Hi, I'm analysing some anthropometric data on fifty odd skull bases. We know the
gender of each skull, and we are trying to develop a predictor to identify the sex of unknown skulls. Rpart with cross-validation produces two models - one of which predicts gender for Males well, and Females poorly, and the other does the opposite (Females well, and Males poorly). In both cases the error rate for the worse predicted gender is close to 50%, and for the better predicted gender about 15%. Bagging tree models produces a model which classifies both males and females equally well (or equally poorly), but has an overall error rate (just over 30%) higher than either of the rpart models (about 25%). My instinct is to go for the bagging results, as they seem more reasonable, but my colleagues really like the lower overall error rate. Any thoughts? Ta, Anthony Staines -- Dr. Anthony Staines, Senior Lecturer in Epidemiology. School of Public Health and Population Sciences, UCD, Earlsfort Terrace, Dublin 2, Ireland. Tel:- +353 1 716 7345. Fax:- +353 1 716 7407 Mobile:- +353 86 606 9713 Web:- http://phm.ucd.ie [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
