`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 train_index, test_index in loo.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]
...     print(X_train, X_test, y_train, y_test)
TRAIN: [1] TEST: [0]
[[3 4]] [[1 2]] [2] [1]
TRAIN: [0] TEST: [1]
[[1 2]] [[3 4]] [1] [2]

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, /, *args, **kwargs)¶: 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.

Returns

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

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.

sklearn.model_selection.LeaveOneOut¶

`sklearn.model_selection`.LeaveOneOut¶