sklearn.cross_validation.LabelShuffleSplit

class sklearn.cross_validation.LabelShuffleSplit(labels, n_iter=5, test_size=0.2, train_size=None, random_state=None) [source]

Shuffle-Labels-Out cross-validation iterator

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

Provides randomized train/test indices to split data according to a third-party provided label. This label information can be used to encode arbitrary domain specific stratifications of the samples as integers.

For instance the labels could be the year of collection of the samples and thus allow for cross-validation against time-based splits.

The difference between LeavePLabelOut and LabelShuffleSplit is that the former generates splits using all subsets of size p unique labels, whereas LabelShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique labels.

For example, a less computationally intensive alternative to LeavePLabelOut(labels, p=10) would be LabelShuffleSplit(labels, test_size=10, n_iter=100).

Note: The parameters test_size and train_size refer to labels, and not to samples, as in ShuffleSplit.

New in version 0.17.

Parameters:

labels : array, [n_samples] Labels of samples n_iter : int (default 5) Number of re-shuffling and splitting iterations. test_size : float (default 0.2), int, or None If float, should be between 0.0 and 1.0 and represent the proportion of the labels to include in the test split. If int, represents the absolute number of test labels. 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 labels to include in the train split. If int, represents the absolute number of train labels. 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.

__init__(labels, n_iter=5, test_size=0.2, train_size=None, random_state=None) [source]