https://github.com/sumny/eagga

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models
https://github.com/sumny/eagga

automl hpo hyperparameter-optimization hyperparameter-tuning interpretable-machine-learning machine-learning multi-objective multiobjective optimization r r-package tabular-data tuning xai xgboost

Last synced: 18 days ago
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Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

• Host: GitHub
• URL: https://github.com/sumny/eagga
• Owner: sumny
• License: gpl-3.0
• Created: 2023-02-02T20:45:10.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-20T20:47:42.000Z (10 months ago)
• Last Synced: 2024-12-17T13:46:40.763Z (23 days ago)
• Topics: automl, hpo, hyperparameter-optimization, hyperparameter-tuning, interpretable-machine-learning, machine-learning, multi-objective, multiobjective, optimization, r, r-package, tabular-data, tuning, xai, xgboost
• Language: R
• Homepage: https://sumny.github.io/eagga/
• Size: 4.65 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE.md

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README

        
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output: github_document

---

# eagga

[![r-cmd-check](https://github.com/sumny/eagga/actions/workflows/r-cmd-check.yml/badge.svg?branch=main)](https://github.com/sumny/eagga/actions/workflows/r-cmd-check.yml)

[![pkgdown](https://img.shields.io/badge/Website-Documentation-blue)](https://sumny.github.io/eagga/)

This is the official implemention of the `EAGGA` algorithm as introduced in the paper: [Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models](https://doi.org/10.1145/3583131.3590380)

A good starting point is the following vignette: https://sumny.github.io/eagga/articles/eagga.html

If you use `eagga`, please cite:

```tex

@inproceedings{schneider_2023,

  author    = {Lennart Schneider and Bernd Bischl and Janek Thomas},

  title     = {Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models},

  year      = {2023},

  url       = {https://doi.org/10.1145/3583131.3590380},

  doi       = {10.1145/3583131.3590380},

  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},

  pages     = {538–547},

  series    = {GECCO '23}

}

```

You can find the original repository containing the code to replicate all results reported in the paper here: https://github.com/slds-lmu/paper_2023_eagga