How do classification models ‘see’ our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes; however, our current set of visualization techniques makes this prospect difficult. In this work, we propose a hybrid supervised-unsupervised technique distinctly suited to visualizing the decision boundaries determined by classification problems. This method provides a human-interpretable map that can be analyzed qualitatively and quantitatively, which we demonstrate through several established examples from literature. While we discuss this method in the context of chemistry-driven problems, its application can be generalized across subfields for ‘unboxing’ the operations of machine-learning classification models.