Hi,

I am using rpart decision trees to analyze customer churn. I am finding that
the decision trees created are not effective because they are not able to
recognize factors that influence churn. I have created an example situation
below. What do I need to do to for rpart to build a tree with the variable
experience? My guess is that this would happen if rpart used the loss matrix
while creating the tree.

> experience <- as.factor(c(rep("good",90), rep("bad",10)))
> cancel <- as.factor(c(rep("no",85), rep("yes",5), rep("no",5),
rep("yes",5)))
> table(experience, cancel)
          cancel
experience no yes
      bad   5   5
      good 85   5
> rpart(cancel ~ experience)
n= 100
node), split, n, loss, yval, (yprob)
      * denotes terminal node
1) root 100 10 no (0.9000000 0.1000000) *

I tried the following commands with no success.
rpart(cancel ~ experience, control=rpart.control(cp=.0001))
rpart(cancel ~ experience, parms=list(split='information'))
rpart(cancel ~ experience, parms=list(split='information'),
control=rpart.control(cp=.0001))
rpart(cancel ~ experience, parms=list(loss=matrix(c(0,1,10000,0), nrow=2,
ncol=2)))

Thanks a lot for your help.

Best regards,
Robert

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