skada.metrics.SupervisedScorer

class skada.metrics.SupervisedScorer(scoring=None, greater_is_better=True)[source]

Compute score on supervised dataset.

Parameters:
scoringstr or callable, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the provided estimator object's score method is used.

greater_is_betterbool, default=True

Whether scorer is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the scorer.

set_score_request(*, sample_domain: bool | None | str = '$UNCHANGED$', target_labels: bool | None | str = '$UNCHANGED$') → SupervisedScorer

Configure whether metadata should be requested to be passed to the score method.

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 (see sklearn.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 to score if provided. The request is ignored if metadata is not provided.

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

  • 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_domain parameter in score.

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

Metadata routing for target_labels parameter in score.

Returns:
selfobject

The updated object.

Examples using skada.metrics.SupervisedScorer

Using cross_val_score with skada

Using cross_val_score with skada