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]