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8 Empirical results

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agudys edited this page Dec 8, 2019 · 4 revisions

Experiments were performed in stratified 10-fold cross validation scheme on 50 classification and 48 regression datasets. Following software packages were evalulated.

Classification:

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

Regression:

  • RuleKit,
  • M5Rules.

All algorithms were run with default parameters except M5Rules for which -R switch was used to force constants in conclusions instead of linear models. This was due to fact, that RuleKit also generates constant conclusions. Detailed results of all investigated algorithms on particular datasets can be found here.

Table of contents

Home

  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
    1. Defining user's knowledge
    2. Examples from GuideR paper
  7. Library API
    1. Running an experiment
    2. Developing a new algorithm
  8. Empirical results
  9. Contrast set mining
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