Machine Learning MCQ Multiple Choice Questions Answers for Practice
1. A measure of goodness of fit for the estimated regression equation is the
Correct Answer is: mean square due to regression
2. A nearest neighbor approach is best used
Correct Answer is: when irrelevant attributes have been removed from the data.
3. A regression model in which more than one independent variable is used to predict the dependent variable is called
Correct Answer is: an independent model
4. A term used to describe the case when the independent variables in a multiple regression model are correlated is
Correct Answer is: multicollinearity
5. Adding more basis functions in a linear model... (pick the most probably option)
Correct Answer is: Decreases model bias
6. Another name for an output attribute.
Correct Answer is: independent variable
7. Bootstrapping allows us to
Correct Answer is: choose the same training instance several times.
8. 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.
Correct Answer is: (A), (C), (D)
9. Classification problems are distinguished from estimation problems in that
Correct Answer is: classification problems do not allow an output attribute.
10. Computational complexity of Gradient descent is
Correct Answer is: polynomial in D
11. Computers are best at learning
Correct Answer is: facts.
12. 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?
Correct Answer is: When my model is logistic regression and Gaussian discriminant analysis.
13. Data used to build a data mining model.
Correct Answer is: training data
14. Data used to optimize the parameter settings of a supervised learner model.
Correct Answer is: validation
15. 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?
Correct Answer is: No
16. Grid search is
Correct Answer is: Exponential in D and Linear in N
17. K-fold cross-validation is
Correct Answer is: linear in K
18. 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?
Correct Answer is: O(D)
19. Logistic regression is a ........... regression technique that is used to model data having a ........... outcome.
Correct Answer is: nonlinear, binary
20. Machine learning techniques differ from statistical techniques in that machine learning methods
Correct Answer is: are better able to deal with missing and noisy data.
21. Regarding bias and variance, which of the follwing statements are true? (Here high and low are relative to the ideal model)
Correct Answer is: Models which overfit have a low bias and underfit have a low variance.
22. Regression trees are often used to model ........... data.
Correct Answer is: nonlinear
23. Selecting data so as to assure that each class is properly represented in both the training and test set.
Correct Answer is: stratification
24. Simple regression assumes a ........... relationship between the input attribute and output attribute.
Correct Answer is: linear
25. Supervised learning and unsupervised clustering both require at least one
Correct Answer is: hidden attribute.
26. Supervised learning differs from unsupervised clustering in that supervised learning requires
Correct Answer is: input attributes to be categorical.
27. Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?
Correct Answer is: Improve the optimisation algorithm being used for error minimisation.
28. The adjusted multiple coefficient of determination accounts for
Correct Answer is: none of the above
29. The average positive difference between computed and desired outcome values.
Correct Answer is: mean positive error
30. The average squared difference between classifier predicted output and actual output.
MCQ quiz on Machine Learning multiple choice questions and answers on Machine Learning MCQ questions on Machine Learning objectives questions with answer test pdf for interview preparations, freshers jobs and competitive exams.
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Multiple Choice Questions and Answers on Machine Learning
Machine Learning Multiple Choice Questions and Answers
