Measure to compare true observed labels with predicted probabilities in binary classification tasks.

## Arguments

- truth
(

`factor()`

)

True (observed) labels. Must have the exactly same two levels and the same length as`response`

.- prob
(

`numeric()`

)

Predicted probability for positive class. Must have exactly same length as`truth`

.- positive
(

`character(1))`

Name of the positive class.- na_value
(

`numeric(1)`

)

Value that should be returned if the measure is not defined for the input (as described in the note). Default is`NaN`

.- ...
(

`any`

)

Additional arguments. Currently ignored.

## Details

Computes the area under the Precision-Recall curve (PRC). The PRC can be interpreted as the relationship between precision and recall (sensitivity), and is considered to be a more appropriate measure for unbalanced datasets than the ROC curve. The AUC-PRC is computed by integration of the piecewise function.

This measure is undefined if the true values are either all positive or all negative.

## References

Davis J, Goadrich M (2006).
“The relationship between precision-recall and ROC curves.”
In *Proceedings of the 23rd International Conference on Machine Learning*.
ISBN 9781595933836.