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:

References in zbMATH (referenced in 44 articles )

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  1. Christian Thiele; Gerrit Hirschfeld: cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R (2021) not zbMATH
  2. Díaz-Coto, Susana; Martínez-Camblor, Pablo; Pérez-Fernández, Sonia: SmoothROCtime: an (\mathsfR) package for time-dependent ROC curve estimation (2020)
  3. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  4. Huo, Yanhao; Xin, Lihui; Kang, Chuanze; Wang, Minghui; Ma, Qin; Yu, Bin: SGL-SVM: a novel method for tumor classification via support vector machine with sparse group lasso (2020)
  5. Muschelli, John III: ROC and AUC with a binary predictor: a potentially misleading metric (2020)
  6. O’Brien, Jonathon J.; Lawson, Michael T.; Schweppe, Devin K.; Qaqish, Bahjat F.: Suboptimal comparison of partitions (2020)
  7. Riahi, Fatemeh; Schulte, Oliver: Model-based exception mining for object-relational data (2020)
  8. Kang, Chuanze; Huo, Yanhao; Xin, Lihui; Tian, Baoguang; Yu, Bin: Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine (2019)
  9. Li, Zehang Richard; McCormick, Tyler H.: An expectation conditional maximization approach for Gaussian graphical models (2019)
  10. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  11. Philip Leifeld; Skyler Cranmer; Bruce Desmarais: Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals (2018) not zbMATH
  12. Singh, Alok Kumar; Lalitha, S.: A novel spatial outlier detection technique (2018)
  13. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  14. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  15. Krautenbacher, Norbert; Theis, Fabian J.; Fuchs, Christiane: Correcting classifiers for sample selection bias in two-phase case-control studies (2017)
  16. Sara Perez-Jaume; Konstantina Skaltsa; Natàlia Pallarès; Josep Carrasco: ThresholdROC: Optimum Threshold Estimation Tools for Continuous Diagnostic Tests in R (2017) not zbMATH
  17. Waldemar W. Koczkodaj, Alicja Wolny-Dominiak: RatingScaleReduction package: stepwise rating scale item reduction without predictability loss (2017) arXiv
  18. Dincer Goksuluk, Selcuk Korkmaz, Gokmen Zararsiz, A. Ergun Karaagaoglu: easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment (2016) not zbMATH
  19. Matthew Friedlander, Adrian Dobra, Helene Massam, Laurent Briollais: Analyzing Genome-wide Association Study Data with the R Package genMOSS (2016) arXiv
  20. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv

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