# 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
pint

Size of the test sets. Must be strictly less than the number of samples.

Examples

>>> import numpy as np
>>> 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(self, X[, y, groups]) Returns the number of splitting iterations in the cross-validator split(self, X[, y, groups]) Generate indices to split data into training and test set.
__init__(self, p)[source]

Initialize self. See help(type(self)) for accurate signature.

get_n_splits(self, X, y=None, groups=None)[source]

Returns the number of splitting iterations in the cross-validator

Parameters
Xarray-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

yobject

Always ignored, exists for compatibility.

groupsobject

Always ignored, exists for compatibility.

split(self, X, y=None, groups=None)[source]

Generate indices to split data into training and test set.

Parameters
Xarray-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

yarray-like, of length n_samples

The target variable for supervised learning problems.

groupsarray-like, with shape (n_samples,), optional

Group labels for the samples used while splitting the dataset into train/test set.

Yields
trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.