sklearn.model_selection
.StratifiedKFold¶

class
sklearn.model_selection.
StratifiedKFold
(n_splits=5, shuffle=False, random_state=None)[source]¶ Stratified KFolds crossvalidator
Provides train/test indices to split data in train/test sets.
This crossvalidation 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_splits : int, default=5
Number of folds. Must be at least 2.
Changed in version 0.22:
n_splits
default value changed from 3 to 5. shuffle : boolean, optional
Whether to shuffle each class’s samples before splitting into batches.
 random_state : int, 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
. Used whenshuffle
== True.
See also
RepeatedStratifiedKFold
 Repeats Stratified KFold n times.
Notes
Train and test sizes may be different in each fold, with a difference of at most
n_classes
.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
(self[, X, y, groups])Returns the number of splitting iterations in the crossvalidator split
(self, X, y[, groups])Generate indices to split data into training and test set. 
get_n_splits
(self, X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
Parameters:  X : object
Always ignored, exists for compatibility.
 y : object
Always ignored, exists for compatibility.
 groups : object
Always ignored, exists for compatibility.
Returns:  n_splits : int
Returns the number of splitting iterations in the crossvalidator.

split
(self, X, y, groups=None)[source]¶ Generate indices to split data into training and test set.
Parameters:  X : arraylike, 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 hencenp.zeros(n_samples)
may be used as a placeholder forX
instead of actual training data. y : arraylike, shape (n_samples,)
The target variable for supervised learning problems. Stratification is done based on the y labels.
 groups : object
Always ignored, exists for compatibility.
Yields:  train : ndarray
The training set indices for that split.
 test : ndarray
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.