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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is Euclidean distance used for?
π‘ Hint: Think of a ruler!
Question 2
Easy
What movement does Manhattan distance consider?
π‘ Hint: Think about navigating a grid layout.
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 type of movement does Manhattan distance consider?
π‘ Hint: Think about moving in a city layout.
Question 2
True or False: Euclidean distance could give misleading results if feature scaling is not applied.
π‘ Hint: Consider how one scale can overwhelm another.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
Design a dataset with features where Euclidean distance would perform better than Manhattan distance. Explain your reasoning.
π‘ Hint: Consider the shapes of your data points and their arrangement.
Question 2
Given a multi-feature dataset, how might you apply feature scaling before using KNN? Discuss your steps.
π‘ Hint: Start with understanding the range of each feature.
Challenge and get performance evaluation