Solved – Recursive partitioning using median (instead of mean)

As I am only familiar with the basics regarding decision trees I would like to ask, with the risk of stating silly question: Is it possible to perform recursive partitioning with the group median as the response/objective?

For example, in stead of R's rpart() using means, could a similar tree be created with medians?

I want to do this because the continuous dependent variable I want to examine has a series of outliers that clearly affect the mean values (especially when the number of observations in each node gets smaller). Am I on the right track, or should I be using other kind of methods? Would preprocessing the data be another alternative (perhaps "capping" the values at a upper limit)?

You could also pre-process your data, using a transformation like the spatial sign transformation, or the rank-order transformation to minimize the impact of outliers.

Similar Posts:

Rate this post

Leave a Comment