Practice Lab: Exploring Advanced Unsupervised Learning And Applying Pca For Data Reduction (3)
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Lab: Exploring Advanced Unsupervised Learning and Applying PCA for Data Reduction

Practice - Lab: Exploring Advanced Unsupervised Learning and Applying PCA for Data Reduction

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does GMM stand for?

💡 Hint: Remember the clustering model we discussed.

Question 2 Easy

Name one application of PCA.

💡 Hint: Think about how we represent complex data.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does GMM stand for?

Gaussian Mixture Model
Generalized Mixture Model
Gaussian Model Method

💡 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.

True
False

💡 Hint: Think about what PCA seeks to preserve.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider a dataset with numerous features. Explain how dimensionality reduction via PCA could impact your machine learning model's performance and computational efficiency.

💡 Hint: Focus on the interplay between complexity and interpretability.

Challenge 2 Hard

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.

💡 Hint: Evaluate the cluster shapes you anticipate encountering.

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Reference links

Supplementary resources to enhance your learning experience.