Maithili Shiva wrote:
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
Sensitivity and specificity have no usefulness in your situation as they
are in reverse time order (condition on the unknown and fail to
condition on what was already known). Your test sample is too small.
You are not addressing absolute calibration through precise
high-resolution methods. My book Regression Modeling Strategies goes
into this.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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