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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
Descripción
Sumario: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.