{
  "slug": "recall",
  "term": "Recall",
  "aliases": [
    "sensitivity",
    "true positive rate",
    "TPR"
  ],
  "definition": "Recall is the fraction of observed positive outcomes identified by a classifier at a specified decision threshold.",
  "note": "For binary failure prediction, recall = TP / (TP + FN), where FN counts observed positives missed by the classifier. It answers how many labeled failures were detected, not how many alerts were useful. If the evaluation sample has no positive outcomes, recall is undefined; report the software convention. [sklearn-recall]",
  "limitations": [
    {
      "text": "High recall alone does not bound false alarms: a classifier that labels every observation positive has recall one whenever positives exist.",
      "citationIds": [
        "sklearn-recall",
        "sklearn-pr-curve"
      ]
    }
  ],
  "related": [
    "precision",
    "false-positive-rate",
    "class-prevalence",
    "average-precision",
    "smart"
  ],
  "citations": [
    {
      "id": "sklearn-recall",
      "title": "scikit-learn: recall_score; binary recall and zero-division behavior.",
      "url": "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html",
      "accessed": "2026-10-12"
    },
    {
      "id": "sklearn-pr-curve",
      "title": "scikit-learn: precision_recall_curve; the all-positive endpoint.",
      "url": "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html",
      "accessed": "2026-10-12"
    },
    {
      "id": "hesela-prediction",
      "title": "Hesela: disk failure prediction, metric denominators and a synthetic alert-count example.",
      "url": "https://hesela.com/analyses/ml-disk-failure-prediction/",
      "accessed": "2026-10-12"
    }
  ],
  "status": "shipping",
  "advantages": [],
  "supersedes": [],
  "supersededBy": [],
  "firstSeen": null,
  "lastSeen": null,
  "updated": "2026-10-12"
}