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_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. shuffleboolean, optional
Whether to shuffle each class’s samples before splitting into batches.
 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
. Used whenshuffle
== True.
See also
RepeatedStratifiedKFold
Repeats Stratified KFold 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"]
toy = [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
(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.

__init__
(self, n_splits=5, shuffle=False, 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.
 yobject
Always ignored, exists for compatibility.
 groupsobject
Always ignored, exists for compatibility.
 Returns
 n_splitsint
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
 Xarraylike, 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. yarraylike, 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.