R help - Oct 2008 - Logistic Regression - Interpreting SENS (Sensitivity) and SPEC (Specificity)

Hi

Hi I am working on credit scoring model using logistic regression. I havd 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.

Maithili Shiva <maithili_shiva <at> yahoo.com> writes:
> I havd 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).
The simple and wrong answer is to use these data directly to compute sensitivity
(fraction of hits). This measure is useless, but I encounter it often in medical
publications.

You can get a more reasonable answer by using cross-validation. Check, for
example, Frank Harrell's 

http://biostat.mc.vanderbilt.edu/twiki/pub/Main/RmS/logistic.val.pdf

Dieter