I am developing a probability to default model on a data from landing firm. After running the GLM() model i have got the below message:
Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred
Then i run xgboost() and was able to get decent accuracy. Now i want to determine the important features and their impact on customer default.
But not sure how to go ahead, as far as i know i could not get variable significance in xgboost() and the GLM() was run with above error.
So can't advise/conclude anything confidently.
Please Note : I am not looking for suggestions on how to avoid perfect separation problem(that is already available in many posts) but need help on how to advise business on change in which feature impact the default rate to what extent.
I know only GLM() model based on which i can give some advise but at the moment i am not so confident on glm() results, so what all other techniques can be picked up.