Practice Lab Objectives - 6.1 | Module 3: Supervised Learning - Classification Fundamentals (Weeks 6) | Machine Learning
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6.1 - Lab Objectives

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a hyperplane in SVMs?

πŸ’‘ Hint: Think about how data is divided in multidimensional space.

Question 2

Easy

Define the term Support Vector.

πŸ’‘ Hint: Consider the data points that matter most for the decision.

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 is the purpose of Support Vectors in SVMs?

  • They determine the position of the hyperplane
  • They increase model complexity
  • They reduce the size of the data

πŸ’‘ Hint: Think about which data points have the most impact on classification.

Question 2

True or False: Hard margin SVMs allow for misclassifications.

  • True
  • False

πŸ’‘ Hint: Consider what 'hard' means in context.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset with noisy labels. Explain how you would choose between a hard margin SVM and a soft margin SVM and justify your decision.

πŸ’‘ Hint: Consider the implications of noise on classification accuracy.

Question 2

Analyze the trade-offs between using a deeper Decision Tree versus a pruned one, providing scenarios where each might be preferable.

πŸ’‘ Hint: Reflect on the benefits of model simplicity vs. complexity.

Challenge and get performance evaluation