DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine

Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncovering general dataset patterns, these methods may introduce artifacts and suffer from interpretability issues. This paper presents DimVis, a visualization tool that employs supervised Explainable Boosting Machine (EBM) models (trained on user-selected data of interest) as an interpretation assistant for DR projections. Our tool facilitates high-dimensional data analysis by providing an interpretation of feature relevance in visual …

boosting cs.hc cs.lg dataset datasets dimensionality general interpretability machine paper patterns popular stat.co tool umap visual visualization