 Solution

The two quantities areClassification precision asks how many retained calls agree with the positive reference; classification recall asks how much of the positive reference set has been recovered. Recall is the true positive rate. Increasing recall by accepting more loci can reduce precision through additional false positives.

If positive prevalence is $p$, thenThus prevalence matters: good ROC discrimination need not give high precision when true positives are rare. A precision–recall area must be judged against its own prevalence-dependent baseline; it is not numerically interchangeable with ROC area.