Subgroups: A Python library for Subgroup Discovery

Abstract

This manuscript introduces Subgroups, an openly accessible Python library designed to ease the use of Subgroup Discovery (SD) algorithms for machine learning and data science. The Subgroups Library offers several advantages: (1) Efficiency Enhancement: Developed in native Python, unlike other software available, the library prioritizes efficiency to ensure seamless execution of SD algorithms; (2) User-Friendly Interface: Modeled after the popular scikit-learn framework, the library boasts an intuitive interface, streamlining the utilization process for practitioners and non-expert programmers; (3) Trustworthy Algorithm Implementations: Drawing from scientific publications authored by leading experts, the Subgroups Library incorporates rigorously tested algorithmic implementations, ensuring reliability and accuracy in results; (4) Customization and Expansion: The modular architecture of the library facilitates effortless integration of additional quality measures, data structures, and SD algorithms, empowering users to tailor their analyses to specific needs and explore new avenues of research. Furthermore, the Subgroups Library has been successfully employed in diverse scientific papers and projects, underscoring its efficacy and versatility as a valuable tool for SD exploration and application.