A model usually has one threshold, for example 0.5, if anything is greater than 0.5 we predict it as 1 vice versa.
However, there are some features that impacts the output probability. Let's say feature
a can equal to either
1. If we split the output results between
1, the thresholds gathered for
0 greatly differs from
1, meaning that the thresholds can be much greater than the probabilities that occurs in
0. Hence that anything predicted with
0 is automatically equal to
0 class, but in reality that is not true.
Is it logical to create two separate thresholds for the
1 input variable or maybe just create two individual models instead?