Amy, If I were you, I will check the misclassification rates in both training set and testing set from 2 models.
On 1/28/07, Amy Koch <[EMAIL PROTECTED]> wrote: > Hi, > > I have done an analysis using 'rpart' to construct a Classification Tree. I > am wanting to retain the output in tree form so that it is easily > interpretable. However, I am wanting to compare the 'accuracy' of the tree > to a Random Forest to estimate how much predictive ability is lost by using > one simple tree. My understanding is that the error automatically displayed > by the two functions is calculated differently so it is therefore incorrect > to use this as a comparison. Instead I have produced a table for both > analyses comparing the observed and predicted response. > > E.g. table(data$dependent,predict(model,type="class")) > > I am looking for confirmation that (a) it is incorrect to compare the error > estimates for the two techniques and (b) that comparing the > misclassification rates is an appropriate method for comparing the two > techniques. > > Thanks > > Amy > > > > > > Amelia Koch > > University of Tasmania > > School of Geography and Environmental Studies > > Private Bag 78 Hobart > > Tasmania, Australia 7001 > > Ph: +61 3 6226 7454 > > [EMAIL PROTECTED] > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > -- WenSui Liu A lousy statistician who happens to know a little programming (http://spaces.msn.com/statcompute/blog) ______________________________________________ R-help@stat.math.ethz.ch 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.