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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 points are grouped based on their proximity.
Question 2
Easy
Name the three types of points in DBSCAN.
💡 Hint: Consider how each point is classified based on density.
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 unsupervised learning allow models to do?
- Learn from labeled data
- Discover patterns in unlabeled data
- Both 1 and 2
💡 Hint: Consider what type of data guides the analysis.
Question 2
Is K-Means sensitive to initial centroid placement?
- True
- False
💡 Hint: Reflect on whether the starting conditions impact the results.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with varying densities, how would you select an appropriate clustering algorithm, and why?
💡 Hint: Consider how different algorithms react to challenges in data structure.
Question 2
You have a dendrogram from hierarchical clustering showing multiple merges. Discuss how you would decide where to make the cut for clusters.
💡 Hint: Look for distance thresholds in the dendrogram that indicate promising cluster arrangements.
Challenge and get performance evaluation