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Data mining multiple choice questions answers

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DATA MINING Multiple Choice Questions :-
1. The problem of finding hidden structure in unlabeled
data is called
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
Ans: B
2. Task of inferring a model from labeled training data is
called
A. Unsupervised learning
B. Supervised learning
C. Reinforcement learning
Ans: B
3. Some telecommunication company wants to segment
their customers into distinct groups in order to send
appropriate subscription offers, this is an example of
A. Supervised learning
B. Data extraction
C. Serration
D. Unsupervised learning
Ans: D

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4. Self-organizing maps are an example of
A. Unsupervised learning
B. Supervised learning
C. Reinforcement learning
D. Missing data imputation
Ans: A
5. You are given data about seismic activity in Japan, and
you want to predict a magnitude of the next earthquake,
this is in an example of
A. Supervised learning
B. Unsupervised learning
C. Serration
D. Dimensionality reduction
Ans: A
6. Assume you want to perform supervised learning and
to predict number of newborns according to size of
storks’ population
(http://www.brixtonhealth.com/storksBabies.pdf), it is
an example of
A. Classification
B. Regression
C. Clustering
D. Structural equation modeling
Ans: B
7. Discriminating between spam and ham e-mails is a
classification task, true or false?
A. True
B. False
Ans: A
8. In the example of predicting number of babies based
on storks’ population size, number of babies is
A. outcome
B. feature
C. attribute
D. observation
Ans: A

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DATA MINING Multiple Choice Questions :- 1. The problem of finding hidden structure in unlabeled data is called A. Supervised learning B. Unsupervised learning C. Reinforcement learning Ans: B 2. Task of inferring a model from labeled training data is called A. Unsupervised learning B. Supervised learning C. Reinforcement learning Ans: B 3. Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of A. Supervised learning B. Data extraction C. Serration D. Unsupervised learning Ans: D 4. Self-organizing maps are an example of A. Unsupervised learning B. Supervised learning C. Reinforcement learning D. Missing data imputation Ans: A 5. You are given data about seismic activity in Japan, and you want to predict a magnitude of the next earthquake, this is in an example of A. Supervised learning B. Unsupervised learning C. Serration D. Dimensionality reduction Ans: A 6. Assume you want to perform supervised learning and to predict number of newborns according to size of storks’ population (http://www.brixtonhealth.com/storksBabies.pdf), it is an example of A. Classification B. Regression C. Clustering D. Structural equation modeling Ans: B 7. Discriminating between spam and ham e-mails is a classification task, true or false? A. True B. False Ans: A 8. In the example of predicting number of babies based on storks’ population size, number of babies is A. outcome B. feature C. at ...
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