# Determining regression tree quality

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?)

The only right performance measure for any method is how well it fits the data. This is what $R^2$ does for linear regression. It is also why you should not use p values to judge a model.
Trees can be used for both continuous and categorical dependent variables (and other types of DV as well). There are various measures of purity of the nodes. In a regression tree context, one such measure would be that the nodes each contain values that are similar to each other and different from those of other nodes. This relates well to the ANOVA concept of "between" and "within" error. It is also related to $R^2$, but the relationship is not quite as intuitive.