Skip to content

9 Empirical results

Jump to bottom
agudys edited this page Apr 22, 2025 · 1 revision

RuleKit 1

The experiments described in this section were included in the RuleKit paper:

Gudyś, A, Sikora, M, Wróbel, Ł (2020) RuleKit: A comprehensive suite for rule-based learning, Knowledge-Based Systems, 194: 105480

The tests were performed in stratified 10-fold cross validation scheme on 50 classification and 47 regression datasets. Following software packages were evalulated.

Classification:

  • RuleKit,
  • RIPPER,
  • CN2,
  • AQ,

Regression:

  • RuleKit,
  • M5Rules.

All algorithms were run with default parameters. Detailed results of all investigated algorithms on particular datasets can be found here.

RuleKit 2

The experiments described in this section were included in the RuleKit 2 paper.

The tests were performed in stratified 10-fold cross validation scheme on 50 classification, 48 regression, and 22 survival datasets. The results can be found here.

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

Clone this wiki locally