Confusion matrix

As explained in First-Party Data Activation

A confusion matrix is a table that organizes the counts or shares of correct and incorrect outcomes for a classification system.

A confusion matrix organizes classification outcomes into four cells, creating shared language for assessing identity-resolution matches and their risks. section 6.1

For identity resolution, each quadrant represents a different matching result:

• True positive: correctly links two records to the same individual—the desired outcome. section 6.1
• False negative: misses a genuine match, causing missed opportunities and inaccurate analysis. section 6.1
• False positive: links different people incorrectly, risking poor customer experiences and privacy violations. section 6.1
• True negative: correctly identifies two records as belonging to different individuals. section 6.1

Retail identity resolution

If activity from a shared household IP address is wrongly attributed to one customer, false positives can produce irrelevant recommendations; false negatives can hide a valuable customer or life-event opportunity. section 6.1

Figure 6.2 Identify resolution confusion matrix

View the two-by-two identity-resolution matrix and its quadrant labels. section 6.1

How First-Party Data Activation covers it

How deep it goes

Explains all four outcomes and their business, analytical, customer, and regulatory consequences.

Examples

Uses identity-resolution sampling tests and a shared-household IP attribution scenario.

What you can do after

Evaluate identity-resolution risks and choose thresholds that balance false positives against false negatives.

How it connects

Connects classification outcomes with identity resolution, data analysis, data privacy, and customer experience.

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Also explained in

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  Starting Data Analytics with Generative AI and PythonA confusion matrix is a table comparing actual classes with predicted classes to summarize classification outcomes.
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  Grokking Machine LearningA tabular representation of classification outcomes divided into true-positive, true-negative, false-positive, and false-negative counts.
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  Statistics Slam DunkA confusion matrix is a table that cross-classifies predicted and observed classes to count correct and incorrect classifications.
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  Statistics Every Programmer NeedsA confusion matrix is a table that summarizes classification results by cross-tabulating actual classes against predicted classes.
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  Knowledge Graphs and LLMs in ActionA confusion matrix is a tabular summary of predicted and actual class assignments, including counts of correct and incorrect classifications.
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  Natural Language Processing in Action, Second EditionA confusion matrix is a table counting predictions by their actual and predicted classes.
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  Grokking StatisticsA confusion matrix is a table that cross-tabulates predicted classes against actual classes and counts correct and incorrect classifications.
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  Machine Learning Algorithms in DepthA confusion matrix is a table counting predictions by combinations of predicted and actual classes.
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  Graph Algorithms for Data ScienceA confusion matrix is a table counting classification outcomes by actual class and predicted class.
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  Data-Driven CybersecurityA confusion matrix is a table that cross-classifies predicted classes against actual classes to show correct and incorrect classification outcomes.
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  Effective Conversational AIA confusion matrix is a tabular summary comparing a classifier's predicted categories with the authoritative categories for evaluation examples.
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  Deep Learning with PyTorch, Second EditionA 2D breakdown of classification outcomes into true/false positives/negatives used to analyze model predictions.
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  Financial AI in PracticeA table that summarizes classification results by comparing predicted classes with actual classes.

Compare how 14 books explain confusion matrix →

Related concepts

• false positivehas part · A false positive is a result that incorrectly indicates a condition or class is present when it is absent.
• classifiermeasures · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• true positivehas part · A true positive is a positive identification or prediction that correctly indicates the presence of the condition, class, or item being sought.
• false negativehas part · A false negative is an outcome that incorrectly indicates a target condition is absent when it is actually present.
• true negativehas part · A true negative is a classification outcome in which an instance that actually belongs to the negative class is correctly predicted as negative.
• language modelmeasures · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

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.