I'm using classification trees for the first time. I understand the difference between these 2 packages, but I'm having a bit of trouble interpreting the results. I have 3 different response variables, but I'll only use 1 in this discussion. I first ran Tree. I was happy with the results, 6 nodes, everything made sense. Misclassification rate of 15%. Then I ran cross-validation and it showed the optimal tree was only 2 nodes. I then ran Rpart, it provides the optimal tree with 2 nodes. But of course, this doesn't provide much explanation for the response. Additionally, the misclassification rate increased to 24%. Is it correct to say that if I want to only describe my results, I can use the Tree result. But if I want a predictive model, I should use the Rpart results, even though it had a higher misclassification? Thank you, Carol Carol Rizkalla Graduate Research Assistant 195 Marsteller St. Purdue University West Lafayette, IN 47907 (765) 494-3997 Sentiment without action is the ruin of the soul. - Edward Abbey [[alternative HTML version deleted]]