Welcome to your Uncovering the Top Machine Learning Quizzes 2024 (Part 3) This quiz has multiple choice questions and each has only one correct answer.
Total Questions : 25
To pass this quiz you need to score 80% or above.
Time Provided : 45 minutes
Today's Date : 1 October 2026
All the best!!
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1. How does the k-NN algorithm classify a new data point?
2. Which of the following scenarios is Naive Bayes particularly well-suited for?
3. In text classification using Naive Bayes, what is often used to represent the text data?
4. What is a major drawback of the k-NN algorithm when applied to large datasets?
5. What is the main advantage of using weighted k-NN?
6. What is the fundamental assumption of the Naive Bayes classifier?
7. In k-NN, what is the purpose of normalizing the feature values?
8. In the context of Naive Bayes, what does the term "naive" refer to?
9. Which of the following is an advantage of Naive Bayes?
10. Naive Bayes is particularly well-suited for which of the following tasks?
11. Which type of learning algorithm is Naive Bayes?
12. Which of the following is a common technique for handling missing values in feature engineering? (Most common one, even though others are used)?
13. Why is scaling numerical features important in feature engineering?
14. Which distance metric is commonly used in k-NN for continuous variables?
15. What is binning used for in feature engineering?
16. What is one-hot encoding used for in feature engineering?
17. In Naive Bayes, how is the posterior probability calculated?
18. What is feature selection in feature engineering?
19. What happens if you choose a very large value of k in k-NN?
20. Which of the following is a common variant of the Naive Bayes algorithm for binary or categorical features?
21. Which of the following distributions is commonly used in Naive Bayes for continuous features?
22. What type of learning algorithm is k-NN?
23. What is the primary goal of feature engineering in machine learning?
24. Which of the following is a disadvantage of k-NN?
25. Which of the following scenarios is k-NN particularly well-suited for?
