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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does GMM stand for?
π‘ Hint: Remember the clustering model we discussed.
Question 2
Easy
Name one application of PCA.
π‘ Hint: Think about how we represent complex data.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does GMM stand for?
π‘ Hint: Focus on the probabilistic nature of this model.
Question 2
True or False: PCA aims to reduce the number of variables while retaining as much variance as possible.
π‘ Hint: Think about what PCA seeks to preserve.
Solve 2 more questions and get performance evaluation
Push your limits with challenges.
Question 1
Consider a dataset with numerous features. Explain how dimensionality reduction via PCA could impact your machine learning model's performance and computational efficiency.
π‘ Hint: Focus on the interplay between complexity and interpretability.
Question 2
You have a dataset suspected to have non-standard clusters. Would you employ K-Means or GMM? Justify your answer with concepts from this section.
π‘ Hint: Evaluate the cluster shapes you anticipate encountering.
Challenge and get performance evaluation