The GenIQ Model
© is a machine learning alternative model to the statistical ordinary least squares and logistic regression models. GenIQ lets the data define the model – automatically data mines for new variables, performs variable selection, and then specifies the model equation – so as to "optimize the decile table," to fill the upper deciles with as much profit/many responses as possible. As an extra-GenIQ application
, GenIQ can be used on a final regression model to let GenIQs data mining prowess enhance the results of the final model. The GenIQ-enhanced Regression Model: Enhance a final regression model by running GenIQ with only one predictor: the final regression equation score.
The purpose of this article is to apply the GenIQ-enhanced Regression Model to show “How to Make the Best Credit Score Even Better: FairCreditScore GenIQ-enhanced.” To make the best credit score (FairCreditScore) even better the GenIQ Model only uses the FairCreditScore variable. FairCreditScore (FCScore) is a number, based on statistical regression analysis of persons' credit reports, used to represent the creditworthiness of a person, i.e., the likelihood of a person will pay his/her debts. Specifically, FCScore is the primary score used throughout the money lending industry, chiefly based on credit report information from the three major credit bureaus. First, I provide a detailed Illustration of the GenIQ-enhanced Regression Model
using a simple two-variable logistic regression model. If the GenIQ illustration piques your
curiosity, I would be glad to email
you, just for the asking: “How to Make the Best Credit Score Even Better.”