Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff

Conference · Tue Jan 01 04:00:00 EST 2019

DOI:https://doi.org/10.1109/BigData47090.2019.9006370· OSTI ID:1605684

Baughman, Matt; Chakubaji, Nifesh; Truong, Hong-Linh; Kreics, Krists; Chard, Kyle; Foster, Ian

Exponentially increasing data volumes, coupled with new modes of analysis have created significant new opportunities for data scientists. However, the stochastic nature of many data science techniques results in tradeoffs between costs and accuracy. For example, machine learning algorithms can be trained iteratively and indefinitely with diminishing returns in terms of accuracy. In this paper we explore the cost-accuracy tradeoff through three representative examples: we vary the number of models in an ensemble, the number of epochs used to train a machine learning model, and the amount of data used to train a machine learning model. We highlight the feasibility and benefits of being able to measure, quantify, and predict cost-accuracy tradeoffs by demonstrating the presence and usability of these tradeoffs in two different case studies.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: National Science Foundation (NSF)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1605684
• Country of Publication: United States
• Language: English

Related Subjects

big data analytics
machine learning
optimization
performance
quality of analytics