9.13.4. sklearn.cross_validation.StratifiedKFold¶
- class sklearn.cross_validation.StratifiedKFold(y, k, indices=False)¶
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 False) :
Return train/test split with integer indices or boolean mask. Integer indices are useful when dealing with sparse matrices that 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: [False True False True] TEST: [ True False True False] TRAIN: [ True False True False] TEST: [False True False True]
- __init__(y, k, indices=False)¶