Abstract
Electronic Journal of Statistics 2007, Vol. 1, 519-537 We characterize and study variable importance (VIMP) and pairwise variable
associations in binary regression trees. A key component involves the node mean
squared error for a quantity we refer to as a maximal subtree. The theory
naturally extends from single trees to ensembles of trees and applies to
methods like random forests. This is useful because while importance values
from random forests are used to screen variables, for example they are used to
filter high throughput genomic data in Bioinformatics, very little theory
exists about their properties.
