RocReport.run#

RocReport.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.