recall_per_class()

audmetric.recall_per_class(truth, prediction, labels=None, *, zero_division=0)[source]

Recall per class.

recallk=true positivektrue positivek+false negativek\text{recall}_k = \frac{\text{true positive}_k} {\text{true positive}_k + \text{false negative}_k}
Parameters

  • truth (Sequence[Any]) – ground truth values/classes

  • prediction (Sequence[Any]) – predicted values/classes

  • labels (Optional[Sequence[Any]]) – included labels in preferred ordering. If no labels are supplied, they will be inferred from {prediction,truth}\{\text{prediction}, \text{truth}\} and ordered alphabetically.

  • zero_division (float) – set the value to return when there is a zero division

Return type

Dict[str, float]

Returns

dictionary with label as key and recall as value

Examples

>>> recall_per_class([0, 0], [0, 1])
{0: 0.5, 1: 0.0}