Machine Learning MCQ : Test 11

Explore this diverse selection of multiple-choice questions (MCQs) designed for various examinations. Machine Learning MCQ : Test 11 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. How does dropout prevent overfitting in neural networks?

2. Which evaluation metric is used for regression problems?

3. What is the purpose of using a Gaussian mixture model (GMM)?

4. How does the BERT model improve NLP tasks?

5. Which method is used for dimensionality reduction in sparse data?

6. What is the role of an optimizer in neural networks?

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