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Comparable Machine Learning Efficiency: Balanced Metrics for Natural Language Processing

Authors:
Daniel Schönle
Christoph Reich
Djaffar Ould Abdeslam

Keywords: machine learning, nlp, efficiency, metric, software performance, automl

Abstract:
As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables the comparison of distinct efficiency types. A quality-focused efficiency metric is introduced that considers resource consumption, computational effort, and runtime in addition to prediction quality. The metric has been successfully tested for usability, plausibility, and compensation for dataset size and host performance. This framework enables informed decisions to be made about the use and design of machine learning in an environmentally responsible and cost-effective manner.

Pages: 15 to 24

Copyright: Copyright (c) IARIA, 2023

Publication date: September 25, 2023

Published in: conference

ISSN: 2519-8483

ISBN: 978-1-68558-097-1

Location: Porto, Portugal

Dates: from September 25, 2023 to September 29, 2023