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Machine learning questions and answers
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1. In classification, what does high entropy mean?
2. Which clustering approach is the best to produce clusters of different sizes and shapes?
3. Which type of machine learning algorithms would be helpful to predict amount of rainfall in a region?
4. In linear regression,which regularization penalties can be used to reduce some parameters to zero?
5. Why the MLE estimates are often considered as undesirable?
6. If you have enormous amount of training data, what would be the variance of your model trained on that data?
7. In a neural network, if you have more neurons in the hidden layers, what would be the impact?
8. What will be the problem with the decision tree that is too shallow?
9. How do we refer to a machine learning model that neither model the training data nor generalize to new unseen data?
10. When can hard margin SVM work?
11. What is the objective of K-means clustering?
12. How does polynomial degree affect the overfitting and underfitting in polynomial regression?
13. When does bias become low or high in polynomial regression?
14. How does the use of weak classifiers help preventing overfitting when perform bagging?
15. How does “the averaging the output of multiple decision tree” help?
16. What are some assumptions made by k-means algorithm?
17. What is an activation function in neural networks?
18. How does the kernel width affect the trade-off between underfitting and overfitting in kernel regression?
19. Why do we need to prune a decision tree?
20. Discuss on bias and variance tradeoff.
21. What is the cause of increase in training loss with number of epochs?
22. Why does decision tree algorithm can achieve zero training error on any linearly separable dataset?
23. Why does perceptron can achieve zero training error on any linearly separable dataset?
24. List down different ways to reduce the overfitting problem.
25. Which is the most suitable error function for gradient descent using logistic regression?
26. When do we use linear kernel while training an SVM?
27. What is the impact of increasing the size of layers in a neural network on bias and variance?
28. How does the increase in the number of hidden units per layer in a neural network affect bias and variance?
29. How does pruning help in the development of a decision tree?
30. What is the major weakness of decision trees when compared with logistic regression classifiers?
31. Why decision tree is not an ensemble machine learning method?
32. What is sequential ensemble?
33. Differentiate between sequential and parallel ensemble models.
34. What will be the effect of k-nearest neighbor model on bias and variance if we increase the value of k?
35. What would be the bias-variance tradeoff if we increase the value of k in k-nearest neighbor?
36. Will k-nearest neighbor model underfit or overfit for a large value of k?
37. Differentiate between underfit and overfit in machine learning.
38. What is the impact of multi-way split in decision tree learning?
39. Why do both the perceptron and the linear SVM have same VC dimension?
40. Does the selection of decision tree split that minimizes the classification error guarantee an optimal tree?
41. What is called the“curse of dimensionality”?
42. Models that are too complex tend to have high variance and low bias. Why?
43. What are some of the popular discriminative models to solve classification problems?
45. How does Hamming distance is different from other distance measures Euclidean and Manhattan?
46. Why Leave-One-Out cross validation is not suitable for very large datasets?
47. What is cross validation used for?
48. Which cross validation technique is working fast on very large datasets?
50. What are some applications of unsupervised learning?
51.
Which method is used to find the optimal clusters in k-means clustering algorithm?
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