# sklearn.model_selection.LeavePOut¶

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: p : int Size of the test sets.

Examples

>>> 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]


Methods

 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: X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. y : object Always ignored, exists for compatibility. groups : object Always ignored, exists for compatibility.
split(X, y=None, groups=None)[source]

Generate indices to split data into training and test set.

Parameters: X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. y : array-like, of length n_samples The target variable for supervised learning problems. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split.