Interpretable visualizations of data spaces for classification problems
Abstract
Abstract 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.
Fields of Study & Subjects
Data Visualization and Analytics
Machine Learning in Materials Science
Computational Drug Discovery Methods
Publication details
- Journal / source
- Machine Learning Science and Technology
- Publisher
- IOP Publishing
- Volume
- 7
- Issue
- 2
- Pages
- 025008–025008
- Publication date
- 16 February 2026
- Language
- EN