Papers

AMLB: an AutoML Benchmark

Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl and Joaquin Vanschoren
Comparing different AutoML frameworks is notoriously challenging and often done incorrectly. We introduce an open and extensible benchmark that follows best practices and avoids common mistakes when comparing AutoML frameworks. We conduct a thorough comparison of 9 well-known AutoML frameworks across 71 classification and 33 regression tasks. The differences between the AutoML frameworks are explored with a multi-faceted analysis, evaluating model accuracy, its trade-offs with inference time, and framework failures. We also use Bradley-Terry trees to discover subsets of tasks where the relative AutoML framework rankings differ. The benchmark comes with an open-source tool that integrates with many AutoML frameworks and automates the empirical evaluation process end-to-end: from framework installation and resource allocation to in-depth evaluation. The benchmark uses public data sets, can be easily extended with other AutoML frameworks and tasks, and has a website with up-to-date results.
@misc{https://doi.org/10.48550/arxiv.2207.12560,
  doi = {10.48550/ARXIV.2207.12560},
  url = {https://arxiv.org/abs/2207.12560},
  author = {Gijsbers, Pieter and Bueno, Marcos L. P. and Coors, Stefan and LeDell, Erin and Poirier, S\'{e}bastien and Thomas, Janek and Bischl, Bernd and Vanschoren, Joaquin},
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {AMLB: an AutoML Benchmark},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

An Open Source AutoML Benchmark

Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren
In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done incorrectly. We introduce an open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes. The framework is open-source, uses public datasets and has a website with up-to-date results. We use the framework to conduct a thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.
@article{amlb2019,
  title={An Open Source AutoML Benchmark},
  author={Gijsbers, P. and LeDell, E. and Poirier, S. and Thomas, J. and Bischl, B. and Vanschoren, J.},
  journal={arXiv preprint arXiv:1907.00909 [cs.LG]},
  url={https://arxiv.org/abs/1907.00909},
  note={Accepted at AutoML Workshop at ICML 2019},
  year={2019}
}