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:我们生动地展示一个R套件,用于可视化机器学习模型中的可变重要性和可变互动;该套件提供一系列显示,包括热图和图形显示,用于在矩阵布局和强调重要可变子集的替代布局中查看可变重要性和共同及部分依赖性地块,以及共同和部分依赖性地块。为了增加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,我们讨论了我们实施后的各种设计选择,侧重于软件包结构,并深入分析软件包的功能和关键特征。我们还提供了一套数据集所用软件的实用示例。