Re: [R] Question about rpart decision trees (being used to predict customer churn)

Hello, Isn't it totally counter-intuitive that if you penalize the error less the tree finds it? See: 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))) foo - function( i ){ tmp - rpart(cancel ~ experience,

Re: [R] Question about rpart decision trees (being used to predict customer churn)

Hello, If you do my.tree - rpart(cancel ~ experience) and then you check my.tree$frame you will note that the complexity parameter there is 0. Check ?rpart.object to get a description of what this output means. But essentially, you will not be able to break the leaf unless you set a

Re: [R] Question about rpart decision trees (being used to predict customer churn)

2009/7/27 Robert Smith robertpsmith2...@gmail.com 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.

[R] Question about rpart decision trees (being used to predict customer churn)

-- begin included message --- 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

[R] Question about rpart decision trees (being used to predict customer churn)

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