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Discussion of “visualizing statistical models: Removing the blindfold”

Some of the concepts discussed are fairly common in applied data analysis—for example, it is common to visualize the boundaries of a decision rule. Other ideas are more infrequently applied, like taking “grand tours” through the high-dimensional data space through a sequence of twodimensional slices. In general, the intuition of displaying as much data as possible is natural among practicing data analysts, but the intuition is frequently based on ad hoc experience. This paper is a nice synthesis of these intuitive ideas and provides some examples of new potential visualizations that expose specific features of complex models. Our intuition agrees with Wickham and colleagues: that displaying more data is better than displaying less. In considering the paper we were motivated by the question: What is the objective of visualizing models?. In their culminating example in Section 6, Wickham and colleagues use grand tours and decision boundary plots to understand the operating characteristics of the neural network procedure. The path suggested by this example moves from data visualization, to model fitting, to model visualization, and culminates in model understanding (Fig. 1, teal path). We envision this path as a useful approach for teaching about model fitting methods on idealized data sets. For example, visualizing the hidden nodes within the neural network (Wickham and colleagues Figure 22) highlights