skada.model_selection.SourceTargetShuffleSplit
- class skada.model_selection.SourceTargetShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]
Source-Target-Shuffle-Split 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.
Default split is implemented hierarchically. If first designates a single domain as a target followed up by the single train/test shuffle split.
- set_split_request(*, sample_domain: bool | None | str = '$UNCHANGED$') SourceTargetShuffleSplit
Request metadata passed to the
splitmethod.Note that this method is only relevant if
enable_metadata_routing=True(seesklearn.set_config()). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tosplitif 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 the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline. Otherwise it has no effect.- Parameters:
- sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_domainparameter insplit.
- Returns:
- selfobject
The updated object.
