iguanas.metrics.classification
.Revenue¶
- class iguanas.metrics.classification.Revenue(y_type: str, chargeback_multiplier: int)[source]¶
Calculates the revenue for either a single or set of binary predictors.
- Parameters
- y_typestr
Dictates whether the binary target column flags fraud (y_type = ‘Fraud’) or non-fraud (y_type = ‘NonFraud’).
- chargeback_multiplierint
Multiplier to apply to chargeback transactions.
- fit(y_true: Union[iguanas.utils.typing.numpy.ndarray, iguanas.utils.typing.pandas.core.series.Series, iguanas.utils.typing.databricks.koalas.series.Series], y_preds: Union[iguanas.utils.typing.numpy.ndarray, iguanas.utils.typing.pandas.core.series.Series, iguanas.utils.typing.databricks.koalas.series.Series, iguanas.utils.typing.pandas.core.frame.DataFrame, iguanas.utils.typing.databricks.koalas.frame.DataFrame], sample_weight: Union[iguanas.utils.typing.numpy.ndarray, iguanas.utils.typing.pandas.core.series.Series, iguanas.utils.typing.databricks.koalas.series.Series]) Union[float, iguanas.utils.typing.numpy.ndarray] [source]¶
Calculates the revenue for either a single or set of binary predictors.
- Parameters
- y_trueUnion[NumpyArrayType, PandasSeriesType, KoalasSeriesType]
The target column.
- y_predsUnion[NumpyArrayType, PandasSeriesType, KoalasSeriesType, PandasDataFrameType, KoalasDataFrameType]
The binary predictor column.
- sample_weightUnion[NumpyArrayType, PandasSeriesType, KoalasSeriesType]
Row-wise transaction amounts to apply.
- Returns
- Union[float, NumpyArrayType]
Revenue(s).