`sklearn.model_selection`.StratifiedKFold¶

class sklearn.model_selection.StratifiedKFold(n_splits=3, 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.

shuffle : boolean, optional

Whether to shuffle each stratification of the data before splitting into batches.

random_state : None, int or RandomState

When shuffle=True, pseudo-random number generator state used for shuffling. If None, use default numpy RNG for shuffling.

Notes

All the folds have size trunc(n_samples / n_splits), the last one has the complementary.

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=3, 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:

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,)