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 Receiver Operator Characteristic (ROC) curve. The AUC can be interpreted as the probability that a randomly chosen positive observation has a higher predicted probability than a randomly chosen negative observation.

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

## References

Youden WJ (1950).
“Index for rating diagnostic tests.”
*Cancer*, **3**(1), 32--35.
doi:10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3
.