Practice Cluster Evaluation Metrics - 6.1.3 | 6. Unsupervised Learning – Clustering & Dimensionality Reduction | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does the Silhouette Score measure?

💡 Hint: Think about cluster similarity.

Question 2

Easy

What does a lower Davies-Bouldin Index imply?

💡 Hint: Remember, lower is better!

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 the Silhouette Score range from?

  • 0 to 1
  • -1 to 1
  • 1 to 10

💡 Hint: Think about the lowest and highest scores.

Question 2

True or False: A lower Davies-Bouldin Index signifies better clustering.

  • True
  • False

💡 Hint: How do we want our index to behave?

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset with several distinct natural clusters. Upon using K-Means, you find a Davies-Bouldin Index of 1.5 and a Silhouette Score of 0.3. What actions could you take to improve your clustering?

💡 Hint: Think about what these indices tell you about the current clustering quality.

Question 2

In an analysis, you're applying the Elbow Method and notice the elbow point is at K=4. However, you notice better cluster separation visually at K=6. How do you decide which K to use?

💡 Hint: Think of balancing visual observations with quantitative evaluations.

Challenge and get performance evaluation