class sklearn.model_selection.LeavePOut(p)
[source]
Leave-P-Out cross-validator
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(p)
is NOT equivalent to KFold(n_splits=n_samples // 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
.
Read more in the User Guide.
Parameters: |
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>>> from sklearn.model_selection import LeavePOut >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) >>> y = np.array([1, 2, 3, 4]) >>> lpo = LeavePOut(2) >>> lpo.get_n_splits(X) 6 >>> print(lpo) LeavePOut(p=2) >>> for train_index, test_index in lpo.split(X): ... 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]
get_n_splits (X[, y, groups]) | Returns the number of splitting iterations in the cross-validator |
split (X[, y, groups]) | Generate indices to split data into training and test set. |
__init__(p)
[source]
get_n_splits(X, y=None, groups=None)
[source]
Returns the number of splitting iterations in the cross-validator
Parameters: |
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split(X, y=None, groups=None)
[source]
Generate indices to split data into training and test set.
Parameters: |
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Yields: |
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html