`sklearn.model_selection`.LeaveOneOut#

class sklearn.model_selection.LeaveOneOut[source]#

Leave-One-Out cross-validator.

Provides train/test indices to split data in train/test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set.

Note: LeaveOneOut() is equivalent to KFold(n_splits=n) and LeavePOut(p=1) where n is the number of samples.

Due to the high number of test sets (which is the same as the number of samples) this cross-validation method can be very costly. For large datasets one should favor KFold, ShuffleSplit or StratifiedKFold.

See also

LeaveOneGroupOut: For splitting the data according to explicit, domain-specific stratification of the dataset.
GroupKFold: K-fold iterator variant with non-overlapping groups.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import LeaveOneOut
>>> X = np.array([[1, 2], [3, 4]])
>>> y = np.array([1, 2])
>>> loo = LeaveOneOut()
>>> loo.get_n_splits(X)
2
>>> print(loo)
LeaveOneOut()
>>> for i, (train_index, test_index) in enumerate(loo.split(X)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}")
...     print(f"  Test:  index={test_index}")
Fold 0:
  Train: index=[1]
  Test:  index=[0]
Fold 1:
  Train: index=[0]
  Test:  index=[1]

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`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_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.

Returns:

n_splitsint: Returns the number of splitting iterations in the cross-validator.

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.

sklearn.model_selection.LeaveOneOut#

`sklearn.model_selection`.LeaveOneOut#