Title: Unbiased Variable Importance for Random Forests

Author: Markus Löcher

Affiliation: Berlin School of Economics and Law

Abstract:
The default variable-importance measure in random Forests, Gini importance,
has been shown to suffer from the bias of the underlying Gini-gain splitting
criterion. While the alternative permutation importance is generally accepted
as a reliable measure of variable importance, it is also computationally
demanding and suffers from other shortcomings. We propose a simple solution to
the misleading/untrustworthy Gini importance which can be viewed as an
overfitting problem: we compute the loss reduction on the out-of-bag instead
of the in-bag training samples.