Visualization of Interval Regression for Facilitating Data and Model Insight

With growing significance of interval-valued data, interest in artificial intelligence methods tailored to this data type is similarly increasing across a range of application domains. Here, regression, i.e., the modelling of the association between interval-valued variables has been shown to be both challenging and rewarding. Beyond the mathematical challenges, fundamentals, such as the visualization of regression models, are not similarly available for interval-valued data, limiting both accessibility and utility of resulting models. Recently, the Interval Regression Graph (IRG) was introduced, providing a powerful visualization tool for interval-valued regression models. In this paper, we demonstrate the IRG in a practical data-science application, showing how it can rapidly highlight powerful insights of data. Specifically, we focus on consumer characteristics, analyzing potential relationships between their demographic characteristics and their product purchase intentions. We conclude with a brief outlook on the potential and remaining challenges of leveraging interval-valued data using fuzzy systems and artificial intelligence more broadly.