6.11.4. scikits.learn.cross_val.StratifiedKFold

class scikits.learn.cross_val.StratifiedKFold(y, k)

Stratified K-Folds cross validation iterator

Provides train/test indexes 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.

__init__(y, k)

K-Folds cross validation iterator

Provides train/test indexes to split data in train test sets

Parameters :

y: array, [n_samples] :

Samples to split in K folds

k: int :

number of folds

Notes

All the folds have size trunc(n/k), the last one has the complementary

Examples

>>> from scikits.learn import cross_val
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = cross_val.StratifiedKFold(y, k=2)
>>> len(skf)
2
>>> print skf
scikits.learn.cross_val.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: [False  True False  True] TEST: [ True False  True False]
TRAIN: [ True False  True False] TEST: [False  True False  True]