Practice Distance Metrics - 3.4.2 | 3. Kernel & Non-Parametric Methods | Advance Machine Learning
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is Euclidean distance in your own words?

πŸ’‘ Hint: Think about the straight-line distance you would measure with a ruler.

Question 2

Easy

Calculate the Manhattan distance between points (2, 3) and (5, 7).

πŸ’‘ Hint: Remember to sum the absolute differences for each dimension.

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 does Euclidean distance measure?

  • Squared differences
  • Straight-line distance
  • Taxicab distance

πŸ’‘ Hint: Think about the most direct route between two points.

Question 2

True or False: Manhattan distance and Euclidean distance are always equivalent.

  • True
  • False

πŸ’‘ Hint: Consider how each calculation handles coordinate differences.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have points A(1, 2), B(4, 6), and C(3, 3) on a 2D grid. Calculate the distances between these points using both Euclidean and Manhattan metrics and analyze which metric is more suitable under which conditions.

πŸ’‘ Hint: Use the distance formulas for calculations!

Question 2

Discuss the implications of using Minkowski distance with p=3 compared to p=1 and p=2 in terms of neighbor selection for k-NN, especially in high-dimensional data.

πŸ’‘ Hint: Consider how distance affects clustering in high dimensions.

Challenge and get performance evaluation