How to find the variables involved in the decision to report adverse action when the origination scorecard is developed using Machine Learning - XGBOOST with monotonic constraints (80 variables)
Scenario 1: easy one- Which 3 distinct noncorrelated variables are the major contributor to the rejection
Scenario 2: Little complex - Selection of variables for adverse action and then matching with development data to get two names. For an example How to get the explanation for a person rejected for credit product in below format. ?
”This person is rejected because their months since most recent inquiry with value 2, number of trade lines opened in the last 12 months with value 4, and number of satisfied trade lines with value 2 are similar to Mike Brown and Alice Wong in the datasets. Wong and Brown could not payoff.”
Disclaimer: Above Line is used from FICO website and intention is to explain my problem.