WeakSegmentsPerformance.run#
- WeakSegmentsPerformance.run(dataset: Union[Dataset, DataFrame], model: Optional[BasicModel] = None, feature_importance: Optional[Series] = None, feature_importance_force_permutation: bool = False, feature_importance_timeout: int = 120, with_display: bool = True, y_pred_train: Optional[ndarray] = None, y_pred_test: Optional[ndarray] = None, y_proba_train: Optional[ndarray] = None, y_proba_test: Optional[ndarray] = None) CheckResult[source]#
Run check.
- Parameters
- dataset: Union[Dataset, pd.DataFrame]
Dataset or DataFrame object, representing data an estimator was fitted on
- model: Optional[BasicModel], default: None
A scikit-learn-compatible fitted estimator instance
- feature_importance: pd.Series , default: None
pass manual features importance
- feature_importance_force_permutationbool , default: False
force calculation of permutation features importance
- feature_importance_timeoutint , default: 120
timeout in second for the permutation features importance calculation
- y_pred_train: Optional[np.ndarray] , default: None
Array of the model prediction over the train dataset.
- y_pred_test: Optional[np.ndarray] , default: None
Array of the model prediction over the test dataset.
- y_proba_train: Optional[np.ndarray] , default: None
Array of the model prediction probabilities over the train dataset.
- y_proba_test: Optional[np.ndarray] , default: None
Array of the model prediction probabilities over the test dataset.
- features_importance: Optional[pd.Series] , default: None
pass manual features importance .. deprecated:: 0.8.1
Use ‘feature_importance’ instead.