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]

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.
groupsobject: Always ignored, exists for compatibility.

Yields:

trainndarray: The training set indices for that split.
testndarray: The testing set indices for that split.