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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of a hyperplane in SVM?
💡 Hint: Think of how it divides space.
Question 2
Easy
What does the term 'support vectors' refer to?
💡 Hint: They help define the margin.
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 main goal of SVM?
- To minimize margin
- To maximize margin
- To eliminate all outliers
💡 Hint: Think about how boundaries should be drawn.
Question 2
True or False: The kernel trick allows SVM to operate in a higher-dimensional space without explicitly calculating it.
- True
- False
💡 Hint: Consider how data can be processed.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Suppose you have an imbalanced dataset where one class is significantly larger than the other. How might you adjust the SVM to account for this imbalance?
💡 Hint: Consider how SVM prioritizes its decisions.
Question 2
When should you choose a polynomial kernel over an RBF kernel, and why in practical terms might this choice matter?
💡 Hint: Think about data complexity and computational cost.
Challenge and get performance evaluation