Interpretable Architecture Neural Networks for Function Visualization
This addresses a need for scientists and researchers to better understand complex models, though it appears incremental as it builds on existing visualization tools with new structures.
The paper tackles the problem of visualizing black-box functions by introducing an interpretable architecture neural network (IANN) that allows simultaneous visualization of all input variables, overcoming the oversimplification of existing 2D or 3D plots.
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.