Machine Learning MCQ : Test 3

Explore this diverse selection of multiple-choice questions (MCQs) designed for various examinations. Machine Learning MCQ : Test 3 focuses on essential aspects of the subject, ensuring comprehensive preparation across different categories and fields of study to enhance your knowledge and readiness. The right answers for each question is provided next to respective questions for your convenience, you can either attend the test or dirtectly access the right answers by clicking the show correct answer button.

Each correct answer earns 1 mark, while each incorrect answer deducts 0.3 marks.

1. Which evaluation metric is used for regression?

2. What is the purpose of k-fold cross-validation?

3. How does ensemble learning improve model performance?

4. Which technique is used for image classification?

5. What is the role of a loss function in machine learning?

6. Which algorithm is used for sentiment analysis?

7. What is the purpose of a learning curve?

8. How does a convolutional neural network (CNN) process images?

9. Which method is used for dimensionality reduction?

10. What is a confusion matrix?

11. Which algorithm is used for clustering?

12. How does reinforcement learning differ from supervised learning?

13. Which type of neural network is used for sequence data?

14. What is the role of a hyperparameter?

15. Which method is used to evaluate the clustering quality?

16. What is an epoch in machine learning?

17. How does batch normalization help neural networks?

18. Which algorithm is used for market basket analysis?

19. What is the purpose of dropout in neural networks?

20. Which method is used for text classification?

21. What is a perceptron?

22. How does a random forest improve prediction accuracy?

23. Which technique is used to handle categorical data?

24. What is a common evaluation metric for clustering?

25. Which algorithm is used for anomaly detection?

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