Dear R helpers, Hi I am working on credit scoring model using logistic regression. I have main sample of 42500 clentes and based on their status as regards to defaulted / non - defaulted, I have genereted the probability of default.
I have a hold out sample of 5000 clients. I have calculated (1) No of correctly classified goods Gg, (2) No of correcly classified Bads Bg and also (3) number of wrongly classified bads (Gb) and (4) number of wrongly classified goods (Bg). My prolem is how to interpret these results? What I have arrived at are the absolute figures. Using these I hav ecalculated Specificity (SPEC) and sensitivity (SENS) as SPEC = Bb / (Bb + Gg) and SENS = Gg / (Gg + Bg) With regards Maithili ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
