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

www.decisionstats.com

On Wed, Oct 8, 2008 at 1:33 AM, Frank E Harrell Jr <f.harrell@vanderbilt.edu
> wrote:

> Pedro.Rodriguez@sungard.com 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:f.harrell@vanderbilt.edu] Sent: Tuesday,
>> October 07, 2008 11:02 AM
>> To: Rodriguez, Pedro
>> Cc: maithili_shiva@yahoo.com; r-help@r-project.org
>> Subject: Re: [R] How to validate model?
>>
>> Pedro.Rodriguez@sungard.com 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).
>>>
>>> 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: r-help-bounces@r-project.org
>>>
>> [mailto:r-help-bounces@r-project.org]
>>
>>> On Behalf Of Maithili Shiva
>>> Sent: Tuesday, October 07, 2008 8:22 AM
>>> To: 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
>>>
>>> ______________________________________________
>>> 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.
>>>
>>> ______________________________________________
>>> 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 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

	[[alternative HTML version deleted]]

