Assessing the accuracy of prediction algorithms for classification: an overview

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Abstract
Abstract 4 Also at the Department of Biological Sciences, University of California, Irvine, USA, to whom all correspondence should be addressed. We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction. Contact: pfbaldi@ics.uci.edu
Reference Key
openalex_W2107432340 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pierre Baldi, Søren Brunak, Yves Chauvin, Claus A. Andersen, Henrik Nielsen
Journal BMC Bioinformatics
Year 2000
DOI
10.1093/bioinformatics/16.5.412
URL
https://doi.org/10.1093/bioinformatics/16.5.412
Keywords Keywords not found

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