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
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