False positive rate

Documentation Index

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Definition

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Taxonomy

• Task types: Tabular classification, text classification.
• Availability: development and monitoring.

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Why it matters

• False positive rate is crucial for understanding the model’s tendency to make incorrect positive predictions.
• It’s particularly important in applications where false positives are costly, such as medical diagnosis, fraud detection, or spam filtering.
• Lower false positive rates indicate better model performance, with 0 representing no false positives.
• This metric complements precision and recall by focusing specifically on the negative class performance.

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Required columns

• Predictions: The predicted class labels from your binary classification model
• Ground truths: The actual/true class labels

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Test configuration examples

[
  {
    "name": "False positive rate below 0.05",
    "description": "Ensure that the false positive rate is below 0.05",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "falsePositiveRate",
        "operator": "<",
        "value": 0.05
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": true,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]

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Related

• Precision test - Measure positive prediction accuracy.
• Recall test - Measure ability to find all positive instances.
• ROC AUC test - Area under the receiver operating characteristic curve.
• Accuracy test - Overall classification correctness.
• Aggregate metrics - Overview of all available metrics.