6.12.1. scikits.learn.cross_val.LeaveOneOut

class scikits.learn.cross_val.LeaveOneOut(n, indices=False)

Leave-One-Out cross validation iterator

Provides train/test indices to split data in train test sets

__init__(n, indices=False)

Leave-One-Out cross validation iterator

Provides train/test indices to split data in train test sets

Parameters :

n: int :

Total number of elements

indices: boolean, optional (default False) :

Return train/test split with integer indices or boolean mask. Integer indices are useful when dealing with sparse matrices that cannot be indexed by boolean masks.

Examples

>>> from scikits.learn import cross_val
>>> X = np.array([[1, 2], [3, 4]])
>>> y = np.array([1, 2])
>>> loo = cross_val.LeaveOneOut(2)
>>> len(loo)
2
>>> print loo
scikits.learn.cross_val.LeaveOneOut(n=2)
>>> 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: [False  True] TEST: [ True False]
[[3 4]] [[1 2]] [2] [1]
TRAIN: [ True False] TEST: [False  True]
[[1 2]] [[3 4]] [1] [2]