I have a set of users. A classification algorithm is applied on all users, and I take (call
analyzedExperts) a set of users which are binary classified (expert & non-expert).
And I use another method to evaluate this algorithm. That does the same thing, and I take another set of users which are also binary classified (call
But if I want to measure the precision and recall, I take the same result for both. The sets
realExperts have both the same size of data.
I don't understand why they are same, and don't know whether it is normal. P.S. I'm not sure whether the precision and recall is a good way to measure the evaluating the results.
Thus, the question is: if they are equalsized, precision and recall have to be same?
Suppose they have 3 users in common (True positive). FN and FP will be always same because they both have the same size. What implicits that the precision and recall will be same.
Second question might be then: does
realExperts has to have greater size? Or is it not the good place to use precision or recall?