Frank, That makes sense.
I just had a look at the actual algorithm calculating the Briar score. One thing that confuses me is how the score is calculated. If I understand the code correctly, it is just: sum((p - y)^2)/n If I have an example with a label of 1 and a probability prediction of .4, it is (.4 - 1)^2 (I know it is the average of these value across all the examples) Wouldn't it make more sense to stratify the probabilities and then check the accuracy of each level. i.e. For predicted probabilities of .10 to .20 the data was actually labeled true .18 percent of the time. mean(label) On 8/19/09 11:51 AM, Frank E Harrell Jr wrote: > Noah Silverman wrote: >> Thanks for the suggestion. >> >> You explained that Briar combines both accuracy and discrimination >> ability. If I understand you right, that is in relation to binary >> classification. >> >> I'm not concerned with binary classification, but the accuracy of the >> probability predictions. >> >> Is there some kind of score that measures just the accuracy? >> >> Thanks! >> >> -N > > The Brier score has nothing to do with classification. It is a > probability accuracy score. > > Frank > >> >> On 8/19/09 10:42 AM, Frank E Harrell Jr wrote: >>> Noah Silverman wrote: >>>> Hello, >>>> >>>> I working on a model to predict probabilities. >>>> >>>> I don't really care about binary prediction accuracy. >>>> >>>> I do really care about the accuracy of my probability predictions. >>>> >>>> Frank was nice enough to point me to the val.prob function from the >>>> Design library. It looks very promising for my needs. >>>> >>>> I've put together some tests and run the val.prob analysis. It >>>> produces some very informative graphs along with a bunch of >>>> performance measures. >>>> >>>> Unfortunately, I'm not sure which measure, if any, is the "best" >>>> one. I'm comparing hundreds of different models/parameter >>>> combinations/etc. So Ideally I'd like a single value or two as the >>>> "performance measure" for each one. That way I can pick the >>>> "best" model from all my experiments. >>>> >>>> As mentioned above, I'm mainly interested in the accuracy of my >>>> probability predictions. >>>> >>>> Does anyone have an opinion about which measure I should look at?? >>>> (I see Dxy, C, R2, D, U, Briar, Emax, Eavg, etc.) >>>> >>>> Thanks!! >>>> >>>> -N >>> >>> It all depends on the goal, i.e., the relative value you place on >>> absolute accuracy vs. discrimination ability. The Brier score >>> combines both and other than interpretability has many advantages. >>> >>> Frank >>> >>>> >>>> ______________________________________________ >>>> 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. >>>> >>> >>> > > [[alternative HTML version deleted]] ______________________________________________ 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.
