用可解释方法预测变量在分布不同位置的响应，提升安全关键场景的模型可信度。
Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles
- 将符号回归拓展至分位数预测，生成可解释的条件分位数模型。
- 在多个数据集上表现优于透明模型，接近黑箱基准性能。
- 适用于需要理解特征在极端与中心值影响差异的高风险场景。
符号回归（SR）是一种生成可解释预测模型的经典框架。尽管已有成功应用在预测结果均值方面，但如何利用它来估计目标变量分布中其他位置（如中位数或极值）的变量关系尚不明确。这些分位数估计能更全面揭示预测变量对结果的影响，对高风险、安全关键领域至关重要。本文提出符号分位数回归（SQR），用于通过符号回归预测条件分位数。在广泛评估中，SQR在保持透明性的同时，超越了现有透明模型，且性能接近强黑箱基线。我们还通过航空燃油消耗案例研究，展示了SQR如何通过比较极端值与中心值预测模型，解释目标分布的差异。结论表明，SQR适用于条件分位数预测，并能揭示特征在不同分位数下的有趣影响。
原文摘要 · Abstract (English)
Symbolic Regression (SR) is a well-established framework for generating interpretable or white-box predictive models. Although SR has been successfully applied to create interpretable estimates of the average of the outcome, it is currently not well understood how it can be used to estimate the relationship between variables at other points in the distribution of the target variable. Such estimates of e.g. the median or an extreme value provide a fuller picture of how predictive variables affect the outcome and are necessary in high-stakes, safety-critical application domains. This study introduces Symbolic Quantile Regression (SQR), an approach to predict conditional quantiles with SR. In an extensive evaluation, we find that SQR outperforms transparent models and performs comparably to a strong black-box baseline without compromising transparency. We also show how SQR can be used to explain differences in the target distribution by comparing models that predict extreme and central outcomes in an airline fuel usage case study. We conclude that SQR is suitable for predicting conditional quantiles and understanding interesting feature influences at varying quantiles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。