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Exprso: An R-package for the rapid implementation of machine learning algorithms

  • Deakin University
  • SUNY Upstate Medical University

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Article number2588
JournalF1000Research
Volume5
DOIs
StatePublished - 2017

Keywords

  • Classification
  • Cross-validation
  • Genomics
  • Machine learning
  • Machine learning
  • Package
  • Prediction
  • R
  • Supervised
  • Unsupervised

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