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