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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.

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.

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)  
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]

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:

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.

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.