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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is K-Means clustering?
💡 Hint: Think about how it groups similar data points.
Question 2
Easy
Define a dendrogram.
💡 Hint: It visually depicts relationships between clusters.
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 K-Means require before clustering?
- Predefined clusters
- Predefined features
- Predefined distance metric
💡 Hint: Think about how K-Means initializes its process.
Question 2
True or False: DBSCAN can handle clusters of varying shapes.
- True
- False
💡 Hint: Consider the definition of how DBSCAN clusters data.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset of customer purchase records with both continuous and categorical variables. Propose a strategy for using K-Means and explain how you would preprocess the data.
💡 Hint: Consider how encoding influences distance calculations.
Question 2
Imagine a dataset where clusters have various densities. Discuss how you would use DBSCAN effectively, identifying the parameters to tune.
💡 Hint: Think about how to handle points that might not fit into clusters.
Challenge and get performance evaluation