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
Configure whether metadata should be requested to be passed to the
splitmethod.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config()). Please check the 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.
- Parameters:
- sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_domainparameter insplit.
- Returns:
- selfobject
The updated object.
