Cost Curves: An Improved Method for Visualizing Classifier Performance.

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TypeArticle
ConferenceMachine Learning, October 2006.
VolumeVolume 65, Number 1
Subjectperformance evaluation; classifiers; ROC curves; machine learning
AbstractThis paper introduces cost curves, a graphical technique for visualizing the performance (error rate or expected cost) of 2-class classifiers over the full range of possible class distributions and misclassification costs. Cost curves are shown to be superior to ROC curves for visualizing classifier performance for most purposes. This is because they visually support several crucial types of performance assessment that cannot be done easily with ROC curves, such as showing confidence intervals on a classifier's performance, and visualizing the statistical significance of the difference in performance of two classifiers. A software tool supporting all the cost curve analysis described in this paper is available from the authors.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48297
NPARC number5764335
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Record identifierb5356640-4abf-4fb7-b55b-1ee4e7765a5b
Record created2009-03-29
Record modified2016-05-09
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