LeaveOneOut#

classsklearn.model_selection.LeaveOneOut[source]#

Leave-One-Out cross-validator.

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

Note:LeaveOneOut() is equivalent toKFold(n_splits=n) andLeavePOut(p=1) wheren is the number of samples.

Due to the high number of test sets (which is the same as thenumber of samples) this cross-validation method can be very costly.For large datasets one should favorKFold,ShuffleSplitorStratifiedKFold.

Read more in theUser 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

>>>importnumpyasnp>>>fromsklearn.model_selectionimportLeaveOneOut>>>X=np.array([[1,2],[3,4]])>>>y=np.array([1,2])>>>loo=LeaveOneOut()>>>loo.get_n_splits(X)2>>>print(loo)LeaveOneOut()>>>fori,(train_index,test_index)inenumerate(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 checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting 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, wheren_samples is the number of samplesandn_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, wheren_samples is the number of samplesandn_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.