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

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

If you use eagga, please cite:

@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