Variable importance in binary regression trees and forests

Details Variable importance in binary regression trees and forests Variable importance in binary regression trees and forests

2007-11-15

arxiv.org/abs/0711.2434v1

Electronic Journal of Statistics 2007, Vol. 1, 519-537

Statistics

Machine Learning

Published in at http://dx.doi.org/10.1214/07-EJS039 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by t

Scientific paper

10.1214/07-EJS039

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.

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