Describe the issue linked to the documentation

TL;DR: Metadata routing for scoring could either use a base class or documentation of how to writeset_score_request().

Currently theMetadata Estimator Dev Guide has examples of a metadata-consuming estimator and a metadata-routing estimator. However, the metadata routing is also designed for scorers and CV splitters which may or may not be estimators. Fortunately,sklearn.model_selection exposesBaseCrossValidator, which likeBaseEstimator, subclasses_MetadataRequester. Unfortunately,there's no base class for scorers. the base class for scorers,_BaseScorer, is not public.

I don't understand how to string together the relevant methods that should be a part ofset_score_params, The current workaround is to simply subclassBaseEstimator, even if I'm not making an estimator, or to subclass_MetadataRequester, even though its not part of the public API.Or usemake_scorer to pin the kwargs when instantiating the meta-estimator, rather than infit()

My use case is for scoring a time-series model where the data generating mechanism is known to the experiment, but not the model, and I need to compare the fit model to the true data generating mechanism. I understand how to use a custom scorer inRandomizedSearchCV, and themetadata API explains how meta estimators likeRandomizedSearchCV can pass additional arguments to my custom scorer.

Suggest a potential alternative/fix

• publicly exposing_MetadataRequester
• publicly exposing_BaseScorer
• Document how to write theset_{method}_request() methods. It looks like_MetadataRequester uses a descriptorRequestMethod, which relies on an instance having a_get_metadata_request method and a_metadata_request attribute (which it doesn't really describe).

EDIT: Found out about_BaseScorer. Also, removedmake_scorer workaround, as it doesn't result in passing the fitted estimator to the_score_func