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RuleKit

Rule-based models are of large interest in data analysis as they combine interpretability with high 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 software is equipped with user-guided induction mode, which is particularly useful in verifying hypotheses concerning data. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plugin, or R package. A well-documented Java API is also provided for convenience.

Table of contents

1. Batch interface

   1. General information
   2. Parameter set definition
   3. Dataset definition
   4. Example

2. RapidMiner plugin

   1. Installation
   2. Usage
   3. Example

3. R package

   1. Installation
   2. Usage
   3. Example

4. Quality and evaluation

   1. Rule quality
   2. Model characteristics
   3. Performance metrices

5. Output files

   1. Training report
   2. Prediction performance report

6. User-guided induction

7. Library API

   References

References

Sikora, M, Wróbel, Ł, Gudyś, A (2018) GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings, Knowledge-Based Systems, 173:1-14.

Wróbel, Ł, Gudyś, A, Sikora, M (2017) Learning rule sets from survival data, BMC Bioinformatics, 18(1):285.