False negative

As explained in First-Party Data Activation

A false negative is a classification outcome in which the system fails to identify two records as belonging to the same individual.

A false negative occurs when identity resolution fails to recognize that two records belong to the same individual. The missed connection can hide valuable customer information, distort analysis, and create missed marketing opportunities. section 6.1

A missed customer opportunity

If Target fails to connect Brittany’s records, it may miss signals such as an upcoming apartment move. A competitor such as Walmart could use that opportunity to win her business. section 6.1

Figure 6.2 Identify resolution confusion matrix

Use the confusion matrix to compare false negatives with true positives and false positives when discussing identity-resolution risk and thresholds. section 6.1

How First-Party Data Activation covers it

How deep it goes

Defines the outcome, business consequences, and confusion-matrix role without detailing matching algorithms.

Examples

Uses Brittany, Target, Walmart, household activity, and an apartment move to illustrate missed recognition and conversion.

What it assumes

Assumes basic familiarity with records, individuals, and identity resolution.

What you can do after

Evaluate identity-resolution results and discuss thresholds using a confusion matrix.

How it connects

Connects false negatives with identity resolution, true positives, false positives, confusion matrices, and customer lifetime value.

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Also explained in

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  Grokking Machine LearningA false negative is a classification error in which a model predicts the negative class for a positive example.
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  Outlier Detection in PythonA prediction that no issue exists when an issue is present.
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  Effective Data AnalysisA Type 2 error is a false-negative conclusion that fails to reject a false null hypothesis or fails to detect an effect that is present.
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  Statistics Every Programmer NeedsA false negative is a positive-class observation incorrectly classified as negative, or the corresponding count in a classification summary.
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  Natural Language Processing in Action, Second EditionA false negative classification error occurs when a system fails to identify an instance that belongs to the target class.
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  Experimentation for EngineersA false negative is an error in which a test fails to identify that a better system version should be adopted.
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  Machine Learning System DesignA classification or alert failing to indicate a condition that is actually present.
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  Unit Testing Principles, Practices, and PatternsA false negative is a test result that fails to reveal a defect that exists in the tested functionality.
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  The Art of AI Product DevelopmentA false negative is an instance that should be assigned to a positive class or category but is not identified as such.
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  Grokking StatisticsA false negative is an incorrect negative classification in which a system fails to indicate that an event is present.
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  Deep Learning with PyTorch, Second EditionAn outcome that is predicted as not belonging to the positive class even though it is actually of interest or positive.
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  Financial AI in PracticeA false negative is a negative classification that incorrectly labels an actual positive case as negative.

Compare how 13 books explain false negative →

Related concepts

• false positivecontrasts with · A false positive is a result that incorrectly indicates a condition or class is present when it is absent.
• bloom filtercontrasts with · A Bloom filter is a space-efficient probabilistic data structure for testing set membership that permits false positives but not false negatives.
• confusion matrixpart of · A confusion matrix is a table that cross-tabulates actual and predicted classes to summarize classification outcomes.
• failurekind of · A failure is an unsuccessful outcome or condition in which an operation, process, or system does not achieve an expected result.
• protection against regressionsaddressed by · Protection against regressions is the extent to which a test detects defects that cause previously working functionality to stop working as intended after a change.

This explainer was generated by Manning's knowledge graph from the text of First-Party Data Activation. The section links point to the parts of the book it draws on.