Machine Learning MCQ Test: Machine Learning MCQs - Practice Questions
1. Explain the concept of 'precision' in the context of classification metrics.
2. What is the purpose of the 'random forest' ensemble learning algorithm?
3. What is the purpose of 'adversarial training' in machine learning models?
4. What is 'hyperparameter tuning' in the context of Machine Learning models?
5. What is the role of 'Federated Learning' in privacy-preserving machine learning?
6. In Machine Learning, what is the role of a 'feature'?
7. What is the purpose of 'one-hot encoding' in representing categorical variables?
8. What role does 'attention mechanism' play in neural network architectures, especially in natural language processing (NLP)?
9. Which Machine Learning task involves grouping similar instances together?
10. What is the purpose of the 'softmax' activation function in neural networks?
11. In the context of machine learning models, what is 'interpretability' and why is it important?
12. In reinforcement learning, what is the significance of 'exploration' and 'exploitation'?
13. Which type of Machine Learning algorithm is used for classification tasks?
14. What is the difference between bagging and boosting in ensemble learning?
15. In unsupervised learning, what is 'anomaly detection' and how is it implemented?
16. Which library is widely used for implementing Machine Learning in Python?
17. Explain the purpose of the 'confusion matrix' in evaluating classification models.
18. What is 'self-supervised learning' and how does it differ from supervised learning?
19. Explain the significance of 'capsule networks' in neural network architectures.
20. What is the role of 'attention heads' in transformer-based neural network architectures?
21. Explain the concept of 'imbalanced data' and its impact on machine learning models. How can it be addressed?
22. What is the primary goal of 'data augmentation' in image classification tasks?
23. In reinforcement learning, what is the role of an 'agent'?
24. What does the acronym 'AI' stand for?
25. Explain the concept of 'meta-learning' and its applications in machine learning.
26. What is the primary goal of 'model distillation' in machine learning?
27. What does 'ensemble learning' involve in Machine Learning?
28. In clustering, what does the 'silhouette score' measure?
29. Explain the concept of 'Gini impurity' in decision tree algorithms.
30. How does 'PCA' contribute to dimensionality reduction in Machine Learning?
31. What is the purpose of cross-validation in machine learning?
32. How does 'LSTM' differ from traditional recurrent neural networks (RNNs)?
33. What is the purpose of the activation function in a neural network?
34. Explain the concept of 'Monte Carlo methods' in the context of reinforcement learning.
35. In the context of reinforcement learning, what is 'policy gradient'?
36. How does 'dropout' differ from 'batch normalization' in neural networks?
37. What is the significance of the 'learning rate' parameter in gradient descent optimization?
38. In ensemble learning, what is the purpose of 'voting classifiers'?
39. Which algorithm is commonly used for clustering in Machine Learning?
40. Which type of Machine Learning model is used for predicting a continuous outcome?