Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression

Abstract

Symbolic regression (SR) is a machine learning approach aimed at discovering mathematical closed-form expressions that best fit a given dataset. Traditional complexity measures in SR, such as the number of terms or expression tree depth, often fail to capture the difficulty of specific analytical tasks a user might need to perform. In this paper, we introduce a new complexity measure designed to quantify the difficulty of conducting single-feature global perturbation analysis (SGPA)—a type of analysis commonly applied in fields like physics and risk scoring to understand the global impact of perturbing individual input features. We present a unified mathematical framework that formalizes and generalizes these established practices, providing a precise method to assess how challenging it is to apply SGPA to different closed-form equations. This approach enables the definition of novel complexity metrics and constraints directly tied to this practical analytical task. Additionally, we establish a reconstruction theorem, offering potential insights for developing future optimization techniques in SR.