LeaveOneOut (original) (raw)
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) andLeavePOut(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, ShuffleSplitor StratifiedKFold.
Read more in the User Guide.
See also
For splitting the data according to explicit, domain-specific stratification of the dataset.
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]
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.