Machine Learning (ML) MCQ Questions with Answer 2022 – In Machine Learning Another important point to be noted is that every machine learning technique is classified as AI ones. However, not all AI could count as machine learning. Human knowledge is barely obtained by the experience throughout their life. For machines that knowledge is required to be fed by collecting enormous amounts of information on a specific application and fed thereto, machines also obtain in an exceedingly short period of your time.
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
Answer: A. data mining.
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
Answer: B. predictive model
3. In what type of learning labelled training data is used
A. unsupervised learning
B. supervised learning
C. reinforcement learning
D. active learning
Answer: B. supervised 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
Answer: A. forward feature selection
5. In PCA the number of input dimensiona are equal to principal components
A. true
B. false
Answer: A. true
6. PCA can be used for projecting and visualizing data in lower dimensions.
A. true
B. false
Answer: A. true
7. Which of the following is the best machine learning method?
A. scalable
B. accuracy
C. fast
D. all of the above
Answer: 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
Answer: D. there is plenty of confusing knowledge
9. What does dimensionality reduction reduce?
A. stochastics
B. collinerity
C. performance
D. entropy
Answer: B. collinerity
10. Data used to build a data mining model.
A. training data
B. validation data
C. test data
D. hidden data
Answer: A. training data
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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
Answer: B. unsupervised learning
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
Answer: A. given email labeled as spam or not spam, learn a spam filter
13. Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model
A. true
B. false
Answer: A. true
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
Answer: A. supervised 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
Answer: C. both a and b
16. Following are the types of supervised learning
A. classification
B. regression
C. subgroup discovery
D. all of the above
Answer: D. all of the above
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17. Type of matrix decomposition model is
A. descriptive model
B. predictive model
C. logical model
D. none of the above
Answer: A. descriptive model
18. Following is powerful distance metrics used by Geometric model
A. euclidean distance
B. manhattan distance
C. both a and b??
D. square distance
Answer: C. both a and b??
19. The output of training process in machine learning is
A. machine learning model
B. machine learning algorithm
C. null
D. accuracy
Answer: A. machine learning model
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
Answer: B. ordinal
21. PCA is
A. forward feature selection
B. backword feature selection
C. feature extraction
D. all of the above
Answer: C. feature extraction
22. Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.
A. true
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
Answer: A. removing columns which have too many missing values
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
Answer: C. input 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
Answer: B. a plane with 2 dimensional fewer than number of input attributes
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