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 0 or 1. If we split the output results between 0 and 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 0 and 1 input variable or maybe just create two individual models instead?


1 Answer 1


Use model(s) that output class probabilities. Whether you should use an omnibus model or separate models should be guided by which approach yields better probabilistic predictions. Use proper to assess this.

Use as many thresholds as required when you use your predictions to make decisions. Note that even if there are only two classes (sick/healthy), there may be more than two decisions (treat as sick/run more tests/treat as healthy).

More here.


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