Simplifying Random Forests' Probabilistic Forecasts

doi:10.1080/00031305.2025.2552284

2025-10-01

Fabian Krüger *

DOI：10.1080/00031305.2025.2552284

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摘要

En 中文

自Breiman引入以来，随机森林（RFs）已被证明适用于分类和回归任务。先前未见观测的RF预测可以表示为所有训练样本观测的加权和。这种基于最近邻类型的表示，除其他用途外，还有助于构建预测分布（如Meinshausen的量化回归森林）。本文考虑通过稀疏化来简化RF基预测分布。即，我们专注于k个最近邻的小子集，同时将剩余权重设为零。这种简化（我们称之为Topk）显著提高了RF预测的可解释性。它可应用于任何预测任务，而无需重新训练现有的RF模型。在实证实验中，我们记录到简化后的预测在预测性能方面可与原始预测媲美或超越之。我们通过一个简化的RF分析模型探索了这一发现背后的统计来源。该模型表明，若未知真实预测分布包含许多被不精确估计的小权重，简化方法尤为有前景。

Keyword:

Forecast distribution

Random forests

Simplicity

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AMERICAN STATISTICIAN

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2.1

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Helmholtz Association

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13.2W

论文数: 10.7W

被引数: 145

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