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 i, (train_index, test_index) in enumerate(lpo.split(X)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}")
...     print(f"  Test:  index={test_index}")
Fold 0:
Train: index=[2 3]
Test:  index=[0 1]
Fold 1:
Train: index=[1 3]
Test:  index=[0 2]
Fold 2:
Train: index=[1 2]
Test:  index=[0 3]
Fold 3:
Train: index=[0 3]
Test:  index=[1 2]
Fold 4:
Train: index=[0 2]
Test:  index=[1 3]
Fold 5:
Train: index=[0 1]
Test:  index=[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.
get_n_splits(X, y=None, groups=None)[source]

Returns the number of splitting iterations in the cross-validator

Parameters:
Xarray-like of 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(X, y=None, groups=None)[source]

Generate indices to split data into training and test set.

Parameters:
Xarray-like of 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 shape (n_samples,)

The target variable for supervised learning problems.

groupsarray-like of shape (n_samples,), default=None

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.