scikits.learn.cross_val.LeavePOut

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

Leave-P-Out cross validation iterator

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

__init__(n, p, indices=False)

Leave-P-Out cross validation iterator

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

Parameters :

n: int :

Total number of elements

p: int :

Size test sets

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], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> lpo = cross_val.LeavePOut(4, 2)
>>> len(lpo)
6
>>> print lpo
scikits.learn.cross_val.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: [False False  True  True] TEST: [ True  True False False]
TRAIN: [False  True False  True] TEST: [ True False  True False]
TRAIN: [False  True  True False] TEST: [ True False False  True]
TRAIN: [ True False False  True] TEST: [False  True  True False]
TRAIN: [ True False  True False] TEST: [False  True False  True]
TRAIN: [ True  True False False] TEST: [False False  True  True]