Practice - Lab: Exploring SVMs with Different Kernels and Constructing Decision Trees, Analyzing Their Decision Boundaries
Practice Questions
Test your understanding with targeted questions
What is the main goal of a Support Vector Machine?
💡 Hint: Think about what is being separated in the classification context.
What does the term 'hyperplane' refer to?
💡 Hint: Consider dimensions when visualizing it.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the margin in SVMs represent?
💡 Hint: It's about the closest points to the boundary.
True or False: Decision Trees are highly interpretable models.
💡 Hint: Think about how easy it is to follow the path of decisions.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You are tasked with classifying a highly non-linear dataset. Which SVM kernel would you select, and why? Discuss the decision-making process.
💡 Hint: Think about the shape of the data!
Consider a Decision Tree classifier that shows a high training accuracy but low test accuracy. What steps could you take to improve its performance?
💡 Hint: Consider how to simplify the tree!
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Reference links
Supplementary resources to enhance your learning experience.