Rejection reasons from binary classification model
Question
Suppose we work in financial services for a firm that provides housing loans. We use a binary classification model to determine whether or not each applicant should be qualified for a loan. Assuming we do not have access to the specific feature weights in this model, how would we provide each rejected candidate a reason why their application was rejected?