Machine Learning (ML) solved MCQs

1 of 31

1. Application of machine learning methods to large databases is called

a. data mining.

B. artificial intelligence

c. big data computing

d. internet of things

2. If machine learning model output involves target variable then that model is called as

a. descriptive model

B. predictive model

c. reinforcement learning

d. all of the above

3. In what type of learning labelled training data is used

a. unsupervised learning

B. supervised learning

c. reinforcement learning

d. active learning

4. In following type of feature selection method we start with empty feature set

a. forward feature selection

B. backword feature selection

c. both a and b??

d. none of the above

5. In PCA the number of input dimensiona are equal to principal components

a. true

B. false

6. PCA can be used for projecting and visualizing data in lower dimensions.

a. true

B. false

7. Which of the following is the best machine learning method?

a. scalable

B. accuracy

c. fast

d. all of the above

8. What characterize unlabeled examples in machine learning

a. there is no prior knowledge

B. there is no confusing knowledge

c. there is prior knowledge

d. there is plenty of confusing knowledge

9. What does dimensionality reduction reduce?

a. stochastics

B. collinerity

c. performance

d. entropy

10. Data used to build a data mining model.

a. training data

B. validation data

c. test data

d. hidden data

11. The problem of finding hidden structure in unlabeled data is called…

a. supervised learning

B. unsupervised learning

c. reinforcement learning

d. none of the above

12. Of the Following Examples, Which would you address using an supervised learning Algorithm?

a. given email labeled as spam or not spam, learn a spam filter

B. given a set of news articles found on the web, group them into set of articles about the same story.

c. given a database of customer data, automatically discover market segments and group customers into different market segments.

d. find the patterns in market basket analysis

13. Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model

a. true

B. false

14. You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of

a. supervised learning

B. unsupervised learning

c. semisupervised learning

d. reinforcement learning

15. Which of the following is a good test dataset characteristic?

a. large enough to yield meaningful results

B. is representative of the dataset as a whole

c. both a and b

d. none of the above

16. Following are the types of supervised learning

a. classification

B. regression

c. subgroup discovery

d. all of the above

17. Type of matrix decomposition model is

a. descriptive model

B. predictive model

c. logical model

d. none of the above

18. Following is powerful distance metrics used by Geometric model

a. euclidean distance

B. manhattan distance

c. both a and b??

d. square distance

19. The output of training process in machine learning is

a. machine learning model

B. machine learning algorithm

c. null

d. accuracy

20. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is

a. nominal

B. ordinal

c. categorical

d. boolean

21. PCA is

a. forward feature selection

B. backword feature selection

c. feature extraction

d. all of the above

22. Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.

a. true

B. false

23. Which of the following techniques would perform better for reducing dimensions of a data set?

a. removing columns which have too many missing values

B. removing columns which have high variance in data

c. removing columns with dissimilar data trends

d. none of these

24. Supervised learning and unsupervised clustering both require which is correct according to the statement.

a. output attribute.

B. hidden attribute.

c. input attribute.

d. categorical attribute

25. What characterize is hyperplance in geometrical model of machine learning?

a. a plane with 1 dimensional fewer than number of input attributes

B. a plane with 2 dimensional fewer than number of input attributes

c. a plane with 1 dimensional more than number of input attributes

d. a plane with 2 dimensional more than number of input attributes

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