect of varying threshold for self-training