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

2.3 - K-Nearest Neighbors (KNN)

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Learning

Practice Questions

Test your understanding with targeted questions

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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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

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

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.