sklearn.model_selection.KFold

class sklearn.model_selection.KFold(n_folds=3, shuffle=False, random_state=None)

K-Folds cross-validator

Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

Each fold is then used once as a validation while the k - 1 remaining folds form the training set.

Read more in the User Guide.

Parameters:

n_folds : int, default=3

Number of folds. Must be at least 2.

shuffle : boolean, optional

Whether to shuffle 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.

See also

StratifiedKFold

Takes label information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).

LabelKFold

K-fold iterator variant with non-overlapping labels.

Notes

The first n_samples % n_folds folds have size n_samples // n_folds + 1, other folds have size n_samples // n_folds, where n_samples is the number of samples.

Examples

>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_folds=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)  
KFold(n_folds=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
...    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: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]

Methods

get_n_splits([X, y, labels])	Returns the number of splitting iterations in the cross-validator
split(X[, y, labels])	Generate indices to split data into training and test set.

__init__(n_folds=3, shuffle=False, random_state=None)

get_n_splits(X=None, y=None, labels=None)

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.

labels : object

Always ignored, exists for compatibility.

Returns:

n_splits : int

Returns the number of splitting iterations in the cross-validator.

split(X, y=None, labels=None)

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.

y : array-like, shape (n_samples,), optional

The target variable for supervised learning problems.

labels : array-like, with shape (n_samples,), optional

Group labels for the samples used while splitting the dataset into train/test set.

Returns:

train : ndarray

The training set indices for that split.

test : ndarray

The testing set indices for that split.

Examples using sklearn.model_selection.KFold

Feature agglomeration vs. univariate selection

Gradient Boosting Out-of-Bag estimates

Cross-validation on diabetes Dataset Exercise