|
Risk Management
The quality of your predictive models can be one of your greater risks in risk management applications such as credit scoring or insurance applications. This is especially true because the inherent arbitrary nature of variable selection in Stepwise Logistic Regression means that each modeler will be likely to generate a different predictive model due to arbitrary parameter cutoff choices. Reduced Error Logistic Regression (RELR) removes this source of risk and uncertainty from your risk management program, as all modelers will generate the very same RELR model. Furthermore, RELR models usually will be much more accurate than Stepwise Logistic Regression models when you have many important variables and interactions, correlated variables, nonlinear variables, and/or small sample sizes. Please read our Case Studies page for specific case examples of how RELR has been quite beneficial in risk management applications in credit scoring.
|
The quality of your predictive modeling solution is probably one of the great risks in your risk management program. Our Reduced Error Logistic Regression will help you to build much higher quality predictive models and lock out much of your analytical risk.
|
