8.3.2. sklearn.cross_validation.KFold

class sklearn.cross_validation.KFold(n, k, indices=True, 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

k: int :

Number of folds

indices: boolean, optional (default True) :

Return train/test split as arrays of indices, rather than a boolean mask array. Integer indices are required when dealing with sparse matrices, since those cannot be indexed by boolean masks.

shuffle: boolean, optional :

whether to shuffle the data before splitting into batches

random_state: int or RandomState :

Pseudo number generator state used for random sampling.

See also

StratifiedKFold
take label information into account to avoid building

folds, classification

Notes

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

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, k=2)
>>> len(kf)
2
>>> print kf
sklearn.cross_validation.KFold(n=4, k=2)
>>> 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]
__init__(n, k, indices=True, shuffle=False, random_state=None)
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