Rapid Selection of Machine Learning Models Using Greedy Cross Validation
dc.contributor.author | Soper, Daniel | |
dc.date.accessioned | 2021-12-24T18:28:43Z | |
dc.date.available | 2021-12-24T18:28:43Z | |
dc.date.issued | 2022-01-04 | |
dc.description.abstract | This paper introduces a greedy method of performing k-fold cross validation and shows how the proposed greedy method can be used to rapidly identify optimal or near-optimal machine learning (ML) models. Although many methods have been proposed that apply metaheuristic and other search methods to the hyperparameter space as a means of accelerating ML model selection, the cross-validation process itself has been overlooked as a means of rapidly identifying optimal ML models. The current study remedies this oversight by describing a simple, greedy cross validation algorithm and demonstrating that even in its simplest form, the greedy cross validation method can vastly reduce the average time required to identify an optimal or near-optimal ML model within a large set of candidate models. This substantially reduced search time is shown to hold across a variety of different ML algorithms and real-world datasets. | |
dc.format.extent | 10 pages | |
dc.identifier.doi | 10.24251/HICSS.2022.903 | |
dc.identifier.isbn | 978-0-9981331-5-7 | |
dc.identifier.uri | http://hdl.handle.net/10125/80245 | |
dc.language.iso | eng | |
dc.relation.ispartof | Proceedings of the 55th Hawaii International Conference on System Sciences | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Computational Intelligence and State-of-the-Art Data Analytics | |
dc.subject | greedy cross validation | |
dc.subject | hyperparameter optimization | |
dc.subject | machine learning | |
dc.subject | model selection | |
dc.title | Rapid Selection of Machine Learning Models Using Greedy Cross Validation | |
dc.type.dcmi | text |
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