Furthermore, detection tasks are commonly evaluated using F-measure on positive classes, rather than accuracy or F-measure on all classes.
positive and negative classes play different roles in the evaluation: the performance is evaluated by only considering how well we can detect positive instances, while correct predictions of negative instances are ignored.