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 : 28 March 2026
All the best!!
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1. Which of the following is a common variant of the Naive Bayes algorithm for binary or categorical features?
2. Which distance metric is commonly used in k-NN for continuous variables?
3. What type of learning algorithm is k-NN?
4. Why is scaling numerical features important in feature engineering?
5. Which of the following scenarios is Naive Bayes particularly well-suited for?
6. In k-NN, what is the purpose of normalizing the feature values?
7. Which of the following is an advantage of Naive Bayes?
8. In the context of Naive Bayes, what does the term "naive" refer to?
9. What is binning used for in feature engineering?
10. What is the fundamental assumption of the Naive Bayes classifier?
11. Naive Bayes is particularly well-suited for which of the following tasks?
12. What is the primary goal of feature engineering in machine learning?
13. How does the k-NN algorithm classify a new data point?
14. Which of the following scenarios is k-NN particularly well-suited for?
15. In Naive Bayes, how is the posterior probability calculated?
16. What is one-hot encoding used for in feature engineering?
17. In text classification using Naive Bayes, what is often used to represent the text data?
18. Which of the following distributions is commonly used in Naive Bayes for continuous features?
19. Which type of learning algorithm is Naive Bayes?
20. Which of the following is a disadvantage of k-NN?
21. Which of the following is a common technique for handling missing values in feature engineering? (Most common one, even though others are used)?
22. What is feature selection in feature engineering?
23. What is the main advantage of using weighted k-NN?
24. What is a major drawback of the k-NN algorithm when applied to large datasets?
25. What happens if you choose a very large value of k in k-NN?
