DSCI 4330 (1) | UPDATED Questions with 100% Verified
Answers
Q1: In the Dell cases study, the largest issue was how to properly
spend the online marketing budget.
A: False
Q2: A data mining study is specific to addressing a well-defined
business task, and different business tasks require
A: different sets of data.
Q3: When a problem has many attributes that impact the
classification of different patterns, decision trees may be a
useful approach.
A: True
Q4: Market basket analysis is a useful and entertaining way to
explain data mining to a technologically less savvy audience,
but it has little business significance.
A: False
Q5: K-fold cross-validation is also called sliding estimation.
A: False
Q6: Data mining can be very useful in detecting patterns such as
credit card fraud, but is of little help in improving sales.
A: False
Q7: Clustering partitions a collection of things into segments
whose members share
A: similar characteristics.
Q8: All of the following statements about data mining are true
EXCEPT
A: The ideas behind it are relatively new.
Q9: What does the robustness of a data mining method refer to?
A: its ability to overcome noisy data to make somewhat accurate predictions
Q10: During classification in data mining, a false positive is an
occurrence classified as true by the algorithm while being
, false in reality.
A: True
Q11: Which of the following is a data mining myth?
A: Data mining requires a separate, dedicated database.
Q12: Prediction problems where the variables have numeric values
are most accurately defined as
A: regressions
Q13: Understanding customers better has helped Amazon and
others become more successful. The understanding comes
primarily from
A: analyzing the vast data amounts routinely collected.
Q14: In the Influence Health case study, what was the goal of the
system?
A: increasing service use
Q15: Which broad area of data mining applications partitions a
collection of objects into natural groupings with similar
features?
A: clustering
Q16: What does the scalability of a data mining method refer to?
A: its ability to construct a prediction model efficiently given a large amount of data
Q17: In data mining, classification models help in prediction.
A: True
Q18: The cost of data storage has plummeted recently, making data
mining feasible for more firms.
A: True
Q19: In the opening case, police detectives used data mining to
identify possible new areas of inquiry.
A: False
Q20: All of the following are challenges associated with natural
language processing EXCEPT
Answers
Q1: In the Dell cases study, the largest issue was how to properly
spend the online marketing budget.
A: False
Q2: A data mining study is specific to addressing a well-defined
business task, and different business tasks require
A: different sets of data.
Q3: When a problem has many attributes that impact the
classification of different patterns, decision trees may be a
useful approach.
A: True
Q4: Market basket analysis is a useful and entertaining way to
explain data mining to a technologically less savvy audience,
but it has little business significance.
A: False
Q5: K-fold cross-validation is also called sliding estimation.
A: False
Q6: Data mining can be very useful in detecting patterns such as
credit card fraud, but is of little help in improving sales.
A: False
Q7: Clustering partitions a collection of things into segments
whose members share
A: similar characteristics.
Q8: All of the following statements about data mining are true
EXCEPT
A: The ideas behind it are relatively new.
Q9: What does the robustness of a data mining method refer to?
A: its ability to overcome noisy data to make somewhat accurate predictions
Q10: During classification in data mining, a false positive is an
occurrence classified as true by the algorithm while being
, false in reality.
A: True
Q11: Which of the following is a data mining myth?
A: Data mining requires a separate, dedicated database.
Q12: Prediction problems where the variables have numeric values
are most accurately defined as
A: regressions
Q13: Understanding customers better has helped Amazon and
others become more successful. The understanding comes
primarily from
A: analyzing the vast data amounts routinely collected.
Q14: In the Influence Health case study, what was the goal of the
system?
A: increasing service use
Q15: Which broad area of data mining applications partitions a
collection of objects into natural groupings with similar
features?
A: clustering
Q16: What does the scalability of a data mining method refer to?
A: its ability to construct a prediction model efficiently given a large amount of data
Q17: In data mining, classification models help in prediction.
A: True
Q18: The cost of data storage has plummeted recently, making data
mining feasible for more firms.
A: True
Q19: In the opening case, police detectives used data mining to
identify possible new areas of inquiry.
A: False
Q20: All of the following are challenges associated with natural
language processing EXCEPT
