This documentation is for scikit-learn version 0.15-gitOther versions

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sklearn.cross_validation.KFold¶

class sklearn.cross_validation.KFold(n, n_folds=3, indices=None, shuffle=False, random_state=None)

K-Folds cross validation iterator.

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

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

Parameters: n : int Total number of elements. 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 Pseudo-random number generator state used for random sampling. If None, use default numpy RNG for shuffling

StratifiedKFold
take label information into account to avoid building

folds, classification

Notes

The first n % n_folds folds have size n // n_folds + 1, other folds have size n // n_folds.

Examples

```>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = cross_validation.KFold(4, n_folds=2)
>>> len(kf)
2
>>> print(kf)
sklearn.cross_validation.KFold(n=4, n_folds=2, shuffle=False,
random_state=None)
>>> for train_index, test_index in kf:
...    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]
.. automethod:: __init__
```