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.
@article{arxiv.2603.22235,
title = {ShapDBM: Exploring Decision Boundary Maps in Shapley Space},
author = {Luke Watkin and Daniel Archambault and Alex Telea},
journal= {arXiv preprint arXiv:2603.22235},
year = {2026}
}