SymboLLM-FE用符号回归+少量LLM调用，解决了表格数据特征工程的可解释性和效率问题，比现有方法更实用。
SymboLLM-FE结合符号回归与大型语言模型，在6个真实数据集和4个Kaggle竞赛中表现优于现有AutoFE方法。该模型通过符号回归提取与目标强相关的数学公式，再利用LLM的丰富先验知识进行优化，确保特征可解释性。SymboLLM-FE采用基于统计先验的LLM优化机制，仅需个位数次的LLM调用即可解决传统AutoFE的可解释性差和迭代次数多的问题。
SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Automated Feature Engineering (AutoFE) overcomes this by automating feature generation and selection, ensuring both model performance and operational efficiency. However, traditional AutoFE often yield features with poor interpretability because they rely on blind mathematical transformations, while large language models (LLM)-based AutoFE faces challenges in requiring costly multi-round iterations to generate high-utility features to effectively enhance model performance, compounded by inherent risks of bias and hallucination. In this paper, we combine symbolic regression with LLMs for feature engineering (SymboLLM-FE) to solve these challenges. We extract mathematically expressive formulas strongly correlated with the target via symbolic regression, which can enhance model performance, then refine them by LLMs with rich prior knowledge to ensure interpretability. Empirical results on six real-world datasets and four Kaggle competitions demonstrate that SymboLLM-FE outperforms existing AutoFE. SymboLLM-FE also addresses the dual challenges of poor interpretability and numerous iterations by employing a statistical prior-grounded LLM refinement mechanism and single-digit LLM calls.