sklearn.model_selection.StratifiedKFold

class sklearn.model_selection.StratifiedKFold(n_splits='warn', 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_splits : int, default=3

Number of folds. Must be at least 2.

Changed in version 0.20: n_splits default value will change from 3 to 5 in v0.22.

shuffle : boolean, optional

Whether to shuffle each stratification of the data 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 when shuffle == True.

See also

RepeatedStratifiedKFold
Repeats Stratified K-Fold n times.

Notes

Train and test sizes may be different in each fold, with a difference of at most n_classes.

Examples

>>> 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)  # doctest: +NORMALIZE_WHITESPACE
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.
__init__(n_splits='warn', shuffle=False, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

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

Returns the number of splitting iterations in the cross-validator

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 cross-validator.
split(X, y, groups=None)[source]

Generate indices to split data into training and test set.

Parameters:
X : array-like, 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.

y : array-like, 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.