Tags#
- class sklearn.utils.Tags(target_tags: TargetTags, transformer_tags: TransformerTags | None, classifier_tags: ClassifierTags | None, regressor_tags: RegressorTags | None, array_api_support: bool = False, no_validation: bool = False, non_deterministic: bool = False, requires_fit: bool = True, _skip_test: bool = False, _xfail_checks: dict[str, str] = <factory>, input_tags: InputTags = <factory>)[source]#
Tags for the estimator.
See Estimator Tags for more information.
- Parameters:
- target_tags
TargetTags
The target(y) tags.
- transformer_tags
TransformerTags
or None The transformer tags.
- classifier_tags
ClassifierTags
or None The classifier tags.
- regressor_tags
RegressorTags
or None The regressor tags.
- array_api_supportbool, default=False
Whether the estimator supports Array API compatible inputs.
- no_validationbool, default=False
Whether the estimator skips input-validation. This is only meant for stateless and dummy transformers!
- non_deterministicbool, default=False
Whether the estimator is not deterministic given a fixed
random_state
.- requires_fitbool, default=True
Whether the estimator requires to be fitted before calling one of
transform
,predict
,predict_proba
, ordecision_function
.- _skip_testbool, default=False
Whether to skip common tests entirely. Don’t use this unless you have a very good reason.
- _xfail_checksdict[str, str], default={}
Dictionary
{check_name: reason}
of common checks that will be marked asXFAIL
for pytest, when usingparametrize_with_checks
. These checks will be simply ignored and not run bycheck_estimator
, but aSkipTestWarning
will be raised. Don’t use this unless there is a very good reason for your estimator not to pass the check. Also note that the usage of this tag is highly subject to change because we are trying to make it more flexible: be prepared for breaking changes in the future.- input_tags
InputTags
The input data(X) tags.
- target_tags