Quiz Topic - Classification

1. Which of the following metrics are used to evaluate classification models?

view answer: D. All of the above
2. Which one is a classification algorithm?

view answer: A. Logistic regression
3. Classification is-

view answer: C. Supervised learning
4. You have a dataset of different flowers containing their petal lengths and color. Your model has to predict the type of flower for given petal lengths and color. This is a-

view answer: B. Classification task
5. A classifier-

view answer: C. Both A and B
6. Classification is appropriate when you-

view answer: B. Try to predict a class or discrete output
7. With the help of a confusion matrix, we can compute-

view answer: D. All of the above
8. What does recall refer to in classification?

view answer: A. The proportion of all the relevant data points
9. False negatives are-

view answer: A. Predicted negatives that are actually positives
10. Suppose your classification model predicted true for a class which actual value was false. Then this is a-

view answer: A. False positive
11. The false negative value is 5 and the true positive value is 20. What will be the value of recall-

view answer: C. 0.8
12. The true positive value is 10 and the false positive value is 15. Calculate the value of precision-

view answer: B. 0.4
13. If the precision is 0.6 and the recall value is 0.4. What will be the f measure?

view answer: A. 0.5
14. Which one is a different algorithm?

view answer: A. Logistic Regression
15. What is a support vector?

view answer: C. The distance between two boundary data points
16. Which of the following is a lazy learning algorithm?

view answer: B. KNN
17. Which of the following is not a lazy learning algorithm?

view answer: D. All of the above
18. What is the most widely used distance metric in KNN?

view answer: A. Euclidean distance
19. Which of the following is the best algorithm for text classification?

view answer: D. Naive Bayes
20. What does k stand for in the KNN algorithm?

view answer: A. Number of neighbors
21. Support Vector Machine is-

view answer: A. a discriminative classifier
22. What are hyperplanes?

view answer: A. Decision boundaries
23. What is a kernel?

view answer: B. A function that maps the value from one dimension to the other
24. Which of the following is not a kernel?

view answer: D. None
25. Why Naive Bayes is called naive?

view answer: A. Because its assumption may or may not true
26. For two events A and B, the Bayes theorem will be-

view answer: B. P(A | B) = P(A) * P(B | A) / P(B)
27. How does a decision tree work?

view answer: B. Maximizes the information gain and minimizes the entropy
28. Suppose you have a dataset that is randomly distributed. What will be the best algorithm for that dataset?

view answer: D. Decision tree
29. Which pair of the algorithms are similar in operation?

view answer: B. Decision tree and Random forest
30. Which metric is not used for evaluating classification models?

view answer: C. Mean absolute error

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