Machine Learning MCQs Quiz Multiple Choice Questions & Answers

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Machine Learning Questions with Answers

1. Regression trees are often used to model ........... data.

2. Selecting data so as to assure that each class is properly represented in both the training and test set.

3. Simple regression assumes a ........... relationship between the input attribute and output attribute.

4. Supervised learning and unsupervised clustering both require at least one

5. Supervised learning differs from unsupervised clustering in that supervised learning requires

6. Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?

7. The adjusted multiple coefficient of determination accounts for

8. The average positive difference between computed and desired outcome values.

9. The average squared difference between classifier predicted output and actual output.

10. The correlation between the number of years an employee has worked for a company and the salary of the employee is 0.75. What can be said about employee salary and years worked?

11. The correlation coefficient for two real-valued attributes is 0.85. What does this value tell you?

12. The leaf nodes of a model tree are

13. The multiple coefficient of determination is computed by

14. The process of forming general concept definitions from examples of concepts to be learned.

15. The standard error is defined as the square root of this computation.

16. This clustering algorithm initially assumes that each data instance represents a single cluster.

17. This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

18. This supervised learning technique can process both numeric and categorical input attributes.

19. This technique associates a conditional probability value with each data instance.

20. This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

21. When doing least-squares regression with regularisation (assuming that the optimisation can be done exactly), increasing the value of the regularisation parameter (Lambda)

22. Which is not true about Gradient of a continuous and differentiable function

23. Which of the following is a common use of unsupervised clustering?

24. Which of the following is not an advantage of Grid search

25. Which of the following points would Bayesians and frequentists disagree on?

26. Which of the following sentence is FALSE regarding regression?

27. Which statement about outliers is true?

28. Which statement is true about neural network and linear regression models?

29. Which statement is true about prediction problems?

30. With Bayes classifier, missing data items are

31. You observe the following while fitting a linear regression to the data: As you increase the amount of training data, the test error decreases and the training error increases. The train error is quite low (almost what you expect it to), while the test error is much higher than the train error. What do you think is the main reason behind this behavior. Choose the most probable option.

32. A measure of goodness of fit for the estimated regression equation is the

33. A nearest neighbor approach is best used

34. A regression model in which more than one independent variable is used to predict the dependent variable is called

35. A term used to describe the case when the independent variables in a multiple regression model are correlated is

36. Adding more basis functions in a linear model... (pick the most probably option)

37. Another name for an output attribute.

38. Bootstrapping allows us to

39. Choose the options that are correct regarding machine learning (ML) and artificial intelligence (AI),(A) ML is an alternate way of programming intelligent machines.(B) ML and AI have very different goals.(C) ML is a set of techniques that turns a dataset into a software.(D) AI is a software that can emulate the human mind.

40. Classification problems are distinguished from estimation problems in that

41. Computational complexity of Gradient descent is

42. Computers are best at learning

43. Consider a binary classification problem. Suppose I have trained a model on a linearly separable training set, and now I get a new labeled data point which is correctly classified by the model, and far away from the decision boundary. If I now add this new point to my earlier training set and re-train, in which cases is the learnt decision boundary likely to change?

44. Data used to build a data mining model.

45. Data used to optimize the parameter settings of a supervised learner model.

46. Generalization error measures how well an algorithm perform on unseen data. The test error obtained using cross-validation is an estimate of the generalization error. Is this estimate unbiased?

47. Grid search is

48. K-fold cross-validation is

49. Let us say that we have computed the gradient of our cost function and stored it in a vector g. What is the cost of one gradient descent update given the gradient?

50. Logistic regression is a ........... regression technique that is used to model data having a ........... outcome.


Multiple Choice Questions and Answers on Machine Learning

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