What is it about?
This research discusses the process of estimating how well a machine learning model must perform on the basis of a quantification of the situation in which it will be used. We develop a novel metric for comparing business cases that can be used to objectively assess which machine learning projects are likely to be easier or more difficult to deliver.
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Why is it important?
Machine learning is being used more and more across industry and academia, however, it is still engaged as a potential solution to problems in a trial-and-error fashion. This research presents tools and ideas for reducing the uncertainty in advance of starting work on the modelling process.
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This page is a summary of: Minimum Viable Model Estimates for Machine Learning Projects, December 2020, Academy and Industry Research Collaboration Center (AIRCC),
DOI: 10.5121/csit.2020.101803.
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