Given a classifier working with double values e.g. between 0 and 1. There are two classes with different ranges. Their distributions are uniform, however, one class is more likely.
Is picking always the class with higher likelihood the best classifier, in case a value lies in both ranges?
I make an example if my question was unclear. A number representing e.g. weight between 0 and 1 kg is to classify. There are two object classes. One between 0.1 and 0.6 kg and one between 0.4 and 0.8 kg. Both uniform distributed, but the first more likely.
Intuitively one tries to calculate a threshold depending on the likelihood of the class. My assumption however is, that picking the first class till 0.6 results in least misclassification. Just because the first class is more likely. No difference if 1.1 times or 5 times more likely.