Practice K-Nearest Neighbors (KNN) - 2.3 | Classification Algorithms | Data Science Basic
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does KNN stand for?

💡 Hint: Recall the main term used in the algorithm.

Question 2

Easy

What is meant by majority voting in KNN?

💡 Hint: Think about how votes are counted.

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 the K in KNN represent?

  • Number of classes
  • Number of neighbors
  • Total data points

💡 Hint: Think about what the algorithm needs to decide the classes.

Question 2

True or False: KNN assumes a normal distribution of data.

  • True
  • False

💡 Hint: Consider the flexibility of KNN in working with data.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are given a dataset with features that are on different scales. How would you ensure KNN performs effectively on this data?

💡 Hint: Consider how distance is measured in KNN.

Question 2

Define a scenario where KNN might not perform well, and explain why.

💡 Hint: Think about how distance behaves in more than three dimensions.

Challenge and get performance evaluation