Practice Distance Metrics (Measuring 'Closeness') - 5.4.2 | Module 3: Supervised Learning - Classification Fundamentals (Weeks 5) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is Euclidean distance used for?

πŸ’‘ Hint: Think of a ruler!

Question 2

Easy

What movement does Manhattan distance consider?

πŸ’‘ Hint: Think about navigating a grid layout.

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 type of movement does Manhattan distance consider?

  • Diagonal Movement
  • Horizontal and Vertical Movement
  • Only Vertical Movement

πŸ’‘ 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.

  • True
  • False

πŸ’‘ Hint: Consider how one scale can overwhelm another.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a dataset with features where Euclidean distance would perform better than Manhattan distance. Explain your reasoning.

πŸ’‘ 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.

πŸ’‘ Hint: Start with understanding the range of each feature.

Challenge and get performance evaluation