sklearn.cross_validation.ShuffleSplit

class sklearn.cross_validation.ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None) [source]

Random permutation cross-validation iterator.

Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.ShuffleSplit instead.

Yields indices to split data into training and test sets.

Note: contrary to other cross-validation strategies, random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.

Read more in the User Guide.

Parameters:

n : int Total number of elements in the dataset. n_iter : int (default 10) Number of re-shuffling & splitting iterations. test_size : float (default 0.1), int, or None If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. train_size : float, int, or None (default is None) If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size. 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.

Examples

>>> from sklearn import cross_validation
>>> rs = cross_validation.ShuffleSplit(4, n_iter=3,
...     test_size=.25, random_state=0)
>>> len(rs)
3
>>> print(rs)
... 
ShuffleSplit(4, n_iter=3, test_size=0.25, ...)
>>> for train_index, test_index in rs:
...    print("TRAIN:", train_index, "TEST:", test_index)
...
TRAIN: [3 1 0] TEST: [2]
TRAIN: [2 1 3] TEST: [0]
TRAIN: [0 2 1] TEST: [3]

>>> rs = cross_validation.ShuffleSplit(4, n_iter=3,
...     train_size=0.5, test_size=.25, random_state=0)
>>> for train_index, test_index in rs:
...    print("TRAIN:", train_index, "TEST:", test_index)
...
TRAIN: [3 1] TEST: [2]
TRAIN: [2 1] TEST: [0]
TRAIN: [0 2] TEST: [3]
.. automethod:: __init__