SeerCuts: Explainable Attribute Discretization
Name
3722212.3725132.pdf
Size
1.42 MB
Format
Adobe PDF
Checksum (MD5)
ca4ca3594324bd8c312f5ba0aa047df1
Author(s)
Lai, Eugenie
Croitoru, Inbal
Bitton, Noam
Shalem, Ariel
Youngmann, Brit
Galhotra, Sainyam
Rezig, El Kindi
Cafarella, Michael
Date Issued
June 22, 2025
Publisher
ACM|Companion of the 2025 International Conference on Management of Data
Citation
Eugenie Lai, Inbal Croitoru, Noam Bitton, Ariel Shalem, Brit Youngmann, Sainyam Galhotra, El Kindi Rezig, and Michael Cafarella. 2025. SeerCuts: Explainable Attribute Discretization. In Companion of the 2025 International Conference on Management of Data (SIGMOD/PODS '25). Association for Computing Machinery, New York, NY, USA, 143–146.
Version
Final published version
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.
Description
SIGMOD-Companion ’25, Berlin, Germany
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3722212.3725132
