Basically, let's assume I have a simple rules-based function/model (if weight >= 150) -> return true. Simple binary answer (true or false) from a single feature input.
If I have a range of samples/population values that this rule was derived from (i.e. 50 - 400), are there techniques to derive a probability distribution or "prediction score". i.e. 150 would be 0.5 but 400 would really be 1.0. I am looking for any mathematical techniques to convert this into a "interpret-able probability score".
My second (related) question is best practices around combining (ensemble) of probability based models with rules-based models. Doing simple prescriptive engines works to a degree, but what I am looking for is for example "stacking" 2 probability models and 2 rules-based models based on a common set of outputs. Obviously, if a rules based model can have an "interpret-able" associated probability this makes it a lot easier.