Michael wrote:
Hi Frank,
Thanks for your help!
I want to incorporate lift score as the optimization objective. How to
do that in logistic regression?
Thanks!
Please re-read my note.
Models should be fitted using proper scoring rules. Otherwise the
resulting fit is bogus.
Thanks
Frank
On Fri, Jun 26, 2009 at 7:47 AM, Frank E Harrell
Jr<f.harr...@vanderbilt.edu> wrote:
Michael wrote:
Hi all,
Is there a way to change the loss function in the logistic regression?
Or we could provide a customized loss function in the logistic
regression so we could use that loss function in the Cross Validation
in logistic regression?
Thanks a lot!
The goal is to use a loss function that yields optimality, with a sensible
definition of optimality. For many purposes, maximum likelihood or
penalized maximum likelihood is optimum. So don't change the optimality
criteria just because you are cross-validating a different measure.
By the way, it's often not a good idea to cross-validate a different
measure. At least the accuracy index should be information-preserving.
Deviance, log-likelihood, and AIC are your friends.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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