SeerCuts: Explainable Attribute Discretization

Abstract

This demonstration showcases SeerCuts - a tool that suggests useful and semantically meaningful discretization strategies (partitions) for numerical attributes. SeerCuts is a generic, interactive framework where users specify attributes to discretize and their utility measure for a downstream task of choice. It uses GPT-4o to assess the semantic meaningfulness of candidate partitions and employs an efficient search strategy to explore the vast space of discretization options. With hierarchical clustering to group related partitions and a multi-armed bandit policy to identify useful partitions with only a few samples, SeerCuts quickly finds meaningful and useful partitions. In the demo, we will provide an overview of SeerCuts and allow the audience to explore various datasets and tasks, including data visualization and comprehensive modeling. The users will be able to evaluate how SeerCuts identifies meaningful discretization strategies and compare the tradeoff between different discretization options.