Amy, I have also had this issue with randomForest, that is, you lose the ability to explain the classifier in a simple way to non-specialists (everyone can understand the single decision tree.) As far as comparing the accuracy of the two, I think that you are correct in comparing them by the actual vs predicted tables. randomForest reports this as the confusion matrix, and it also reports the out-of-bag error, which I think you are referring to. I would not compare the rf out-of-bag error with the rpart relative error (or cross-validated error if you are doing cross validation.)
So, for what it's worth I think you are correct. Also, do you know about ctree in the "party" package? If you want to retain the explanatory power of a single tree and have a nice accurate classifier, I have found ctree to work quite well. HTH, Darin On Mon, Jan 29, 2007 at 11:34:51AM +1100, Amy Koch 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. ______________________________________________ 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.