Practice K-nearest Neighbors (k-nn) (3.4) - Kernel & Non-Parametric Methods
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k-Nearest Neighbors (k-NN)

Practice - k-Nearest Neighbors (k-NN)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does k-NN stand for?

💡 Hint: Think about the terminology used in classification tasks.

Question 2 Easy

Name one distance metric used in k-NN.

💡 Hint: Consider the different ways to measure distances.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does k in k-NN represent?

The number of neighbors
The number of data points
The model parameters

💡 Hint: It's related to the number of points used for voting.

Question 2

True or False: k-NN requires a training process.

True
False

💡 Hint: Think about how it operates when making predictions.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with 10 features and 1,000 instances, describe how you would preprocess the data before applying k-NN.

💡 Hint: Focus on the impact of varying feature scales and relevance.

Challenge 2 Hard

Consider a k-NN model deployed in a recommendation system. How would you ensure the 'k' parameter is optimally set?

💡 Hint: Remember, balancing underfitting and overfitting is key!

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Reference links

Supplementary resources to enhance your learning experience.