Variable importance, interaction measures, and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In this paper we describe new visualization techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We describe a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualizations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large. Our R package vivid (variable importance and variable interaction displays) provides an implementation.

翻译：可变重要性、互动措施和部分依赖性地块是解释统计和机器学习模型的重要摘要。本文介绍探索这些模型摘要的新可视化技术。我们建造热图和图表显示,显示不同的重要性和相互作用,共同设计这些显示,精心设计,以突出适合性的重要方面。我们描述一个新的矩阵型布局,显示所有单项和双项部分依赖性地块,以及基于以关键子集为重点的欧莱安图的替代布局。我们的新可视化是模型-不可知性,适用于回归和分类监督的学习环境。它们加强解释,即使在变量数量大的情况下也是如此。我们的R包包(可变重要性和可变互动显示)提供了一种实施。