21. K-fold cross-validation is

- linear in K
- quadratic in K
- cubic in K
- exponential in K

22. 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?

- O(D)
- O(N)
- O(ND)
- O(ND
^{2})

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

- linear, numeric
- linear, binary
- nonlinear, numeric
- nonlinear, binary

24. Machine learning techniques differ from statistical techniques in that machine learning methods

- typically assume an underlying distribution for the data.
- are better able to deal with missing and noisy data.
- are not able to explain their behavior.
- have trouble with large-sized datasets

25. Regarding bias and variance, which of the follwing statements are true? (Here high and low are relative to the ideal model)

- Models which overfit have a high bias and underfit have a high variance.
- Models which overfit have a high bias and underfit have a low variance.
- Models which overfit have a low bias and underfit have a high variance.
- Models which overfit have a low bias and underfit have a low variance.

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

- linear
- nonlinear
- categorical
- symmetrical

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

- cross validation
- stratification
- verification
- bootstrapping

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

- linear
- quadratic
- reciprocal
- inverse

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

- hidden attribute.
- output attribute.
- input attribute.
- categorical attribute.

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

- Increase the amount of training data.
- Improve the optimisation algorithm being used for error minimisation.
- Decrease the model complexity.
- Reduce the noise in the training data.

MCQ Multiple Choice Questions and Answers on Machine Learning

Machine Learning Trivia Questions and Answers PDF

Machine Learning Question and Answer

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