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Machine Learning

Unsupervised Learning

Interview questions on Unsupervised Learning.

19 questions

All subtopicsGeneral ConceptsClusteringDimensionality ReductionRecommendation EnginesAutoencoders

Clustering

Q2. What is the main difference between clustering and PCA?

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Clustering

Q5. K-means clustering:

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  1. How would you choose the value of k?
  2. If the labels are known, how would you evaluate the performance of your k-means clustering algorithm?
  3. How would you do it if the labels aren’t known?
  4. Given the following dataset, can you predict how K-means clustering works on it? Explain. image

Clustering

Q8. Why is it important to run the K-means algorithm multiple times with different initial cluster assignments, and how is the best solution selected?

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Clustering

Q11. What is Silhouette score, and How do we calculate it?

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Clustering

Q12. What are some applications of K-means clustering?

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Clustering

Q14. How do you evaluate the quality of K-means clusters?

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Clustering

Q15. k-means and GMM are both powerful clustering algorithms.

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  1. Compare the two.
  2. When would you choose one over another?

Clustering

Q16. What are the key parameters in DBSCAN, and what do they represent?

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Clustering

Q17. What is the difference between core points, border points, and noise points in DBSCAN?

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Clustering

Q18. How does DBSCAN handle clusters of different shapes?

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Clustering

Q19. What are the advantages of using DBSCAN over other clustering algorithms, such as K-means?

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