8.3.4. 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)¶