RuleKit 2: Faster and simpler rule learning
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Abstract (arXiv)
Rules offer an invaluable combination of predictive and descriptive capabilities. Our package for rule-based data analysis, RuleKit, has proven its effectiveness in classification, regression, and survival problems. Here we present its second version. New algorithms and optimized implementations of those previously included, significantly improved the computational performance of our suite, reducing the analysis time of some data sets by two orders of magnitude. The usability of RuleKit 2 is provided by two new components: Python package and browser application with a graphical user interface. The former complies with scikit-learn, the most popular data mining library for Python, allowing RuleKit 2 to be straightforwardly integrated into existing data analysis pipelines. RuleKit 2 is available at GitHub under GNU AGPL 3 license (https://github.com/adaa-polsl/RuleKit)
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 29 Apr 2025.
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6 of 27 references.
References · 6
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2020Learning Gradient Boosted Multi-label Classification Rulescited by 11
- 2019RuleKit: A Comprehensive Suite for Rule-Based Learningcited by 6
- 2018GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingscited by 5
- 2022Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival datacited by 1
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