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Confidence

Confidence is the conditional proportion of transactions containing an association rule’s consequent among those containing its antecedent.

How 2 Manning books explain it

Data Without Labels

Confidence is the conditional proportion of transactions containing the consequent among those containing the antecedent of an association rule.

How it is used
Shortlists association rules for market basket decisions by measuring how often a consequent appears when its antecedent appears.
How deep it goes
Defines confidence through transaction counts and demonstrates threshold tuning in Apriori; does not develop statistical uncertainty.
Examples
Uses five grocery invoices for rule calculations and a Python store dataset for filtering and inspecting generated rules.
What you can do after
Apply it via apyori to filter candidate rules, then interpret confidence alongside support and lift.

Read how Data Without Labels explains confidence →

Advanced Algorithms and Data Structures

Confidence is the conditional frequency with which one product is purchased when another product is purchased.

How it is used
Supports product association measurement, group merging, and follow-up recommendations in non-personalized e-commerce systems.
How deep it goes
Defines the conditional-frequency calculation and applies thresholds, but stops short of detailed recommender-system implementation.
Examples
Uses laptops with external disks, tennis rackets with tennis balls, and follow-up product suggestions.
What you can do after
Apply confidence by counting co-purchases, calculating conditional frequency, and thresholding associations for recommendations.

Read how Advanced Algorithms and Data Structures explains confidence →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.