Michael, a lift chart for evaluating binary scoring classifiers, as I understand it, plots...
lift score: P(Yhat = + | Y = +)/P(Yhat = +) against rate of rate of positive predictions: P(Yhat = +). ...across the continuum of possible cutoffs. If you want to do this, here is how you would do this with ROCR: library(ROCR) x <- your.predicted.scores y <- your.true.class.labels pred <- prediction(x, y) perf <- performance(pred, 'lift', 'rpp') plot(perf) x and y can be vectors, or, in the case of cross-validation, data frames or lists representing the individual cross-validation runs. See the ROCR help pages ?performance, help(package=ROCR) and this slide deck: http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt HTH, Tobias On Wed, Jun 24, 2009 at 5:17 PM, Michael<comtech....@gmail.com> wrote: > Hi all, > > Could anybody give me some pointers to Cross Validation using Lifting > Score as error function, as commonly used in data-mining and > classification field in marketing and e-commerce research? > > Thanks! > > ______________________________________________ > 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.
