Practice Dimensionality Reduction (6.2) - Unsupervised Learning – Clustering & Dimensionality Reduction

Practice: Dimensionality Reduction

Lesson 6.2Unsupervised Learning – Clustering & Dimensionality Reduction

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9 questions

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6 sample questions

Question 1.
What does dimensionality reduction aim to achieve?
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Think about how high dimensions can complicate data analysis.
Question 2.
What is PCA primarily used for?
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Consider PCA's role in simplifying data.
3Question 3.
What does dimensionality reduction primarily address?
- Reducing computation time
- Increasing features
- Enhancing data sparsity
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Focus on what dimensionality reduction aims to solve.
4Question 4.
True or False: PCA assumes the relationships in data are non-linear.
- True
- False
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Think about the nature of PCA.
Question 5.
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.
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Think about the dataset characteristics and how dimensionality reduction can clarify analysis.
Question 6.
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.
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Consider exploring academic papers or project reports showcasing these applications.

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