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)

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)

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)

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)

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)

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)

Bases: nannyml.performance_calculation.metrics.base.Metric

Specificity metric.

Creates a new F1 instance.