Practice Lab Objectives (6.1) - Supervised Learning - Classification Fundamentals (Weeks 6)
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Lab Objectives

Practice - Lab Objectives

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a hyperplane in SVMs?

💡 Hint: Think about how data is divided in multidimensional space.

Question 2 Easy

Define the term Support Vector.

💡 Hint: Consider the data points that matter most for the decision.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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

💡 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

💡 Hint: Consider what 'hard' means in context.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

💡 Hint: Consider the implications of noise on classification accuracy.

Challenge 2 Hard

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

💡 Hint: Reflect on the benefits of model simplicity vs. complexity.

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