Hi, I am trying to implement the Adaboost.M1. algorithm as described in "The Elements of Statistical Learning" p.301 I don't use Dtettling 's library "boost" because : - I don't understande the difference beetween Logitboost and L2boost - I 'd like to use larger trees than stumps.
By using option weights set to (1/n, 1/n, ..., 1/n) in rpart or tree function, the tree obtained is trivial (just root, no split) whereas without weight or for each weight >1,trees are just fine. So here is my question : how are weights taken into account in optimal tree's discovery ? Did someone implement boosting algorithm ? Regards, Olivier Celhay - Student - Paris, France ______________________________________________ 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
