RuleKit 2: Faster and simpler rule learning
RuleKit suffers from low computational efficiency, poor usability, and limited task coverage (e.g., lack of support for regression and survival analysis). To address these limitations, we propose RuleKit 2—the first high-performance, interpretable rule learning framework supporting classification, regression, and survival analysis. Methodologically, it features algorithmic enhancements to AQ and CN2, multi-threaded parallelization, and low-level Cython acceleration, achieving up to 100× speedup on certain tasks. It introduces a dual-interface design: a native Python package fully compliant with the scikit-learn API and a lightweight Streamlit-based web GUI. Additionally, it enables end-to-end visual modeling and rule interpretation. The open-source implementation (GNU AGPLv3) has been integrated into multiple research and industrial pipelines, substantially improving the practicality, accessibility, and adoption of rule-based learning tools.