In many scientific research fields, understanding and visualizing a black-box function in terms of the effects of all the input variables is of great importance. Existing visualization tools do not allow one to visualize the effects of all the input variables simultaneously. Although one can select one or two of the input variables to visualize via a 2D or 3D plot while holding other variables fixed, this presents an oversimplified and incomplete picture of the model. To overcome this shortcoming, we present a new visualization approach using an interpretable architecture neural network (IANN) to visualize the effects of all the input variables directly and simultaneously. We propose two interpretable structures, each of which can be conveniently represented by a specific IANN, and we discuss a number of possible extensions. We also provide a Python package to implement our proposed method. The supplemental materials are available online.

翻译：在许多科学研究领域，理解和可视化黑箱函数中所有输入变量的影响至关重要。现有可视化工具无法同时展示所有输入变量的影响。尽管可以通过固定其他变量、选择一两个输入变量进行二维或三维绘图，但这会呈现简化且不完整的模型图像。为克服这一缺陷，我们提出了一种新的可视化方法，利用可解释架构神经网络（IANN）直接且同时可视化所有输入变量的影响。我们提出了两种可解释结构，每种结构均可通过特定的IANN方便表示，并讨论了若干可能的扩展。我们还提供了实现所提方法的Python软件包。补充材料在线可获取。