This is a general question. I have a binary classification which predicts if someone is rich or not. I had a question from someone asking that if the probability someone is rich is 0.6 and another person is also given this probability are the reasons for WHY they are rich the same?
I am using an xgboost and my instinct is to say no. e.g. if i were to profile each population > = 0.5, >= 0.6,... etc would i find differences in their features? I would say it's hard because there's no linear relationship between outcome and target most of time, it can be complex.
In general i guess my question is: if two people are given same probabiity of being class 1 - will the models reasons for giving each of these people this 0.6 be the same? 'reasons' being features/feature values