ROCR

ROCR: Visualizing the performance of scoring classifiers , ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 11 articles )

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  1. Kumar, Ravindra; Srivastava, Abhishikha; Kumari, Bandana; Kumar, Manish: Prediction of $\beta$-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine (2015)
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  4. Japkowicz, Nathalie; Shah, Mohak: Evaluating learning algorithms. A classification perspective (2011)
  5. Sutton, Charles; Jordan, Michael I.: Bayesian inference for queueing networks and modeling of internet services (2011)
  6. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
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  8. Horton, Nicholas J.; Kleinman, Ken: Using R for data management, statistical analysis, and graphics. (2010)
  9. Qu, Long; Nettleton, Dan; Dekkers, Jack C.M.; Bacciu, Nicola: Variance model selection with application to joint analysis of multiple microarray datasets under false discovery rate control (2010)
  10. Wollschläger, Daniel: Foundations of data analysis with R. An application oriented introduction. (2010)
  11. Li, Sujun; Liu, Boshu; Zeng, Rong; Cai, Yudong; Li, Yixue: Predicting $O$-glycosylation sites in mammalian proteins by using SVMs (2006)