# RuleKit: A Comprehensive Suite for Rule-Based Learning

## TL;DR

RuleKit is a versatile, open-source software suite that facilitates rule-based learning for various predictive tasks, combining interpretability with flexible experimental options and user-guided induction.

## Contribution

It introduces a comprehensive, user-friendly tool for rule learning applicable to classification, regression, and survival analysis, with flexible schemes and multiple interfaces.

## Key findings

- Supports classification, regression, and survival analysis
- Enables hypothesis verification through user-guided induction
- Available as Java API, R package, and RapidMiner plugin

## 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.