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

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Authors Daniel S Tylee, Stephen J. Glatt, Henry Loeffler-Wirth, Thomas P. Quinn, Dariusz Plewczynski, Julian Zubek
Journal/Conference Name F1000Research
Paper Category
Paper Abstract 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.
Date of publication 2016
Code Programming Language R
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