LeavePOut#

classsklearn.model_selection.LeavePOut(p)[source]#

Leave-P-Out cross-validator.

Provides train/test indices to split data in train/test sets. This resultsin testing on all distinct samples of size p, while the remaining n - psamples form the training set in each iteration.

Note:LeavePOut(p) is NOT equivalent toKFold(n_splits=n_samples//p) which creates non-overlapping test sets.

Due to the high number of iterations which grows combinatorically with thenumber of samples this cross-validation method can be very costly. Forlarge datasets one should favorKFold,StratifiedKFoldorShuffleSplit.

Read more in theUser Guide.

Parameters:

pint

Size of the test sets. Must be strictly less than the number ofsamples.

Examples

>>>importnumpyasnp>>>fromsklearn.model_selectionimportLeavePOut>>>X=np.array([[1,2],[3,4],[5,6],[7,8]])>>>y=np.array([1,2,3,4])>>>lpo=LeavePOut(2)>>>lpo.get_n_splits(X)6>>>print(lpo)LeavePOut(p=2)>>>fori,(train_index,test_index)inenumerate(lpo.split(X)):...print(f"Fold{i}:")...print(f"  Train: index={train_index}")...print(f"  Test:  index={test_index}")Fold 0:  Train: index=[2 3]  Test:  index=[0 1]Fold 1:  Train: index=[1 3]  Test:  index=[0 2]Fold 2:  Train: index=[1 2]  Test:  index=[0 3]Fold 3:  Train: index=[0 3]  Test:  index=[1 2]Fold 4:  Train: index=[0 2]  Test:  index=[1 3]Fold 5:  Train: index=[0 1]  Test:  index=[2 3]

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.

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.