LeavePOut (original) (raw)

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 toKFold(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, StratifiedKFoldor 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.