{
  "@type": "dcat:Dataset",
  "accessLevel": "public",
  "accrualPeriodicity": "irregular",
  "bureauCode": [
    "006:55"
  ],
  "contactPoint": {
    "fn": "Rachael Sexton",
    "hasEmail": "mailto:rachael.sexton@nist.gov"
  },
  "description": "With this tool, users can rapidly calculate binary classifier metrics like Matthew's Correlation Coefficient, F-Scores, and Average Precision Scores from scalar and binary predictions. It is primarily used to calculate threshold sensitivity studies, and has several routines to significantly speed up metric calculation (or approximation) when aggregate measures of performance over all thresholds are needed.",
  "distribution": [
    {
      "accessURL": "https://github.com/usnistgov/Contingency",
      "description": "GitHub repository within the usnistgov organization.",
      "format": "git repository",
      "title": "Contingency (source repository)"
    },
    {
      "downloadURL": "https://data.nist.gov/od/ds/mds2-4079/README-contingency.md",
      "mediaType": "text/x-web-markdown",
      "title": "README-contingency"
    }
  ],
  "identifier": "ark:/88434/mds2-4079",
  "issued": "2026-05-08",
  "keyword": [
    "accuracy",
    "approximation",
    "batched",
    "binary classification",
    "classifier",
    "contingency table",
    "error",
    "experiments",
    "machine learning",
    "marginal counts",
    "metrics",
    "performance",
    "precision",
    "recall",
    "type I",
    "type II",
    "vectorized"
  ],
  "landingPage": "https://data.nist.gov/od/id/mds2-4079",
  "language": [
    "en"
  ],
  "license": "https://www.nist.gov/open/license",
  "modified": "2026-03-19 00:00:00",
  "programCode": [
    "006:045"
  ],
  "publisher": {
    "@type": "org:Organization",
    "name": "National Institute of Standards and Technology"
  },
  "theme": [
    "Information Technology:Computational science",
    "Information Technology:Data and informatics",
    "Mathematics and Statistics:Experiment design",
    "Mathematics and Statistics:Numerical methods and software",
    "Mathematics and Statistics:Statistical analysis",
    "Metrology:Metric"
  ],
  "title": "Contingency: a python library for fast, vectorized metrology with binary contingency counts."
}