Market Basket analysis terminology In 1,000 video rental transactions with items A, B, C, and D, the probability that Video D is rented given that Video C is rented (P(D | C)) is called:

Introduction / Context:
Association rule mining (Market Basket analysis) uses three core measures—support, confidence, and lift—to quantify relationships like “if C then D.” Understanding the difference between these measures is essential for evaluating cross-sell opportunities and designing promotions.

Given Data / Assumptions:

• A dataset of 1,000 transactions includes videos C and D among other items.
• We are interested in the conditional probability P(D | C).
• We assume standard definitions used in association rule mining.

Concept / Approach:

Support measures how often items occur together: support(C ∧ D) = count(C and D) / total_transactions. Confidence measures the rule strength when the antecedent occurs: confidence(C → D) = support(C ∧ D) / support(C) = P(D | C). Lift normalizes confidence by the base rate of D: lift(C → D) = confidence(C → D) / support(D).

Step-by-Step Solution:

Verification / Alternative check:

Any text on association rules (Apriori, FP-Growth) defines confidence as a conditional probability given the antecedent, aligning with P(D | C).

Why Other Options Are Wrong:

• Basic probability: vague and not a standard metric here.
• Support: joint occurrence frequency, P(C ∧ D), not conditional.
• Lift: a ratio that compares confidence to the baseline frequency of D.
• Conviction: another quality metric, not P(D | C).

Common Pitfalls:

• Confusing support with confidence because both use counts.
• Using high confidence rules with low support, leading to unstable insights.

Final Answer:

Confidence.