- class sklearn.cross_validation.LeavePOut(n, p, indices=True)¶
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.
n : int
Total number of elements in dataset.
p : int
Size of the test sets.
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.
>>> 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]
- __init__(n, p, indices=True)¶