RuleKit: A Comprehensive Suite for Rule-Based Learning

Abstract

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.

arXiv comment5 pages, 3 figures

References in corpus (1)

• GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings

Cited by in corpus (2)

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• Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance