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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does dimensionality reduction aim to achieve?
💡 Hint: Think about how high dimensions can complicate data analysis.
Question 2
Easy
What is PCA primarily used for?
💡 Hint: Consider PCA's role in simplifying data.
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 dimensionality reduction primarily address?
- Reducing computation time
- Increasing features
- Enhancing data sparsity
💡 Hint: Focus on what dimensionality reduction aims to solve.
Question 2
True or False: PCA assumes the relationships in data are non-linear.
- True
- False
💡 Hint: Think about the nature of PCA.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design an experiment where dimensionality reduction would enhance the analysis of a high-dimensional dataset. Outline the steps you would take and justify your choices.
💡 Hint: Think about the dataset characteristics and how dimensionality reduction can clarify analysis.
Question 2
Investigate a real-life application that uses t-SNE or UMAP. Describe the data, the dimensionality reduction method, and the impact it had on results.
💡 Hint: Consider exploring academic papers or project reports showcasing these applications.
Challenge and get performance evaluation