Classification - Ai Quiz Questions

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

A. Area under the ROC curve

B. F1 score

C. Confusion matrix

D. All of the above

2. Which one is a classification algorithm?

A. Logistic regression

B. Linear regression

C. Polynomial regression

D. None

3. Classification is-

A. Unsupervised learning

B. Reinforcement learning

C. Supervised learning

D. None

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-

A. Regression task

B. Classification task

C. Clustering task

D. None

5. A classifier-

A. Inputs a vector of continuous values and outputs a single discrete value

B. Inputs a vector of discrete values and outputs a single discrete value

C. Both A and B

D. None

6. Classification is appropriate when you-

A. Try to predict a continuous valued output

B. Try to predict a class or discrete output

C. Both A and B for different contexts

D. None

7. With the help of a confusion matrix, we can compute-

A. Recall

B. Precision

C. Accuracy

D. All of the above

8. What does recall refer to in classification?

A. The proportion of all the relevant data points

B. The proportion of only the correct data points

C. The proportion of all data points

D. None

9. False negatives are-

A. Predicted negatives that are actually positives

B. Predicted positives that are actually negatives

C. Predicted negatives that are actually negatives

D. Predicted positives that are actually positives

10. Suppose your classification model predicted true for a class which actual value was false. Then this is a-

A. False positive

B. False negative

C. True positive

D. True negative

11. The false negative value is 5 and the true positive value is 20. What will be the value of recall-

A. 0.2

B. 0.6

C. 0.8

D. 0.3

12. The true positive value is 10 and the false positive value is 15. Calculate the value of precision-

A. 0.6

B. 0.4

C. 0.5

D. None

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

A. 0.5

B. 0.6

C. 0.4

D. 0.3

14. Which one is a different algorithm?

A. Logistic Regression

B. Support Vector Regression

C. Polynomial Regression

D. None

15. What is a support vector?

A. The distance between any two data points

B. The average distance between all the data points

C. The distance between two boundary data points

D. The minimum distance between any two data points

16. Which of the following is a lazy learning algorithm?

A. SVM

B. KNN

C. Decision tree

D. All of the above

17. Which of the following is not a lazy learning algorithm?

A. SVM

B. Decision tree

C. Random forest

D. All of the above

18. What is the most widely used distance metric in KNN?

A. Euclidean distance

B. Manhattan distance

C. Perpendicular distance

D. All of the above

19. Which of the following is the best algorithm for text classification?

A. KNN

B. Decision tree

C. Random forest

D. Naive Bayes

20. What does k stand for in the KNN algorithm?

A. Number of neighbors

B. Number of output classes

C. Number of input features

D. None

21. Support Vector Machine is-

A. a discriminative classifier

B. a lazy learning classifier

C. a probabilistic classifier

D. None

22. What are hyperplanes?

A. Decision boundaries

B. Decision functions

C. Mapping functions

D. None

23. What is a kernel?

A. A function that calculates the distance of two boundary data points

B. A function that maps the value from one dimension to the other

C. A function that predicts the output value of a regression

D. None

24. Which of the following is not a kernel?

A. Polynomial Kernel

B. Gaussian Kernel

C. Sigmoid Kernel

D. None

25. Why Naive Bayes is called naive?

A. Because its assumption may or may not true

B. Because it’s a bad classifier

C. The accuracy is very poor

D. All of the above

26. For two events A and B, the Bayes theorem will be-

A. P(A | B) = P(B) * P(B | A) / P(A)

B. P(A | B) = P(A) * P(B | A) / P(B)

C. P(A | B) = P(B) * P(A | B) / P(A)

D. P(A | B) = P(A) * P(A | B) / P(B)

27. How does a decision tree work?

A. Minimizes the information gain and maximizes the entropy

B. Maximizes the information gain and minimizes the entropy

C. Minimizes the information gain and minimizes the entropy

D. Maximizes the information gain and maximizes the entropy

28. Suppose you have a dataset that is randomly distributed. What will be the best algorithm for that dataset?

A. Support vector machine

B. Naive Bayes

C. K nearest neighbors

D. Decision tree

29. Which pair of the algorithms are similar in operation?

A. SVM and KNN

B. Decision tree and Random forest

C. SVM and Naive Bayes

D. All of the above

30. Which metric is not used for evaluating classification models?

A. AUC ROC score

B. Accuracy

C. Mean absolute error

D. Precision

Quiz Category

Machine Learning

Artificial Intelligence

Quiz Topic - Classification