Hi, Yes, from my humble opinion, it doesnt make any sense to use the (2-class) ROC curve for a rating system. For example, if the classifier predicts 100% for all the defaulted exposures and 0% for the good clients, then even though we have a perfect classifier we have a bad rating system.
However, if we use the multi-class version of Hand and Till (2001), we may test how good is the model to discriminate between classes or ratings. Hand, David J. and Robert J. Till, "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems", Machine Learning, Vol. 45, No. 2, (November 2001), pp. 171-186. Regards, Pedro -----Original Message----- From: Ajay ohri [mailto:[EMAIL PROTECTED] Sent: Tue 10/7/2008 6:46 PM To: Frank E Harrell Jr Cc: Rodriguez, Pedro; r-help@r-project.org Subject: Re: [R] How to validate model? the purpose of validating indirect measures such as ROC curves. Biggest Purpose- It is useful while in more marketing /sales meeting context ;) Also , Deciles specific performance is easy to explain and monitor for faster execution/re modeling. Regards, Ajay On Wed, Oct 8, 2008 at 4:01 AM, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote: 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 ______________________________________________ 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. ______________________________________________ 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 -- Regards, Ajay Ohri http://tinyurl.com/liajayohri ______________________________________________ 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.
