Explainable machine learning techniques have gained increasing attention in engineering applications, especially in aerospace design and analysis, where understanding how input variables influence data-driven models is essential. Partial Dependence Plots (PDPs) are widely used for interpreting black-box models by showing the average effect of an input variable on the prediction. However, their global sensitivity metric can be misleading when strong interactions are present, as averaging tends to obscure interaction effects. To address this limitation, we propose a global sensitivity metric based on Individual Conditional Expectation (ICE) curves. The method computes the expected feature importance across ICE curves, along with their standard deviation, to more effectively capture the influence of interactions. We provide a mathematical proof demonstrating that the PDP-based sensitivity is a lower bound of the proposed ICE-based metric under truncated orthogonal polynomial expansion. In addition, we introduce an ICE-based correlation value to quantify how interactions modify the relationship between inputs and the output. Comparative evaluations were performed on three cases: a 5-variable analytical function, a 5-variable wind-turbine fatigue problem, and a 9-variable airfoil aerodynamics case, where ICE-based sensitivity was benchmarked against PDP, SHapley Additive exPlanations (SHAP), and Sobol' indices. The results show that ICE-based feature importance provides richer insights than the traditional PDP-based approach, while visual interpretations from PDP, ICE, and SHAP complement one another by offering multiple perspectives.

翻译：可解释机器学习技术在工程应用中日益受到关注，尤其在航空航天设计与分析领域，理解输入变量如何影响数据驱动模型至关重要。部分依赖图通过展示输入变量对预测的平均效应，被广泛用于解释黑盒模型。然而，当存在强交互作用时，其全局敏感性度量可能产生误导，因为平均化处理往往会掩盖交互效应。为克服这一局限，我们提出一种基于个体条件期望曲线的全局敏感性度量方法。该方法通过计算ICE曲线的期望特征重要性及其标准差，更有效地捕捉交互作用的影响。我们提供了数学证明，表明在截断正交多项式展开下，基于PDP的敏感性是所提出的ICE基度量的下界。此外，我们引入ICE基相关值来量化交互作用如何改变输入与输出之间的关系。我们在三个案例中进行了对比评估：五变量解析函数、五变量风力涡轮机疲劳问题以及九变量翼型空气动力学案例，将ICE基敏感性与PDP、SHAP可加性解释及Sobol指数进行基准比较。结果表明，ICE基特征重要性比传统PDP方法提供更丰富的洞察，而PDP、ICE和SHAP的可视化解释通过提供多重视角形成互补。