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Related Experiment Videos

ROCR: visualizing classifier performance in R.

Tobias Sing1, Oliver Sander, Niko Beerenwinkel

  • 1Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany. tobias.sing@mpi-sb.mpg.de

Bioinformatics (Oxford, England)
|August 13, 2005
PubMed
Summary
This summary is machine-generated.

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The ROCR package offers a flexible and user-friendly tool for evaluating scoring classifier performance in R. It provides numerous performance measures and visualization options for robust data analysis.

Area of Science:

  • Bioinformatics
  • Statistical Computing

Background:

  • The ROCR package is a freely available tool for R.
  • It operates under the GNU General Public License and is platform-independent.

Purpose of the Study:

  • To introduce the ROCR package for evaluating and visualizing scoring classifier performance.
  • To highlight its flexibility, ease of use, and integration with R's graphics capabilities.

Main Methods:

  • ROCR offers over 25 performance measures that can be combined into 2D performance curves.
  • It supports standard methods like ROC graphs, precision/recall plots, lift charts, and cost curves.
  • The package integrates with R's graphics for adjustable plots.

Main Results:

  • ROCR provides a uniform framework for investigating trade-offs between performance measures.

Related Experiment Videos

  • It combines flexibility with ease of use through three core commands and sensible defaults.
  • Highly adjustable plots are achievable due to tight integration with R's graphics.

    • Conclusions:

    • ROCR is a powerful and versatile tool for performance evaluation of scoring classifiers.
    • Its design facilitates both complex analyses and straightforward application for R users.