Formal Hypothesis Tests for Additive Structure in Random Forests

TL;DR

This paper introduces the first formal hypothesis tests for assessing variable importance and additive structure in random forests, leveraging ensemble properties and random projections for efficient, high-power inference.

Contribution

It develops novel statistical tests for additive structure in random forests, utilizing grid structures and random projections to enable efficient and powerful inference.

Findings

First tests for regression structure in random forests.

Variance estimation integrated into ensemble construction.

Random projections enable scalable testing with large grids.

Abstract

While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference. However, the natural structure of ensemble learners like bagged trees and random forests has been shown to admit desirable asymptotic properties when base learners are built with proper subsamples. In this work, we demonstrate that by defining an appropriate grid structure on the covariate space, we may carry out formal hypothesis tests for both variable importance and underlying additive model structure. To our knowledge, these tests represent the first statistical tools for investigating the underlying regression structure in a context such as random forests. We develop notions of total and partial additivity and further demonstrate that testing can be carried out at no additional computational cost by estimating the variance within the process of constructing the ensemble. Furthermore, we propose a novel extension of these testing procedures utilizing random projections in order to allow for computationally efficient testing procedures that retain high power even when the grid size is much larger than that of the training set.