Practice Key Metrics for Evaluating Association Rules - 13.3.3 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 13) | Machine Learning
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13.3.3 - Key Metrics for Evaluating Association Rules

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is support in association rule mining?

💡 Hint: Think about the proportion of transactions containing the itemset.

Question 2

Easy

Define confidence in the context of association rules.

💡 Hint: Consider it a measure of reliability for the rule.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does support measure in association rule mining?

  • The frequency of an itemset
  • The reliability of a rule
  • The strength of an association

💡 Hint: Think about the basic definitions in association rule mining.

Question 2

True or False: A high confidence value for an association rule indicates a meaningful relationship.

  • True
  • False

💡 Hint: Consider the definition of confidence carefully.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Propose a new itemset {X, Y} that you suspect could have high lift based on variations in transaction data. Describe how you would test for support, confidence, and lift analytically.

💡 Hint: Reference the forms and relationships defined for support, confidence, and lift across your testing groups.

Question 2

If a grocery store has the following transactions: [ {Milk, Bread}, {Bread, Butter}, {Milk, Butter}, {Milk, Bread, Butter}, {Bread}]. Calculate support, confidence, and lift for the rule {Milk} ➔ {Butter}. What can you derive?

💡 Hint: Pay attention to how each metric defines relevance individually and in context.

Challenge and get performance evaluation