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

Quinn, Thomas, Tylee, Daniel and Glatt, Stephen 2017, exprso: an R-package for the rapid implementation of machine learning algorithms, F1000research, vol. 5, pp. 1-11, doi: 10.12688/f1000research.9893.2.

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Title exprso: an R-package for the rapid implementation of machine learning algorithms
Author(s) Quinn, Thomas
Tylee, Daniel
Glatt, Stephen
Journal name F1000research
Volume number 5
Article ID 2588
Start page 1
End page 11
Total pages 11
Publisher F1000 Research Ltd
Place of publication London, Eng.
Publication date 2017-12
ISSN 2046-1402
Keyword(s) R
classification
cross-validation
genomics
machine learning
package
prediction
supervised
unsupervised
Summary 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.
Language eng
DOI 10.12688/f1000research.9893.2
Field of Research 080101 Adaptive Agents and Intelligent Robotics
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Quinn T et al.
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109423

Document type: Journal Article
Collections: School of Medicine
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.