GroupKFold#

classsklearn.model_selection.GroupKFold(n_splits=5,*,shuffle=False,random_state=None)[source]#

K-fold iterator variant with non-overlapping groups.

Each group will appear exactly once in the test set across all folds (thenumber of distinct groups has to be at least equal to the number of folds).

The folds are approximately balanced in the sense that the number ofsamples is approximately the same in each test fold whenshuffle is True.

Read more in theUser Guide.

For visualisation of cross-validation behaviour andcomparison between common scikit-learn split methodsrefer toVisualizing cross-validation behavior in scikit-learn

Parameters:

n_splitsint, default=5

Number of folds. Must be at least 2.

Changed in version 0.22:n_splits default value changed from 3 to 5.

shufflebool, default=False

Whether to shuffle the groups before splitting into batches.Note that the samples within each split will not be shuffled.

Added in version 1.6.

random_stateint, RandomState instance or None, default=None

Whenshuffle is True,random_state affects the ordering of theindices, which controls the randomness of each fold. Otherwise, thisparameter has no effect.Pass an int for reproducible output across multiple function calls.SeeGlossary.

Added in version 1.6.

See also

LeaveOneGroupOut

For splitting the data according to explicit domain-specific stratification of the dataset.

StratifiedKFold

Takes class information into account to avoid building folds with imbalanced class proportions (for binary or multiclass classification tasks).

Notes

Groups appear in an arbitrary order throughout the folds.

Examples

>>>importnumpyasnp>>>fromsklearn.model_selectionimportGroupKFold>>>X=np.array([[1,2],[3,4],[5,6],[7,8],[9,10],[11,12]])>>>y=np.array([1,2,3,4,5,6])>>>groups=np.array([0,0,2,2,3,3])>>>group_kfold=GroupKFold(n_splits=2)>>>group_kfold.get_n_splits(X,y,groups)2>>>print(group_kfold)GroupKFold(n_splits=2, random_state=None, shuffle=False)>>>fori,(train_index,test_index)inenumerate(group_kfold.split(X,y,groups)):...print(f"Fold{i}:")...print(f"  Train: index={train_index}, group={groups[train_index]}")...print(f"  Test:  index={test_index}, group={groups[test_index]}")Fold 0:  Train: index=[2 3], group=[2 2]  Test:  index=[0 1 4 5], group=[0 0 3 3]Fold 1:  Train: index=[0 1 4 5], group=[0 0 3 3]  Test:  index=[2 3], group=[2 2]

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=None,y=None,groups=None)[source]#

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

Parameters:

Xobject

Always ignored, exists for compatibility.

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.

set_split_request(*,groups:bool|None|str='$UNCHANGED$')→GroupKFold[source]#

Configure whether metadata should be requested to be passed to thesplit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed tosplit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it tosplit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forgroups parameter insplit.

Returns:

selfobject

The updated object.

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,), default=None

The target variable for supervised learning problems.

groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset intotrain/test set.

Yields:

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.

Gallery examples#

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn

Release Highlights for scikit-learn 1.4

Release Highlights for scikit-learn 1.4