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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a hyperplane in SVMs?
π‘ Hint: Think about how data is divided in multidimensional space.
Question 2
Easy
Define the term Support Vector.
π‘ Hint: Consider the data points that matter most for the decision.
Practice 4 more questions and get performance evaluation
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?
π‘ Hint: Think about which data points have the most impact on classification.
Question 2
True or False: Hard margin SVMs allow for misclassifications.
π‘ Hint: Consider what 'hard' means in context.
Solve 1 more question and get performance evaluation
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.
π‘ 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.
π‘ Hint: Reflect on the benefits of model simplicity vs. complexity.
Challenge and get performance evaluation