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agudys edited this page Jun 27, 2019 · 15 revisions

RuleKit

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 user-guided induction mode 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 under GNU AGPL-3.0 license.

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.

Table of contents

Home

  1. Batch interface
    1. General information
    2. Parameter set definition
    3. Dataset definition
    4. Example
  2. Graphical user interface
  3. Python package
  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
    1. Defining user's knowledge
    2. Examples from GuideR paper
  7. Contrast set mining
  8. Library API
  9. Empirical results
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