{
  "name": "Classifier Performance Evaluation Tool Comparison",
  "description": "Comparative matrix of six open-source statistical packages and libraries for classifier performance evaluation across R and Python, comparing computational engines, curve types, cross-validation handling, and confidence interval estimation methods compiled from official package manuals and primary publications.",
  "page": "https://casrai.org/guides/rocr",
  "publisher": "CASRAI",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "dateModified": "2026-11-14T17:01:31Z",
  "temporalCoverage": "2005/2026",
  "spatialCoverage": "Global",
  "columns": [
    {
      "name": "Package or Library",
      "unit": "",
      "description": "Software package or library name"
    },
    {
      "name": "Language Environment",
      "unit": "",
      "description": "Primary programming language and ecosystem"
    },
    {
      "name": "Core Data Class or Interface",
      "unit": "",
      "description": "Primary data structure used for model predictions and evaluations"
    },
    {
      "name": "Supported Curve Types",
      "unit": "",
      "description": "Performance curves supported natively"
    },
    {
      "name": "Cross-Validation Averaging",
      "unit": "",
      "description": "Native multi-run aggregation method"
    },
    {
      "name": "Confidence Intervals",
      "unit": "",
      "description": "Statistical inference or resampling support for curves and metrics"
    },
    {
      "name": "Computational Backend",
      "unit": "",
      "description": "Core implementation language"
    }
  ],
  "rows": [
    {
      "Package or Library": "ROCR",
      "Language Environment": "R",
      "Core Data Class or Interface": "S4 prediction and performance classes",
      "Supported Curve Types": "ROC, PR, Lift, Cost, Acc/Err vs Cutoff (25+ measures)",
      "Cross-Validation Averaging": "Vertical, horizontal, and threshold curve averaging",
      "Confidence Intervals": "Standard error, standard deviation, and boxplots across runs",
      "Computational Backend": "R and methods S4 infrastructure"
    },
    {
      "Package or Library": "pROC",
      "Language Environment": "R",
      "Core Data Class or Interface": "S3 roc and auc classes",
      "Supported Curve Types": "ROC and partial ROC curves",
      "Cross-Validation Averaging": "Averaging via resampled or pooled runs",
      "Confidence Intervals": "DeLong analytic test and bootstrap confidence intervals",
      "Computational Backend": "R with C++ extensions via Rcpp"
    },
    {
      "Package or Library": "precrec",
      "Language Environment": "R",
      "Core Data Class or Interface": "S3 mmdata, evalmod, and mscurve classes",
      "Supported Curve Types": "Precision-Recall and ROC curves",
      "Cross-Validation Averaging": "Pooled and averaged curves across multi-dataset/fold splits",
      "Confidence Intervals": "Basic bootstrap confidence intervals",
      "Computational Backend": "C++ backend via Rcpp for fast evaluation"
    },
    {
      "Package or Library": "PRROC",
      "Language Environment": "R",
      "Core Data Class or Interface": "S3 PRROC class",
      "Supported Curve Types": "ROC and Precision-Recall curves",
      "Cross-Validation Averaging": "Manual loop or external aggregation",
      "Confidence Intervals": "Bootstrap confidence intervals for curve coordinates and AUC",
      "Computational Backend": "R native implementations of interpolation algorithms"
    },
    {
      "Package or Library": "yardstick",
      "Language Environment": "R (tidymodels)",
      "Core Data Class or Interface": "Tibble / data frame with tidy evaluation",
      "Supported Curve Types": "ROC, PR, gain, lift curves",
      "Cross-Validation Averaging": "Grouped data frame aggregation across resamples",
      "Confidence Intervals": "Integration with rsample bootstrap confidence intervals",
      "Computational Backend": "R tidyverse ecosystem"
    },
    {
      "Package or Library": "scikit-learn",
      "Language Environment": "Python",
      "Core Data Class or Interface": "NumPy ndarrays and Scipy sparse matrices",
      "Supported Curve Types": "ROC, Precision-Recall, Det, Calibration curves",
      "Cross-Validation Averaging": "Manual fold looping or cross_validate metrics aggregation",
      "Confidence Intervals": "External bootstrap via resample or statsmodels",
      "Computational Backend": "Python, Cython, and C extensions"
    }
  ],
  "sources": [
    {
      "title": "ROCR CRAN Manual and Reference",
      "url": "https://cran.r-project.org/package=ROCR",
      "accessed": "2026-11-14"
    },
    {
      "title": "Sing et al. (2005) Bioinformatics Publication",
      "url": "https://doi.org/10.1093/bioinformatics/bti623",
      "accessed": "2026-11-14"
    },
    {
      "title": "Robin et al. (2011) pROC BMC Bioinformatics Publication",
      "url": "https://doi.org/10.1186/1471-2105-12-77",
      "accessed": "2026-11-14"
    },
    {
      "title": "Saito & Rehmsmeier (2017) Precrec Bioinformatics Publication",
      "url": "https://doi.org/10.1093/bioinformatics/btw570",
      "accessed": "2026-11-14"
    }
  ]
}