Which metric should I use for classifying an imbalace data with fewer labels for the negative class?
The names of the classes don't matter, you might as well call them class A and class B. In binary classification the typical choice is to evaluate using precision, recall and F1-score. There are other options, but that depends on the task.
Assuming you choose F1-score, the choice of which class you select as the &quot;positive&quot; class for evaluation also depends on the task. Usually it's recommended to use the minority class because it's the most challenging one for the classifier.
The only problem here is the possible confusion of calling a class &quot;negative&quot; and calculating F1-score using it as the &quot;positive&quot; class, but that's just a naming issue. You could easily add this point to the explanations, or avoid any confusion by calling your classes A and B for instance.