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. The software is available at GitHub (https://github.com/adaa-polsl/RuleKit) under GNU AGPL-3.0 license.

基于规则的模型通常用于数据分析，因为它们结合了可解释性和预测能力。我们介绍RuleKit，这是一种用于规则学习的通用工具。基于顺序覆盖归纳算法，它适用于分类，回归和生存问题。用户引导的归纳的存在有助于验证关于预期或感兴趣的数据依赖性的假设。强大而灵活的实验环境允许直接研究不同的诱导方案。可以通过RapidMiner插件或R包以批处理模式执行分析。该软件可通过GNU AGPL-3.0许可在GitHub（https://github.com/adaa-polsl/RuleKit）上获得。