# RuleKit: A Comprehensive Suite for Rule-Based Learning

arXiv: https://arxiv.org/abs/1908.01031 · HTML: https://mlsanity.com/paper/1908.01031/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance](https://mlsanity.com/paper/2407.14543/) (2024)
- [Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data](https://mlsanity.com/paper/2204.00497/) (2022)
- [SCARI: Separate and Conquer Algorithm for Action Rules and Recommendations Induction](https://mlsanity.com/paper/2106.05348/) (2021)
- [Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis](https://mlsanity.com/paper/2201.08987/) (2022)
- [RuleKit 2: Faster and simpler rule learning](https://mlsanity.com/paper/2504.20650/) (2025)
- [Discovering Subgroups with Exceptional Survival Characteristics](https://mlsanity.com/paper/2602.22179/) (2026)

## Abstract (arXiv)

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. A documented Java API is also provided for convenience. The software is publicly available at GitHub under GNU AGPL-3.0 license.