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.usa at 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!
>
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