Fork me on GitHub


class sklearn.cross_validation.LeaveOneOut(n, indices=True)

Leave-One-Out cross validation iterator.

Provides train/test indices to split data in train test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set.

Note: LeaveOneOut(n) is equivalent to KFold(n, n_folds=n) and LeavePOut(n, p=1).

Due to the high number of test sets (which is the same as 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.

indices : boolean, optional (default True)

Return train/test split as arrays of indices, rather than a boolean mask array. Integer indices are required when dealing with sparse matrices, since those cannot be indexed by boolean masks.

See also

LeaveOneLabelOut, domain-specific


>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4]])
>>> y = np.array([1, 2])
>>> loo = cross_validation.LeaveOneOut(2)
>>> len(loo)
>>> print(loo)
>>> for train_index, test_index in loo:
...    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]
...    print(X_train, X_test, y_train, y_test)
TRAIN: [1] TEST: [0]
[[3 4]] [[1 2]] [2] [1]
TRAIN: [0] TEST: [1]
[[1 2]] [[3 4]] [1] [2]
__init__(n, indices=True)