Practice K-Means Clustering - 5.4 | Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 9) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is K-Means clustering?

πŸ’‘ Hint: Think about the core idea of grouping based on distance.

Question 2

Easy

Explain the steps involved in the K-Means algorithm.

πŸ’‘ Hint: Consider the iterative nature of the algorithm.

Practice 1 more question 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 clustering primarily aim to achieve?

  • Group data points based on similarity
  • Predict future outcomes
  • Label data points
  • None of the above

πŸ’‘ Hint: Remember what K-Means does with data.

Question 2

The Elbow Method helps determine what?

  • True
  • False

πŸ’‘ Hint: Think about cluster selection techniques.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset with many outliers. Explain how K-Means will handle this and propose a solution.

πŸ’‘ Hint: Consider how the mean is affected by extreme values.

Question 2

You’re tasked with clustering customer data. Describe how you would determine the optimal K and why it's important.

πŸ’‘ Hint: Think about accuracy in your clustering results.

Challenge and get performance evaluation