Benchmarking Auto-WEKA on a Commodity Machine

João Freitas, Nuno Lavado, Jorge Bernardino

Abstract

Machine Learning model building is an important and complex task in Data Science but also a good target for automation as recently exploited by AutoML. In general, free and open-source packages offer a joint space of learning algorithms and their respective hyperparameter settings and an optimization method for model search and tuning. In this paper, Auto-WEKA’s performance has been tested by running it for short periods of time (5, 15 and 30 minutes) using a commodity machine and suitable datasets with a limited number of observations and features. Benchmarking was performed against the best human-generated solution available in OpenML for each selected dataset. We concluded that increasing the overall time budget available over the previous values didn’t improve significantly classifiers’ performance.

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Paper Citation


in Harvard Style

Freitas J., Lavado N. and Bernardino J. (2018). Benchmarking Auto-WEKA on a Commodity Machine.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 180-186. DOI: 10.5220/0006914801800186


in Bibtex Style

@conference{data18,
author={João Freitas and Nuno Lavado and Jorge Bernardino},
title={Benchmarking Auto-WEKA on a Commodity Machine},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={180-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006914801800186},
isbn={978-989-758-318-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Benchmarking Auto-WEKA on a Commodity Machine
SN - 978-989-758-318-6
AU - Freitas J.
AU - Lavado N.
AU - Bernardino J.
PY - 2018
SP - 180
EP - 186
DO - 10.5220/0006914801800186