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.

翻译：决策边界图（DBMs）是可视化机器学习分类边界的有效工具。然而，DBM 的质量在很大程度上依赖于用于数据点的降维（DR）技术和高维空间。对于复杂机器学习数据集，降维会产生许多混合类别，进而导致难以使用的DBM。我们提出了一种通过将数据空间转换为沙普利空间并在其上计算降维来生成DBM的新技术。与直接从数据计算得到的标准DBM相比，我们的地图具有相似或更高质量指标值，并且决策区域明显更紧凑、更易于探索。