Finite-sample guarantees for classical random forests

Establish rigorous finite-sample performance guarantees for Breiman’s classical random forest algorithm under i.i.d. sampling, providing non-asymptotic results that quantify prediction error or risk behavior in finite samples.

Background

The paper notes that, even for i.i.d. data, the theory of classical random forests remains challenging, with important aspects not fully resolved. In particular, while asymptotic properties have been studied, non-asymptotic (finite-sample) guarantees are scarce, leaving a gap in understanding the behavior of random forests in realistic sample sizes.

This unresolved issue matters for both methodological development and practical deployment, as finite-sample guarantees underpin reliable inference, calibration, and robustness claims for widely used tree-ensemble methods.