Suite.run#
- Suite.run(train_dataset: Optional[Union[Dataset, DataFrame]] = None, test_dataset: Optional[Union[Dataset, DataFrame]] = None, model: Optional[BasicModel] = None, features_importance: Optional[Series] = None, feature_importance_force_permutation: bool = False, feature_importance_timeout: Optional[int] = None, scorers: Optional[Mapping[str, Union[str, Callable]]] = None, scorers_per_class: Optional[Mapping[str, Union[str, Callable]]] = None) SuiteResult[source]#
Run all checks.
- Parameters
- train_dataset: Optional[Union[Dataset, pd.DataFrame]] , default None
object, representing data an estimator was fitted on
- test_datasetOptional[Union[Dataset, pd.DataFrame]] , default None
object, representing data an estimator predicts on
- modelBasicModel , default None
A scikit-learn-compatible fitted estimator instance
- features_importancepd.Series , default None
pass manual features importance
- feature_importance_force_permutationbool , default None
force calculation of permutation features importance
- feature_importance_timeoutint , default None
timeout in second for the permutation features importance calculation
- scorersMapping[str, Union[str, Callable]] , default None
dict of scorers names to scorer sklearn_name/function
- scorers_per_classMapping[str, Union[str, Callable]], default None
dict of scorers for classification without averaging of the classes See <a href= “https://scikit-learn.org/stable/modules/model_evaluation.html#from-binary-to-multiclass-and-multilabel”> scikit-learn docs</a>
- Returns
- ——-
- SuiteResult
All results by all initialized checks