Machine Learning MCQ Test: Machine Learning MCQs - Practice Questions
1. Which algorithm is commonly used for clustering in Machine Learning?
2. Explain the concept of 'meta-learning' and its applications in machine learning.
3. Explain the concept of 'Gini impurity' in decision tree algorithms.
4. In unsupervised learning, what is 'anomaly detection' and how is it implemented?
5. Explain the concept of 'dropout regularization' in training neural networks.
6. In Machine Learning, what is the role of a 'feature'?
7. In reinforcement learning, what is the role of an 'agent'?
8. What is the purpose of 'one-hot encoding' in representing categorical variables?
9. Explain the concept of 'precision' in the context of classification metrics.
10. In supervised learning, what is the role of the target variable?
11. Explain the concept of 'cross-entropy loss' in training neural networks.
12. What is the role of 'Kullback-Leibler divergence' in probabilistic models?
13. Which Machine Learning task involves grouping similar instances together?
14. Explain the significance of 'capsule networks' in neural network architectures.
15. What is the role of 'Federated Learning' in privacy-preserving machine learning?
16. Explain the concept of 'adversarial attacks' in the context of machine learning models.
17. What is the primary goal of 'distributional reinforcement learning'?
18. Explain the concept of 'Monte Carlo methods' in the context of reinforcement learning.
19. Explain the purpose of the 'confusion matrix' in evaluating classification models.
20. What does 'ensemble learning' involve in Machine Learning?