用等式图探索符号回归中的表达式构建块，提升模型可解释性。
rEGGression: an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models
- 基于等式图压缩存储大量符号表达式，支持高效查询与匹配。
- 可交互探索近似最优解，发现隐藏的表达式模式与结构。
- 适合关注模型可解释性的研究人员，尤其擅长发现关键构建模块。
回归分析用于预测及理解自变量对因变量的影响。符号回归（SR）自动搜索非线性回归模型，生成一组在精度与可解释性之间权衡的假设。许多SR实现返回帕累托前沿，但会忽略接近非支配的替代方案，限制选择范围。等式图（e-graphs）能通过高效处理多个表达式中共有的子结构，紧凑表示大规模表达式集合。e-graphs可高效存储和查询单次或多次遗传编程（GP）运行中访问的所有SR候选解，使分析更大规模的候选解成为可能。我们提出rEGGression，一个利用e-graphs的工具，支持对大量符号表达式的探索，具备查询、过滤和模式匹配功能，提供交互式体验以洞察SR模型。其核心优势在于聚焦搜索过程中发现的构建块，帮助专家揭示研究现象的深层规律，这得益于e-graph数据结构的模式匹配能力。
原文摘要 · Abstract (English)
Regression analysis is used for prediction and to understand the effect of independent variables on dependent variables. Symbolic regression (SR) automates the search for non-linear regression models, delivering a set of hypotheses that balances accuracy with the possibility to understand the phenomena. Many SR implementations return a Pareto front allowing the choice of the best trade-off. However, this hides alternatives that are close to non-domination, limiting these choices. Equality graphs (e-graphs) allow to represent large sets of expressions compactly by efficiently handling duplicated parts occurring in multiple expressions. E-graphs allow to store and query all SR solution candidates visited in one or multiple GP runs efficiently and open the possibility to analyse much larger sets of SR solution candidates. We introduce rEGGression, a tool using e-graphs to enable the exploration of a large set of symbolic expressions which provides querying, filtering, and pattern matching features creating an interactive experience to gain insights about SR models. The main highlight is its focus in the exploration of the building blocks found during the search that can help the experts to find insights about the studied phenomena.This is possible by exploiting the pattern matching capability of the e-graph data structure.
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