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
.
Read more in the User Guide.
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
| Returns the number of splitting iterations in the cross-validator |
| Generate indices to split data into training and test set. |
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.
Always ignored, exists for compatibility.
groupsobjectAlways ignored, exists for compatibility.
Returns:n_splitsintReturns the number of splitting iterations in the cross-validator.
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.
The target variable for supervised learning problems.
groupsarray-like of shape [n_samples,], default=NoneGroup labels for the samples used while splitting the dataset into train/test set.
Yields:trainndarrayThe training set indices for that split.
testndarrayThe testing set indices for that split.