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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is non-identifiability in the context of mixture models?
💡 Hint: Think about how different formulas can yield the same result.
Question 2
Easy
What must we ensure when using the EM algorithm?
💡 Hint: Remember the metaphor of climbing a hill?
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 is a key limitation of mixture models related to parameters?
- Non-identifiability
- Gaussianity
- Local optimality
- All of the Above
💡 Hint: Think about what complicates the interpretation of results.
Question 2
True or False: Local maxima can result in incorrect clustering solutions.
- True
- False
💡 Hint: Consider how climbing a hill works.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design an experiment to test the impact of varying K on cluster quality in a dataset with known distributions. Explain your methodology.
💡 Hint: Consider how to measure 'closeness' among clusters.
Question 2
Analyze a real-world dataset (e.g., customer data) where specifying K was challenging. What approach did you take?
💡 Hint: Think about tools you could use to assess the quality of different clusters.
Challenge and get performance evaluation