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.

## Note

The score function calls mlr3measures::auc() from package mlr3measures.

If the measure is undefined for the input, NaN is returned. This can be customized by setting the field na_value.

## Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures\$get("auc")
msr("auc")

## Meta Information

• Type: "binary"

• Range: $$[0, 1]$$

• Minimize: FALSE

• Required prediction: prob