Classification and Regression Trees : rpart

How to prune a decision tree ?

Prune back the tree to avoid overfitting the data. Typically, you will want to select a tree size that minimizes the cross-validated error, the xerror column printed by printcp( ).

Prune the tree to the desired size using
prune(fit, cp= )

Specifically, use printcp( ) to examine the cross-validated error results, select the complexity parameter associated with minimum error, and place it into the prune( ) function. Alternatively, you can use the code fragment

     fit$cptable[which.min(fit$cptable[,"xerror"]),"CP"]

to automatically select the complexity parameter associated with the smallest cross-validated error. Thanks to HSAUR for this idea.

http://www.mayo.edu/hsr/techrpt/61.pdf
http://www.statmethods.net/advstats/cart.html - classification, regression trees, random forests