I'm attempting to use the
rpart R package, and I'm having difficulty figuring out how to determine the quality of a given tree output. For most linear models I would just examine p-values and $r^2$ values to determine whether the model performs satisfactorally. Is there a similar number for decision trees, or is the only performance metric available how well it can fit the data?
(Note: I'm trying to use a decision tree to fit to a continuous dataset. I guess a very relevant separate question is, whether a decision tree is appropriate for that type of data?)