nannyml.performance_calculation.metrics.binary_classification module¶
- class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationAUROC(y_true: str, y_pred: str, y_pred_proba: Optional[str] = None)[source]¶
Bases:
nannyml.performance_calculation.metrics.base.Metric
Area under Receiver Operating Curve metric.
Creates a new AUROC instance.
- class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationAccuracy(y_true: str, y_pred: str, y_pred_proba: Optional[str] = None)[source]¶
Bases:
nannyml.performance_calculation.metrics.base.Metric
Accuracy metric.
Creates a new Accuracy instance.
- class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationF1(y_true: str, y_pred: str, y_pred_proba: Optional[str] = None)[source]¶
Bases:
nannyml.performance_calculation.metrics.base.Metric
F1 score metric.
Creates a new F1 instance.
- class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationPrecision(y_true: str, y_pred: str, y_pred_proba: Optional[str] = None)[source]¶
Bases:
nannyml.performance_calculation.metrics.base.Metric
Precision metric.
Creates a new Precision instance.
- class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationRecall(y_true: str, y_pred: str, y_pred_proba: Optional[str] = None)[source]¶
Bases:
nannyml.performance_calculation.metrics.base.Metric
Recall metric, also known as ‘sensitivity’.
Creates a new Recall instance.
- class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationSpecificity(y_true: str, y_pred: str, y_pred_proba: Optional[str] = None)[source]¶
Bases:
nannyml.performance_calculation.metrics.base.Metric
Specificity metric.
Creates a new F1 instance.