I'm trying to build a logistic regression model to predict 90+ Days past due(DPD) events. The size of the database is 96000, with an event rate of 6%. We ran the entire data set through the info value process, and converted it into Weight of Evidence (WOE) bins. When I try to build the model using 60% of the data (for development data, the other 40% are held out for validation), the logistic regression model gives me 7 significant variables with a very high wald score for the intercept. Below, I give the results from the model:
- The overall model is significant (P<.0001).
- ROC 0.84.
- The Hosmer and Lemeshow Test is significant (P<0.0001), which implies the model does not adequately fit the data.
- The accuracy of this model is poor, and has a correct classification rate of 21%.
Please tell me your views on this, specifically: . Are there any ways/methods that can help improve performance on the HL Test, since we need use the probability for the prediction? . Can I ignore a few good loans based on some business rules? . Is there a different methodology we should try?
I'm fairly new to the credit risk modeling and looking forward for your view.
Thank you in advance