8.3.7. sklearn.cross_validation.StratifiedKFold

class sklearn.cross_validation.StratifiedKFold(y, k, indices=True)

Stratified K-Folds cross validation iterator

Provides train/test indices to split data in train test sets.

This cross-validation object is a variation of KFold, which returns stratified folds. The folds are made by preserving the percentage of samples for each class.

Parameters :

y: array, [n_samples] :

Samples to split in K folds

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.

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([0, 0, 1, 1])
>>> skf = cross_validation.StratifiedKFold(y, k=2)
>>> len(skf)
2
>>> print skf
sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], k=2)
>>> for train_index, test_index in skf:
...    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]

__init__(y, k, indices=True)