You need to think through the application of your model. Is it more important to get more cases classified correctly, or to avoid "bigger" errors versus a probability prediction? You should optimize your choice of a loss function so that it is appropriate to the way in which the model will be used.
lubaroz wrote: > > Hi, > I am working with CART regression now to predict a probability; the > response is binary. Could anyone tell me in which cases it is better to > use mean square error for splitting nodes and when mean absolute error > should be preferred. > I am now using the default (MSE) version and I can see that the obtained > optimal tree is very different from the tree with the least mean absolute > error. > > Thanks in advance, > Luba > -- View this message in context: http://www.nabble.com/regression-trees%3A-mean-square-vs.-absolute-errors-tp16274094p16286639.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
