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

## Arguments

- truth
(

`factor()`

)

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

.- response
(

`factor()`

)

Predicted response labels. Must have the exactly same two levels and the 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

Calculates the geometric mean of `precision()`

P and `recall()`

R as $$
\sqrt{\mathrm{P} \cdot \mathrm{R}}.
$$

This measure is undefined if precision or recall is undefined, i.e. if TP + FP = 0 or if TP + FN = 0.

## References

He H, Garcia EA (2009).
“Learning from Imbalanced Data.”
*IEEE Transactions on knowledge and data engineering*, **21**(9), 1263–1284.
doi:10.1109/TKDE.2008.239
.