Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783303387622670336 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5832912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT quinnthomas exprsoanrpackagefortherapidimplementationofmachinelearningalgorithms AT tyleedaniel exprsoanrpackagefortherapidimplementationofmachinelearningalgorithms AT glattstephen exprsoanrpackagefortherapidimplementationofmachinelearningalgorithms |