Computational experiments for the paper "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost" (IJCNN 2021)
To cite this work please use:
@inproceedings{DBLP:conf/ijcnn/FerreiraPMPC21,
author = {Lu{\'{\i}}s Ferreira and
Andr{\'{e}} Luiz Pilastri and
Carlos Manuel Martins and
Pedro Miguel Pires and
Paulo Cortez},
title = {A Comparison of AutoML Tools for Machine Learning, Deep Learning and
XGBoost},
booktitle = {International Joint Conference on Neural Networks, {IJCNN} 2021, Shenzhen,
China, July 18-22, 2021},
pages = {1--8},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/IJCNN52387.2021.9534091},
doi = {10.1109/IJCNN52387.2021.9534091},
}
- The code that was used to generate all the benchmark models is inside the data folder and its subfolders.
- Inside the data folder, there is a subfolder for each of the datasets used for the benchmark.
- Inside the datasets subfolders, there is one subfolder for each AutoML tool used for that dataset.
- Inside the tools subfolders, there is the script used to generate the ML models and the resulting metadata (e.g., model leaderboards, performance metrics)
project
└───aux_functions: scripts to divide the original datasets into folds
│ join_data.py
│ split_data.py
└───docs: PDF of the IJCNN paper and other documentation (e.g., list of OpenML datasets, AutoML tools descriptions)
└───data:
└───dataset A
└───AutoML Tool A
│ run.py: script to run the experiment
└─── fold 1
| model leaderboard
| performance metrics
| other metadata files
└─── fold 2
└─── fold 3
└─── ....
└───AutoML Tool B
└───AutoML Tool C
└───......
└───dataset B
└───dataset C
└───.....
│ README.md
│ requirements.txt