Ajay ohri wrote:
This is an approach
Run the model variables on hold out sample.
Check and compare ROC curves between build and validation datasets.
Check for changes in parameter estimates (co efficients of variables) p
value and signs.
Check for binning (response versus deciles of individual variables).
Check concordance, and KS Statistic.
A decile wise performance of the model in terms of predicted versus
actual, rank ordering of deciles, helps in explaining the model to
business audience who generally have some business specific input that
may require scoring model to be tweaked.
This assumes multicollinearity, outliers and missing value treatment
have already been done, and holdout sample checks for overfitting. You
can always rebuild the model using a different random holdout sample.
A stable model would not change too much.
In actual implementation , try and build real time triggers for
deviations (%) between predicted and actual.
Regards,
Ajay
I wouldn't recommend that approach but legitimate differences of opinion
exist on the subject. In particular I fail to see the purpose of
validating indirect measures such as ROC curves.
Frank
www.decisionstats.com <http://www.decisionstats.com>
On Wed, Oct 8, 2008 at 1:33 AM, Frank E Harrell Jr
<[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>> wrote:
[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]> wrote:
Hi Frank,
Thanks for your feedback! But I think we are talking about two
different
things.
1) Validation: The generalization performance of the classifier.
See,
for example, "Studies on the Validation of Internal Rating
Systems" by
BIS.
I didn't think the desire was for a classifier but instead was for a
risk predictor. If prediction is the goal, classification methods
or accuracy indexes based on classifications do not work very well.
2) Calibration: Correct calibration of a PD rating system means
that the
calibrated PD estimates are accurate and conform to the observed
default
rates. See, for instance, An Overview and Framework for
PD Backtesting and Benchmarking, by Castermans et al.
I'm unclear on what you mean here. Correct calibration of a
predictive system means that the UNcalibrated estimates are accurate
(i.e., they don't need any calibration). (What is PD?)
Frank, you are referring the #1 and I am referring to #2.
Nonetheless, I would never create a rating system if my model
doesn't
discriminate better than a coin toss.
For sure
Frank
Regards,
Pedro
-----Original Message-----
From: Frank E Harrell Jr [mailto:[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>] Sent: Tuesday, October 07,
2008 11:02 AM
To: Rodriguez, Pedro
Cc: [EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>;
r-help@r-project.org <mailto:r-help@r-project.org>
Subject: Re: [R] How to validate model?
[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>
wrote:
Usually one validates scorecards with the ROC curve, Pietra
Index, KS
test, etc. You may be interested in the WP 14 from BIS
(www.bis.org <http://www.bis.org>).
Regards,
Pedro
No, the validation should be done using an absolute reliability
(calibration) curve. You need to verify that at all levels of
predicted
risk there is agreement with the true probability of failure.
An ROC curve does not do that, and I doubt the others do. A
resampling-corrected loess calibration curve is a good approach
as implemented in the Design package's calibrate function.
Frank
-----Original Message-----
From: [EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>
[mailto:[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>]
On Behalf Of Maithili Shiva
Sent: Tuesday, October 07, 2008 8:22 AM
To: r-help@r-project.org <mailto:r-help@r-project.org>
Subject: [R] How to validate model?
Hi!
I am working on scorecard model and I have arrived at the
regression
equation. I have used logistic regression using R.
My question is how do I validate this model? I do have hold
out sample
of 5000 customers.
Please guide me. Problem is I had never used Logistic regression
earlier
neither I am used to credit scoring models.
Thanks in advance
Maithili
______________________________________________
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PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible
code.
______________________________________________
R-help@r-project.org <mailto: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.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
______________________________________________
R-help@r-project.org <mailto: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.
--
Regards,
Ajay Ohri
http://tinyurl.com/liajayohri
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
