search for: kyphosis1

...not identical trees (similar topology but different cutoff points), #while all other combinations (even complete reversion, i.e. preference for the other class) #lead to totally different trees... #third approach using weights: #sorting of data to design weight vector ind<-order(kyphosis[,1]) kyphosis1<-kyphosis[ind,] summary(kyphosis1[,1]) weight<-c(rep(1,64),rep(2,17)) summary(as.factor(weight)) fit4 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis1, weights=weight) #leads to result very similar to fit2 with loss<-matrix(c(0,1,2,0),nrow=2,ncol=2) #(same tree and cutoff poi...