论文提出 Surrogate Quality Score：兼顾精度与复杂度的模型选择指标
Surrogate Quality Score: A Novel Composite Performance Metric for Model Selection
挑模型时只看精度容易选到难维护的复杂模型，这篇论文给的 SQS 一个分数同时算两个维度，还能自己调偏好权重。
arXiv 论文 2611.08937 提出 Surrogate Quality Score（SQS），用于工程场景中的机器学习模型选择。SQS 将预测精度与模型复杂度两项指标归一化后加权相乘，输出统一分数。用户可通过一个自定义参数显式控制精度与复杂度之间的权衡，把人的偏好纳入选择过程。论文论证了该公式的合理性并用示例演示了 SQS 的用法。
Surrogate Quality Score: A Novel Composite Performance Metric for Model Selection
The growing reliance on high-fidelity, state-of-the-art machine learning based models for engineering decision-making necessitates robust and systematic approaches for model selection. While high predictive accuracy at untrained points is a necessary trait in the final model, other attributes such as model complexity, forecastability, explainability, scalability, and physics-awareness may be equally important or relevant criteria depending on application context and user requirements. In this work, we propose a novel composite performance metric, Surrogate Quality Score (SQS) for model selection based on predictive accuracy and model complexity simultaneously. SQS heuristically integrates these two attributes through a weighted product of normalized measures, yielding a unified score to reflect overall model quality. A user-specified parameter in SQS enables explicit control over the accuracy-complexity trade-off, thereby incorporating human judgement and preference in the model selection process. We extensively discuss and justify the formulation of SQS and demonstrate its utility through an illustrative example. By combining predictive accuracy and model complexity, SQS offers a systematic and objective approach for model selection based on both attributes.