 Simplifying Random Forests’ Probabilistic ForecastsNils Koster and Fabian Krüger The American Statistician, 2026, vol. 80, issue 1, 135-145 Abstract: Since their introduction by Breiman, Random Forests (RFs) have proven to be useful for both classification and regression tasks. The RF prediction of a previously unseen observation can be represented as a weighted sum of all training sample observations. This nearest-neighbor-type representation is useful, among other things, for constructing forecast distributions (as in Meinshausen’s Quantile Regression Forests). In this article, we consider simplifying RF-based forecast distributions by sparsifying them. That is, we focus on a small subset of k nearest neighbors while setting the remaining weights to zero. This simplification, which we refer to as “Topk”, greatly improves the interpretability of RF predictions. It can be applied to any forecasting task without re-training existing RF models. In empirical experiments, we document that the simplified predictions can be similar to or exceed the original ones in terms of forecasting performance. We explore the statistical sources of this finding via a stylized analytical model of RFs. The model suggests that simplification is particularly promising if the unknown true forecast distribution contains many small weights that are estimated imprecisely. Date: 2026 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:80:y:2026:i:1:p:135-145 Ordering information: This journal article can be ordered from DOI: 10.1080/00031305.2025.2552284 Access Statistics for this article The American Statistician is currently edited by Eric Sampson More articles in The American Statistician from Taylor & Francis Journals |
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