用随机森林的邻近度构建局部置信区间，提升预测可靠性。
Localized Uncertainty Quantification in Random Forests via Proximities
- 利用测试样本邻近度构造局部误差分布
- 可调覆盖度的回归置信区间，分类信任度更高
- 无需额外训练，适合高风险场景应用
在机器学习中，不确定性量化有助于评估模型预测的可靠性，尤其在高风险场景中至关重要。传统方法侧重预测准确性，而当前研究愈发关注不确定性度量。本文针对随机森林提出一种局部不确定性量化方法。现有方法多依赖分位数回归或蒙特卡洛技术，我们则利用随机森林中自然产生的袋外（OOB）误差和相似性度量（邻近度）——通常被视为副产品。具体地，通过邻近度定义附近点的局部误差分布，为回归任务生成预测区间，为分类任务生成信任得分。通过调整邻近点数量，可灵活调节置信区间覆盖度，同时反映单个预测的确定性。对于分类任务，剔除本方法判定为不可分类的样本后，模型准确率提升，且准确率-拒识AUC优于对比方法。
原文摘要 · Abstract (English)
In machine learning, uncertainty quantification helps assess the reliability of model predictions, which is important in high-stakes scenarios. Traditional approaches often emphasize predictive accuracy, but there is a growing focus on incorporating uncertainty measures. This paper addresses localized uncertainty quantification in random forests. While current methods often rely on quantile regression or Monte Carlo techniques, we propose a new approach using naturally occurring test sets and similarity measures (proximities) typically viewed as byproducts of random forests. Specifically, we form localized distributions of OOB errors around nearby points, defined using the proximities, to create prediction intervals for regression and trust scores for classification. By varying the number of nearby points, our intervals can be adjusted to achieve the desired coverage while retaining the flexibility that reflects the certainty of individual predictions. For classification, excluding points identified as unclassifiable by our method generally enhances the accuracy of the model and provides higher accuracy-rejection AUC scores than competing methods.
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