Macro averaged in binary classification
It's of course technically possible to calculate macro (or micro) average performance with only two classes, but there's no need for it. Normally one specifies which of the two classes is the positive one (usually the minority class), and then regular precision, recall and F-score can be used.
Commonly there is a majority and a minority class, and naturally the majority class is easier to predict for the classifier. That's why the minority class is usually chosen as the positive class: by choosing the most difficult class to predict, the performance value represents more precisely the real ability of the classifier. As a consequence, the macro-average performance is often better than the performance on the positive class, since the former includes the &quot;easy&quot; class.