{
  "slug": "false-positive-rate",
  "term": "False positive rate",
  "aliases": [
    "FPR",
    "false alarm rate"
  ],
  "definition": "The false positive rate is the fraction of observed negative cases incorrectly classified as positive at a specified decision threshold.",
  "note": "FPR = FP / (FP + TN), with FP false positives and TN true negatives, so FPR equals one minus specificity. It is undefined without observed negatives. The fraction of alerts that are false is instead FP / (TP + FP), or one minus precision when alerts exist. [sklearn-confusion] State the denominator whenever using the ambiguous phrase false alarm rate. Low FPR need not imply high precision when failures are rare. [saito2015]",
  "limitations": [],
  "related": [
    "precision",
    "recall",
    "class-prevalence",
    "smart"
  ],
  "citations": [
    {
      "id": "sklearn-confusion",
      "title": "scikit-learn: model evaluation, confusion-matrix rates.",
      "url": "https://scikit-learn.org/stable/modules/model_evaluation.html#multilabel-confusion-matrix",
      "accessed": "2026-10-12"
    },
    {
      "id": "saito2015",
      "title": "Saito and Rehmsmeier. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE 10(3): e0118432 (2015).",
      "url": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432",
      "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"
}