Density-based interpretable hypercube region partitioning for mixed numeric and categorical data

Understand

Consider a structured dataset of features, such as $\{\textrm{SEX}, \textrm{INCOME}, \textrm{RACE}, \textrm{EXPERIENCE}\}$.

• A user may want to know where in the feature space observations are concentrated, and where it is sparse or empty.
• The existence of large sparse or empty regions can provide domain knowledge of soft or hard feature constraints (e.g., what is the typical income range, or that it may be unlikely to have a high income with few years of work experience).
• Also, these can suggest to the user that machine learning (ML) model predictions for data inputs in sparse or empty regions may be unreliable.