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github_document\n---\n\n# eagga\n\n\u003c!-- badges: start --\u003e\n[![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)\n[![pkgdown](https://img.shields.io/badge/Website-Documentation-blue)](https://sumny.github.io/eagga/)\n\u003c!-- badges: end --\u003e\n\nThis 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)\n\nA good starting point is the following vignette: https://sumny.github.io/eagga/articles/eagga.html\n\nIf you use `eagga`, please cite:\n\n```tex\n@inproceedings{schneider_2023,\n  author    = {Lennart Schneider and Bernd Bischl and Janek Thomas},\n  title     = {Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models},\n  year      = {2023},\n  url       = {https://doi.org/10.1145/3583131.3590380},\n  doi       = {10.1145/3583131.3590380},\n  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},\n  pages     = {538–547},\n  series    = {GECCO '23}\n}\n```\n\nYou 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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsumny%2Feagga","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsumny%2Feagga","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsumny%2Feagga/lists"}