RuleKit: A Comprehensive Suite for Rule-Based Learning
This provides a comprehensive suite for researchers and practitioners needing interpretable models, though it is incremental as it builds on existing sequential covering algorithms.
The authors tackled the need for a versatile rule-based learning tool by developing RuleKit, which supports classification, regression, and survival problems, and is available as open-source software under the GNU AGPL-3.0 license.
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.