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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the primary focus of unsupervised learning?
💡 Hint: Think about the difference between supervised and unsupervised learning.
Question 2
Easy
What does a centroid represent in clustering?
💡 Hint: It's like the middle point of a group.
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 type of data does unsupervised learning utilize?
- Labeled data
- Unlabeled data
- Structured data
💡 Hint: Consider what distinguishes it from supervised learning.
Question 2
True or False: K-Means requires you to specify the number of clusters in advance.
- True
- False
💡 Hint: Reflect on how K-Means starts its process.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with various characteristics, explain how you would determine the optimal number of clusters using both the Elbow method and Silhouette analysis.
💡 Hint: Both methods approach the problem from different angles.
Question 2
Suppose you have customer data with missing values and categorical features. Describe how you would preprocess this data for K-Means and DBSCAN.
💡 Hint: Consider the nature of each algorithm while preprocessing.
Challenge and get performance evaluation