[2603.22235] ShapDBM: Exploring Decision Boundary Maps in Shapley Space

Computer Science > Human-Computer Interaction arXiv:2603.22235 (cs) [Submitted on 23 Mar 2026] Title:ShapDBM: Exploring Decision Boundary Maps in Shapley Space Authors:Luke Watkin, Daniel Archambault, Alex Telea View a PDF of the paper titled ShapDBM: Exploring Decision Boundary Maps in Shapley Space, by Luke Watkin and 1 other authors View PDF HTML (experimental) Abstract:Decision Boundary Maps (DBMs) are an effective tool for visualising machine learning classification boundaries. Yet, DBM quality strongly depends on the dimensionality reduction (DR) technique and high dimensional space used for the data points. For complex ML datasets, DR can create many mixed classes which, in turn, yield DBMs that are hard to use. We propose a new technique to compute DBMs by transforming data space into Shapley space and computing DR on it. Compared to standard DBMs computed directly from data, our maps have similar or higher quality metric values and visibly more compact, easier to explore, decision zones. Comments: Subjects: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) Cite as: arXiv:2603.22235 [cs.HC]   (or arXiv:2603.22235v1 [cs.HC] for this version)   https://doi.org/10.48550/arXiv.2603.22235 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Daniel Archambault [view email] [v1] Mon, 23 Mar 2026 17:31:20 UTC (16,901 KB) Full-text links: Access Paper: View a PDF of the paper titled ShapDBM: Exploring Decision Boundary...