search for: brieman

In the help for rpart it says, "This differs from the tree function mainly in its handling of surrogate variables." And it says that an rpart object is a superset of a tree object. Both cite Brieman et al. 1984. Both call external code which looks like martian poetry to me. I've seen posts in the archives where BDR, and other knowledgeable folks, have said that rpart() is to be prefered over tree() Is there a simple reason why? They use the same fundamental algorithm. Are there differenc...

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others highly associated with them. This strategy is most helpful with single decision trees where the opportunity for variables to pa...

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]