StratifiedKFold(y, n_folds=3, shuffle=False, random_state=None)¶
Stratified K-Folds cross validation iterator
Deprecated since version 0.18: This module will be removed in 0.20. Use
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 : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when
- 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__