Interpretation of decision tree output

Hi, I have a question on decision trees used in the context of a retention/churn model.

If I run a decision tree (after training it on a sample data set) then what do I get as output?

In other words how can I identify where the new unclassified data have been placed in the trees branches, in order to do some actions on them (e.g customer care actions, etc.) ?.  Can I also get a score applied to each new data that has been classified according to the likelihood of churn?      

Not sure if I was clear, if you need more details please ask!

Thanks in advance, Giovanni



Use the weka predictor followed by scorer. Will give you predictions, score and false pos/neg info. but not sure it will tell u which branch of the decision tree was used for the decision!

Thanks a lot!

Basically what I need is to see how the new data have been classified and assign them a score. Will try the Weka predictor and scorer and see what I'll get...