Practice T-sne (t-distributed Stochastic Neighbor Embedding) (6.2.3) - Unsupervised Learning – Clustering & Dimensionality Reduction

Practice: t-SNE (t-Distributed Stochastic Neighbor Embedding)

Lesson 6.2.3Unsupervised Learning – Clustering & Dimensionality Reduction

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does t-SNE stand for?
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Think about what the 'SNE' part means.
Question 2.
What is one advantage of using t-SNE?
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Consider aspects of visual clarity.
3Question 3.
What is t-SNE used for?
- Clustering Analysis
- Dimensionality Reduction
- Both
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Think about its role in data analysis.
4Question 4.
True or False: t-SNE is ideal for large datasets.
- True
- False
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Consider its computational demands.
Question 5.
Analyze the impact of not minimizing KL divergence in t-SNE. What would the visualization look like without that step?
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Consider the importance of accurate distance mapping in data representation.
Question 6.
Design an experiment to validate the effectiveness of t-SNE over PCA for a specific dataset. What metrics would you use?
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Focus on both qualitative and quantitative aspects for comparisons.

5 more questions in this section. Enroll free to practise them all.