ROCR: visualizing classifier performance in R | Zendy

Tobias Sing | Zendy; Oliver Sander | Zendy; Niko Beerenwinkel | Zendy; Thomas Lengauer | Zendy

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ROCR: visualizing classifier performance in R

Author(s) -

Tobias Sing,

Oliver Sander,

Niko Beerenwinkel,

Thomas Lengauer

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti623

Subject(s) - computer science , receiver operating characteristic , lift (data mining) , graphics , classifier (uml) , flexibility (engineering) , precision and recall , artificial intelligence , machine learning , data mining , software , pattern recognition (psychology) , computer graphics (images) , programming language , statistics , mathematics

ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage.

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