StratifiedKFold(y, n_folds=3, shuffle=False, random_state=None)¶
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 that returns stratified folds. The folds are made by preserving the percentage of samples for each class.
Read more in the User Guide.
y : array-like, [n_samples]
Samples to split in K folds.
n_folds : int, default=3
Number of folds. Must be at least 2.
shuffle : boolean, optional
Whether to shuffle each stratification of the data before splitting into batches.
random_state : None, int or RandomState
When shuffle=True, pseudo-random number generator state used for shuffling. If None, use default numpy RNG for shuffling.
- K-fold iterator variant with non-overlapping labels.
All the folds have size trunc(n_samples / n_folds), the last one has the complementary.
>>> from sklearn.cross_validation import StratifiedKFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> skf = StratifiedKFold(y, n_folds=2) >>> len(skf) 2 >>> print(skf) sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2, shuffle=False, random_state=None) >>> 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] .. automethod:: __init__