Practice The Apriori Algorithm (conceptual Steps) (13.3.4) - Advanced ML Topics & Ethical Considerations (Weeks 13)
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

The Apriori Algorithm (Conceptual Steps)

Practice - The Apriori Algorithm (Conceptual Steps)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the Apriori property?

💡 Hint: Think about how subsets relate to their parent set.

Question 2 Easy

Define Support in the context of the Apriori algorithm.

💡 Hint: Consider what percentage of your dataset needs to have this itemset for it to be considered frequent.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the Apriori property state?

If an itemset is frequent
all subsets are frequent
An itemset must contain all items to be frequent
All supersets of an infrequent itemset are frequent

💡 Hint: Think about how subsets relate to their parent set.

Question 2

True or False: Support is the proportion of transactions that contain the entire itemset.

True
False

💡 Hint: Consider what 'support' really implies in terms of transactions.

3 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Using a dataset of at least 10 transactions, illustrate the Apriori algorithm by calculating the support, confidence, and lift for a small itemset. Present your findings.

💡 Hint: Set up a transaction matrix and systematically work through calculating the support first.

Challenge 2 Hard

Discuss the trade-offs of using a low support threshold versus a high support threshold in the Apriori algorithm. What are the consequences on the number of rules generated?

💡 Hint: Think about the advantages and disadvantages of finding more data versus potential overfitting on less significant rules.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.