Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification

ArXi:2605.20716v1 Announce Type: new Random forests aggregate tree votes by simple majority, treating all trees as equally informative. We observe that the topological pattern along each tree's root-to-leaf decision path -- where and how often the dominant class label flips along it -- carries a signal of tree reliability that is exploitable for per-sample reweighting. The naive use of this signal is structurally confounded with the predicted class, so we propose a class-conditional ratio weighting that guarantees zero expected class bias by construction.