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exprso: an R-package for the rapid implementation of machine learning algorithms

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso, a new R package that is an intuitive machine learning suite desig...

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Detalles Bibliográficos
Autores principales: Quinn, Thomas, Tylee, Daniel, Glatt, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832912/
https://www.ncbi.nlm.nih.gov/pubmed/29560250
http://dx.doi.org/10.12688/f1000research.9893.2
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author Quinn, Thomas
Tylee, Daniel
Glatt, Stephen
author_facet Quinn, Thomas
Tylee, Daniel
Glatt, Stephen
author_sort Quinn, Thomas
collection PubMed
description Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso, a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.
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spelling pubmed-58329122018-03-19 exprso: an R-package for the rapid implementation of machine learning algorithms Quinn, Thomas Tylee, Daniel Glatt, Stephen F1000Res Software Tool Article Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso, a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes. F1000 Research Limited 2017-12-06 /pmc/articles/PMC5832912/ /pubmed/29560250 http://dx.doi.org/10.12688/f1000research.9893.2 Text en Copyright: © 2017 Quinn T et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software Tool Article
Quinn, Thomas
Tylee, Daniel
Glatt, Stephen
exprso: an R-package for the rapid implementation of machine learning algorithms
title exprso: an R-package for the rapid implementation of machine learning algorithms
title_full exprso: an R-package for the rapid implementation of machine learning algorithms
title_fullStr exprso: an R-package for the rapid implementation of machine learning algorithms
title_full_unstemmed exprso: an R-package for the rapid implementation of machine learning algorithms
title_short exprso: an R-package for the rapid implementation of machine learning algorithms
title_sort exprso: an r-package for the rapid implementation of machine learning algorithms
topic Software Tool Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832912/
https://www.ncbi.nlm.nih.gov/pubmed/29560250
http://dx.doi.org/10.12688/f1000research.9893.2
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