sklearn.cross_validation.LeavePOut

class sklearn.cross_validation.LeavePOut(n, p, indices=None)[source]

Leave-P-Out cross validation iterator

Provides train/test indices to split data in train test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration.

Note: LeavePOut(n, p) is NOT equivalent to KFold(n, n_folds=n // p) which creates non-overlapping test sets.

Due to the high number of iterations which grows combinatorically with the number of samples this cross validation method can be very costly. For large datasets one should favor KFold, StratifiedKFold or ShuffleSplit.

Parameters:

n : int

Total number of elements in dataset.

p : int

Size of the test sets.

Examples

>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> lpo = cross_validation.LeavePOut(4, 2)
>>> len(lpo)
6
>>> print(lpo)
sklearn.cross_validation.LeavePOut(n=4, p=2)
>>> for train_index, test_index in lpo:
...    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: [2 3] TEST: [0 1]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
TRAIN: [0 2] TEST: [1 3]
TRAIN: [0 1] TEST: [2 3]
.. automethod:: __init__