[R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)

Gad Abraham gabraham at csse.unimelb.edu.au
Thu Oct 16 01:48:00 CEST 2008

Frank E Harrell Jr wrote:
> Gad Abraham wrote:
>>> This approach leaves much to be desired.  I hope that its 
>>> practitioners start gauging it by the mean squared error of predicted 
>>> probabilities.
>> Is the logic here is that low MSE of predicted probabilities equals a 
>> better calibrated model? What about discrimination? Perfect calibration 
> Almost.  I was addressed more the wish for the use of strategies that 
> maximize precision while keeping bias to a minimim.
>> implies perfect discrimination, but I often find that you can have two 
> That doesn't follow.  You can have perfect calibration in the large with 
> no discrimination.

I'm not sure I understand: if you have perfect calibration, so that you 
correctly assign the probability Pr(y=1|x) to each x, doesn't it follow 
that the x will also be ranked in correct order of probability, which is 
what the AUC is measuring?

>> competing models, the first with higher discrimination (AUC) and worse 
>> calibration, and the the second the other way round. Which one is the 
>> better model?
> I judge models on the basis of both discrimination (best measured with 
> log likelihood measures, 2nd best AUC) and calibration.  It's a 
> two-dimensional issue and we don't always know how to weigh the two. For 
> many purposes calibration is a must.  In those we don't look at 
> discrimination until calibration-in-the-small is verified at high 
> resolution.

By "log likelihood measures" do you mean likelihood-ratio tests?

Gad Abraham
Dept. CSSE and NICTA
The University of Melbourne
Parkville 3010, Victoria, Australia
email: gabraham at csse.unimelb.edu.au
web: http://www.csse.unimelb.edu.au/~gabraham

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