Machine Learning MCQ Test: Machine Learning MCQs - Practice Questions
1. What is the purpose of the 'Bagging' technique in ensemble learning?
2. What is the purpose of the 'Adam' optimization algorithm in gradient-based optimization?
3. Explain the concept of 'imbalanced data' and its impact on machine learning models. How can it be addressed?
4. Which method is used to handle missing data in a dataset?
5. What is 'hyperparameter tuning' in the context of Machine Learning models?
6. What is the primary advantage of using dropout layers in neural networks?
7. Explain the concept of 'cross-entropy loss' in training neural networks.
8. Which type of Machine Learning model is used for predicting a continuous outcome?
9. What is 'meta-reinforcement learning' and how does it differ from traditional reinforcement learning?
10. Explain the purpose of the 'confusion matrix' in evaluating classification models.
11. What is the purpose of the activation function in a neural network?
12. Explain the concept of 'residual networks' (ResNets) in deep learning architectures.
13. What is the purpose of 'one-hot encoding' in representing categorical variables?
14. What is the purpose of cross-validation in machine learning?
15. Explain the concept of 'transferability' in the context of transfer learning.
16. Explain the concept of 'Gini impurity' in decision tree algorithms.
17. What is the role of 'early stopping' in training Machine Learning models?
18. What does 'Supervised Learning' require during the training phase?
19. What is 'self-supervised learning' and how does it differ from supervised learning?
20. What is the role of 'Federated Learning' in privacy-preserving machine learning?
21. In the context of reinforcement learning, what is 'policy gradient'?
22. Explain the concept of 'adversarial attacks' in the context of machine learning models.
23. Explain the concept of 'meta-learning' and its applications in machine learning.
24. Which library is widely used for implementing Machine Learning in Python?
25. What is the significance of the 'learning rate' parameter in gradient descent optimization?
26. Which type of Machine Learning algorithm is inspired by the human brain's structure?
27. What are the key challenges in training deep neural networks, and how can they be mitigated?
28. Explain the significance of 'capsule networks' in neural network architectures.
29. Which metric is commonly used to evaluate classification models?
30. What is the significance of 'capsule routing' in capsule networks?