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 PRC is computed by integration of the piecewise function.

prauc(truth, prob, positive, na_value = NaN, ...)

## 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. |

## Value

Performance value as `numeric(1)`

.

## Note

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.

## See also

Other Binary Classification Measures:
`auc()`

,
`bbrier()`

,
`dor()`

,
`fbeta()`

,
`fdr()`

,
`fnr()`

,
`fn()`

,
`fomr()`

,
`fpr()`

,
`fp()`

,
`mcc()`

,
`npv()`

,
`ppv()`

,
`tnr()`

,
`tn()`

,
`tpr()`

,
`tp()`

## Examples

#> [1] 0.904106