We present vivid, an R package for visualizing variable importance and variable interactions in machine learning models. The package provides a range of displays including heatmap and graph-based displays for viewing variable importance and interaction jointly and partial dependence plots in both a matrix layout and an alternative layout emphasizing important variable subsets. With the intention of increasing a machine learning models' interpretability and making the work applicable to a wider readership, we discuss the design choices behind our implementation by focusing on the package structure and providing an in-depth look at the package functions and key features. We also provide a practical illustration of the software in use on a data set.

翻译：摘要：本文介绍了vivid，一个用于可视化机器学习模型中变量重要性与变量交互的R包。该包提供了一系列可视化图表，包括热力图和基于图形的展示，可同时呈现变量重要性与交互信息；还提供了矩阵布局以及强调重要变量子集的替代布局下的偏依赖图。为提升机器学习模型的可解释性，并使工作适用于更广泛的读者群体，我们详细阐述了实现过程中的设计选择，重点介绍了包的结构，并深入解析了包函数及其关键特性。此外，我们还通过实际数据集演示了该软件的应用。