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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does GMM stand for?
💡 Hint: Think about the type of data it deals with.
Question 2
Easy
What is the role of the mixing coefficient in GMM?
💡 Hint: Remember, it indicates how much each component contributes.
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 the GMM likelihood function represent?
- The total probability of observed data
- The choice of clustering method
- The distance between points
💡 Hint: Focus on the role of likelihood in probability.
Question 2
True or False: Soft clustering means each data point belongs exclusively to one cluster.
- True
- False
💡 Hint: Revisit the definition of soft clustering.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with two clusters represented in a 2D space, demonstrate how you would fit a GMM to this data and explain your reasoning.
💡 Hint: Consider how the EM algorithm optimizes parameters based on observed data.
Question 2
Explain the impact of increasing the number of components in a GMM on model complexity and likelihood.
💡 Hint: Think about the balance between fitting the data well and maintaining a generalizable model.
Challenge and get performance evaluation